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Dr. Miles Weaver, PhD thesis entitled 'A simulation conceptual modelling methodology for supply chain application'

Dr. Miles Weaver, PhD thesis entitled 'A simulation conceptual modelling methodology for supply chain application'

Awarded from Aston Business School, Aston University.

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    Miles Weaver PhD Thesis Miles Weaver PhD Thesis Document Transcript

    • A SIMULATION CONCEPTUAL MODELLING METHODOLOGY FOR SUPPLY CHAIN MANAGEMENT APPLICATIONS MILES WEAVER DOCTOR OF PHILOSOPHY ASTON UNIVERSITY OCTOBER 2010This copy of the thesis has been supplied on condition that anyone who consults it isunderstood to recognise that its copyright rests with the author and that no quotationfrom this thesis and no information derived from it may be published without properacknowledgement. 1
    • Aston University A Simulation Conceptual Modelling Methodology for Supply Chain Management Applications Miles Weaver Doctor of Philosophy 2010Thesis summaryThe research focuses upon the development of a simulation conceptual modelling methodologyfor SCM applications (termed the ‘SCM2’). The originality of the SCM2 is that it combines aprescribed procedure for simulation conceptual modelling with supply chain domain-specificknowledge. This procedure is used to guide participants to create a non-software specificdescription of the simulation model to be developed, in the context of SCM applications.The SCM2 is presented as a series of seven phases, associated steps, who participates in each step,information needs and points of entry between steps. The SCM2 is entered when a client has asupply problem to be evaluated using a simulation approach. The supply problem is described interms of the improvement(s) to be evaluated, for a given objective(s) within its supply setting.From this description, how each objective is to be measured and how each improvement is to berepresented is determined. The interconnections between model components and theimmediate supply setting are discriminated, model boundary formulated and level of detaildesigned. The output from the SCM2 is a documented and validated conceptual model.The need for a greater understanding of how to perform the conceptual modelling stage, as partof a simulation project, is shown to be of great significance and relevance. In particular the thesisargues that no methodologies exist that can guide participants in a simulation project through theprocess of creating a simulation conceptual model. A research methodological programme isdesigned to review existing modelling practice, form a specification for the methodology, developan outline for the SCM2, detail the outline through refinement and application and a preliminaryvalidation of the SCM2.The specification is formed to identify a set of requirements that the methodology shouldaddress. The methodology is developed to meet the specification by refining the outline designusing two developmental cases of typical and complex supply chain problems. The outline designis founded on existing practice for conceptual modelling and identifies ten key concepts that havebeen synthesised by considering the design issues for each requirement identified in thespecification. A major advance made by this thesis is a suggestion that the process of conceptualmodelling could benefit from utilising domain knowledge provided by the Supply Chain CouncilSCOR model. It is demonstrated that using SCOR is a powerful way to enable a more focused andefficient procedure for conceptual modelling. The methodology incorporates the key conceptsand aligns these with a general process for conceptual modelling. A preliminary validation with adifferent supply chain illustration demonstrates that the methodology is initially ‘feasible’ and has‘utility’. Future testing is required in different industrial contexts with actual participants and anopportunity exists to extend the methodology into a web-based application tool.KeywordsSupply chain management, conceptual modelling, simulation, performance evaluation 2
    • To my late grandfather for whom I hold great respect and pride Hon. Alderman Albert [Tom] Matthews MBE Forever my inspiration Orchards gay with blossom, Beauty, there to see, Hollows where breeze is tender, Moorlands where wind breaks free; Sowing, Lambing, and Harvest, Overlooked by Giant Clee, Hop Kilns, Farmsteads, and TENBURY, This is happiness is for me. Source: Anon 3
    • AcknowledgementsFirstly, I would like to sincerely thank Dr. Doug Love and Dr. Pavel Albores for supervising this PhDthesis and investing their time in mentoring my early research career. Their energy, drive andsupport has inspired, empowered and enabled me for which I am entirely grateful.I would like to warmly acknowledge my family and friends for their continued support,encouragement and understanding throughout my doctoral studies. My parents, David and PatWeaver, have been a source of determination throughout my life providing the bite to seekfulfilling goals, taking me to new horizons. My sister, Elaine Dolby and my brother, Nigel Weaver,have been there for me during the highs as well as the lows. Sarah Greenhouse provided me withstrength when times got challenging; I am entirely grateful for her support, detailed discussionsand time invested in me during the writing up of my thesis. Similarly, Sarah’s mother Josie keptsmiling and offering her time by proof-reading the final drafts – I shall never forget the supportand encouragement from ‘Team Greenhouse’. My two best friends, Paul and Neil, for helping meto switch off from time to time. I would also like to thank certain colleagues and friends inparticular: Alfred, Anita, Breno, Deycy, Emma, Eleanor, Helen, James, Joanna, Naomi, Natalia, NickT, T.T., Tony and Wenshin, who I have had the pleasure to work with or interact with during suchstimulating times.I would also like to thank researchers who have supported and shaped my doctoral work. Inparticular: Prof. Don Taylor (Virgina Tech), Prof. Rafaela Alfalla-Luque and Dr. Carmen Medina-Lopez (both from Seville University). The data for the industrial development case was gatheredby the FUSION research group (collaboration between Aston, Liverpool and Strathclyde). I amgrateful for all the support and fun times while conducting this research, and look forward tofuture collaborations and projects. 4
    • Notations used in thesisBeerCo Beer company supply chain caseCarCo Car company supply chain caseCHR Central headrests manufacturerCM Conceptual ModellingCoffeePotCo Coffee pot supply chain caseDES Discrete Event SimulationGSFC Global Supply Chain ForumLA Luxury Automotive ManufacturerMABM Multi-Agent Based ModellingSCM Supply Chain ManagementSCM2 Simulation conceptual modelling methodology for supply chain management applicationsSCOR Supply-Chain Operations Reference-modelSD Systems DynamicsSME Subject Matter ExpertSS Seat set manufacturerSSM Soft Systems MethodologyT Tracks manufacturer 5
    • PublicationsDuring the period of conducting this research the following publications have been contributedto:Albores, P., Love, D., Weaver, M., Stone, J. & Benton, H. (2006) An evaluation of SCOR modellingtools and techniques. Technology and Global Integration. IN: Proceedings of the Second EuropeanConference on the Management of Technology. Aston Business School, Birmingham, UK.Taylor, G. D., Love, D. M., Weaver, M. W. & Stone, J. (2008) Determining inventory service supportlevels in multi-national companies, International Journal of Production Economics, 116(1), 1-11.Niranjan, T., Weaver, M., (2010) A unifying view of goods and services supply chain management,The Service Industries Journal, iFirst Article, 1–20.Niranjan, T., Weaver, M., Pillai, S., (2009) Bridging between goods and services SCM: Some freshperspectives. Green Management Matters. IN: Proceedings of the Academy of ManagementAnnual Meeting. Chicago, Illinois, USAWeaver, M., Love, D. & Albores, P. (2008) Supply chain improvement options and their decisionvariables. Tradition and Innovation in Operations Management. IN: 15th Annual EurOMAConference of the European Association of Operations Management. University of Gronigen,Netherlands.Weaver, M., Love, D. & Albores, P. (2007a) A decision aid to select techniques to evaluate supplychain improvement options. Managing Operations in an Expanding Europe. IN: 14th AnnualConference of the European Association of Operations Management. Bilkent University, Ankara,Turkey.Weaver, M., Love, D. & Albores, P. (2006) Towards the development of a supply strategyevaluation methodology. Moving Up the Value Chain. IN: Conference of the European Associationof Operations Management. Strathclyde University, Scotland, UK. 6
    • Table of ContentsThesis summary ................................................................................................................................. 2 Keywords ......................................................................................................................................... 2 Acknowledgements ......................................................................................................................... 4 Notations used in thesis .................................................................................................................. 5 Publications ..................................................................................................................................... 6 List of figures in thesis................................................................................................................... 11 List of tables in thesis .................................................................................................................... 12Chapter 1 Introduction ................................................................................................................. 14 1.1 Research background ........................................................................................................ 14 1.2 Research aims, objectives and programme ...................................................................... 16 1.3 Justification for the research focus ................................................................................... 18 1.4 Outline of the thesis .......................................................................................................... 19 1.5 Delimitation of scope and definitions ............................................................................... 22 1.6 Chapter summary .............................................................................................................. 24Chapter 2 Research issues in conceptual modelling for SCM applications .................................. 26 2.1 Scope and selection of contributions in literature review ................................................ 27 2.2 Importance of evaluating supply chain problems............................................................. 29 2.3 Complexity of evaluating supply chain problems ............................................................. 31 2.4 Role of simulation to evaluate supply chain problems ..................................................... 32 2.4.1 Range of approaches used in simulation ................................................................. 33 2.4.2 Extent and usage of simulation for research ........................................................... 34 2.5 Role of conceptual modelling in simulation projects........................................................ 37 2.5.1 Importance of conceptual modelling in a simulation project .................................. 38 2.5.2 Key debates around the nature of conceptual modelling ....................................... 38 2.5.3 Defining conceptual modelling for supply chain problems ..................................... 40 2.6 Understanding of CM for SCM simulation applications .................................................... 43 2.6.1 General issues in understanding of conceptual modelling ...................................... 43 2.6.2 Application of the process of conceptual modelling for SCM problems ................. 45 2.7 Usefulness of a CM methodology for SCM applications ................................................... 46 2.8 Benefits of developing a conceptual modelling methodology for SCM applications ....... 48 2.9 Chapter summary .............................................................................................................. 49Chapter 3 Research programme for the development and preliminary validation of the SCM2 . 50 3.1 Justification of methodological approach ......................................................................... 50 3.1.1 Methodological approaches for the development of methodologies ..................... 51 3.1.2 Key methodological issues in the area of developing a methodology..................... 52 3.1.3 General methodological issues for developing the SCM2 ........................................ 53 3.1.4 Justification of five stage approach.......................................................................... 61 3.2 Research programme and methods.................................................................................. 64 3.2.1 Overview of research programme and methods ..................................................... 64 3.2.2 Stage I: Review of existing conceptual modelling practice ...................................... 65 3.2.3 Stage II: Forming the specification for SCM2............................................................ 67 3.2.4 Stage III: Discussion of the outline design for the SCM2 .......................................... 68 3.2.5 Stage IV: Discussion of the detailed and refined design of the SCM2 ...................... 70 3.2.6 Stage V: Preliminary validation of the SCM2 ............................................................ 71 3.3 Theory building through existing case study applications ................................................ 73 3.3.1 Involvement and reflexivity of the researcher ......................................................... 74 3.3.2 Consistency of the process....................................................................................... 74 3.3.3 Choice of supply chain application cases ................................................................. 75 3.3.4 Data collection methods .......................................................................................... 76 3.4 Limitations of research approach ..................................................................................... 77 3.5 Validity and reliability of the research .............................................................................. 78 7
    • 3.6 Ethical considerations and issues...................................................................................... 78 3.7 Chapter summary .............................................................................................................. 79Chapter 4 Review of existing CM (Stage I) .................................................................................... 80 4.1 Approaches to conceptual modelling in practice ............................................................. 80 4.1.1 Principles in conceptual modelling .......................................................................... 81 4.1.2 Methods of simplification ........................................................................................ 82 4.1.3 Modelling frameworks ............................................................................................. 85 4.2 Problems encountered in simulation modelling ............................................................... 86 4.3 Communicating and representing the conceptual model ................................................ 87 4.3.1 Simulation project specification............................................................................... 88 4.3.2 Representing the conceptual model ........................................................................ 89 4.4 Validation of conceptual models ...................................................................................... 91 4.5 Chapter summary .............................................................................................................. 94Chapter 5 Forming the specification for the SCM2 (Stage II) ........................................................ 95 5.1 Requirements for an ‘effective’ conceptual model .......................................................... 95 5.1.1 Four requirements of a conceptual model .............................................................. 96 5.1.2 Building ‘valid’ and ‘credible’ models ...................................................................... 97 5.1.3 Fundamental need to keep the model ‘simple’ ....................................................... 98 5.2 Requirements for ‘good’ methodologies .......................................................................... 98 5.3 Requirements for conceptual modelling of supply chain problems ............................... 100 5.3.1 Handle the complexity and detail of supply chain improvements ........................ 101 5.3.2 Address a range of supply chain objectives ........................................................... 105 5.3.3 Identify interconnections with the supply setting ................................................. 106 5.4 Chapter summary ............................................................................................................ 106Chapter 6 Outline design for the SCM2 (Stage III) ....................................................................... 108 6.1 Design issues for developing a ‘good’ methodology ...................................................... 109 6.1.1 General guide for conceptual modelling................................................................ 109 6.1.2 Role of participants in the process of conceptual modelling ................................. 111 6.1.3 Points of entry in the methodology ....................................................................... 112 6.2 Design issues for creating an ‘effective’ conceptual model............................................ 113 6.2.1 Keep the model as ‘simple’ as possible.................................................................. 113 6.2.2 Creating a ‘valid’ and ‘credible’ conceptual model ................................................ 115 6.3 Design issues for the domain specific needs for creating a CM...................................... 117 6.3.1 Opportunities to use a process reference model for creating a CM ..................... 117 6.3.2 Identification of a suitable process reference model for creating a CM ............... 118 6.4 Using SCOR for conceptual modelling............................................................................. 121 6.4.1 Using SCOR to describe supply chain improvements ............................................ 122 6.4.2 Using SCOR to describe supply chain objectives .................................................... 122 6.4.3 Using SCOR to determine the interconnections with the supply setting .............. 124 6.5 Presentation of outline design ........................................................................................ 125 6.5.1 Key concepts to be incorporated into the methodology ....................................... 125 6.5.2 Linking key concepts to phases in the SCM2 .......................................................... 128 6.6 Chapter summary ............................................................................................................ 130Chapter 7 Detailed design for SCM2 (Stage IV) ........................................................................... 132 7.1 Overview of the SCM2 ..................................................................................................... 132 7.2 Presentation of the development cases ......................................................................... 136 7.2.1 Development case 1: BeerCo ................................................................................. 136 7.2.2 Development case 2: CarCo ................................................................................... 137 7.3 Application of the development cases to refine and detail the SCM2 ............................ 138 7.3.1 Phase 1: Describe the supply problem from the client’s perspective ................... 139 7.3.2 Phase 2: Determine how each objective is to be measured .................................. 144 7.3.3 Phase 3: Determine how each improvement is to be represented ....................... 150 8
    • 7.3.4 Phase 4: Determine how the inputs and their sources interconnect .................... 153 7.3.5 Phase 5: Formulation of the model boundary ....................................................... 157 7.3.6 Phase 6: Design of the detail of the model ............................................................ 166 7.3.7 Phase 7: Validate and document the conceptual model ....................................... 172 7.4 Implementing the SCM2 using a spreadsheet application ............................................. 177 7.5 Alignment of detailed design of the SCM2 against specification..................................... 177 7.5.1 Meet the requirements for an ‘effective’ conceptual model ................................ 178 7.5.2 Meet the requirements of ‘good’ methodologies ................................................. 179 7.5.3 Meet the requirements for conceptual modelling of supply chain problems ....... 181 7.6 Chapter summary ............................................................................................................ 182Chapter 8 Preliminary validation of the SCM2 (Stage V) ............................................................. 184 8.1 Presentation of validation case: CoffeePotCO ................................................................ 184 8.2 Application of SCM2 to preliminary validation case ........................................................ 185 8.2.1 Phase one: Describe the supply problem............................................................... 186 8.2.2 Phase two: Determine how each objective is to be measured ............................. 187 8.2.3 Phase three: Determine how each improvement is to be represented ................ 189 8.2.4 Phase four: Determine the model inputs and source process elements ............... 190 8.2.5 Phase five: Formulate the model boundary .......................................................... 191 8.2.6 Phase six: Designing the model detail .................................................................... 194 8.3 Purpose of the evaluation of the methodology .............................................................. 195 8.3.1 Criteria for evaluating the feasibility of the SCM2 ................................................. 195 8.3.2 Criteria for evaluating the utility of the SCM2 ........................................................ 196 8.4 Evaluation of the initial feasibility of the SCM2............................................................... 196 8.4.1 Evaluation of the availability of information required by the SCM2 ...................... 197 8.4.2 Evaluation of the availability of information provided by the SCM2 ..................... 198 8.5 Evaluation of the initial utility of the SCM2 ..................................................................... 199 8.5.1 Relevance of output derived from the SCM2 ......................................................... 200 8.5.2 Usefulness of the output derived from the SCM2 .................................................. 200 8.5.3 How the methodology could be facilitated............................................................ 202 8.6 Identification of issues for testing................................................................................... 204 8.6.1 Feasibility ............................................................................................................... 204 8.6.2 Utility ...................................................................................................................... 204 8.6.3 Usability.................................................................................................................. 205 8.7 Opportunities to improve the SCM2................................................................................ 206 8.7.1 Role and impact of automating the methodology ................................................. 206 8.7.2 Strengthening the utilisation of domain knowledge ............................................. 207 8.7.3 Development of a web based tool ......................................................................... 208 8.8 Chapter summary ............................................................................................................ 208Chapter 9 Conclusion and future work ....................................................................................... 210 9.1 Original contribution made by the thesis ....................................................................... 210 9.1.1 Primary research contribution ............................................................................... 211 9.1.2 Secondary research contributions ......................................................................... 217 9.2 Conclusions from the research objectives and questions .............................................. 219 9.2.1 Objective one: Documentation of required specification...................................... 219 9.2.2 Objective two: Development of SCM2 addressing the specification ..................... 220 9.2.3 Objective three: Preliminary validation of the SCM2 ............................................. 221 9.3 Limitations of study ........................................................................................................ 222 9.3.1 Application in different industrial contexts with primary data.............................. 224 9.3.2 Use of different facilitators (potential users) to follow the SCM2 ......................... 224 9.3.3 Validation of the usability of the SCM2 .................................................................. 225 9.4 Implication for further research and practice ................................................................ 225 9.5 Chapter summary ............................................................................................................ 227 9
    • References................................................................................................................................... 229Appendix A Principles/observations made in the design of the SCM2 ...................................... 251Appendix B Actual practice to be modelled (BeerCO development case) ................................ 260Appendix C Actual practice to be modelled (CarCO development case) .................................. 263Appendix D Illustrations of model components to be developed into a computer model....... 266Appendix E Example process flow diagrams for the BeerCo development case ...................... 268Appendix F Flowchart of the CoffeePotCo computer simulation model .................................. 270Appendix G Comparison of practice to be modelled and CoffeePotCo computer model ........ 271Appendix H Evaluation of how information is used and provided in the preliminary validationcase ................................................................................................................................ 275Appendix I Issues for testing the ‘usability’ of the SCM2 ......................................................... 277 10
    • List of figures in thesisFigure 1.1 Overview of the thesis structure and research programme .................................... 20Figure 2.1 Classification of supply chain simulation approaches.............................................. 34Figure 3.1 Overview of research programme ........................................................................... 64Figure 6.1 Example of SCOR inputs and outputs to a decomposed business process............ 124Figure 7.1 Overview of the SCM2 ............................................................................................ 133Figure 7.2 Structure and flows in the BeerCo development case........................................... 137Figure 7.3 A simplified diagram of CarCo’s supply chain ........................................................ 138Figure 7.4 ‘Reliability’ metric structure with an example of a level 3 metric ......................... 147Figure 7.5 Calculation and data collection needs for RL.2.1 % of orders delivered in full ..... 148Figure 7.6 Extract of the SCOR descriptions of best practices ................................................ 151Figure 7.7 Example of the inputs of a source process element described in SCOR ................ 154Figure 7.8 Process elements, inputs, source process element and suggested source actor .. 155Figure 7.9 Extract of the list of inputs considered for S1.1 in the CarCo development case . 156Figure 7.10 Extract of how phase four was completed for the CarCo development case ....... 157Figure 7.11 Extract of the output from phase four that is transferred (in step 5.1) ................ 160Figure 7.12 Extract of phase five from the BeerCo development case for the Wholesaler ..... 163Figure 7.13 Template used to check the linkages between processes in the CarCo development case......................................................................................................................... 165Figure 7.14 Tracing back the inputs of included processes from a data source ....................... 166Figure 7.15 ‘Phantoms’ in the CarCo development case (inputs shown for D1.10 SS) ............ 168Figure 7.16 Extract of actual practice descriptions in the BeerCo development case ............. 169Figure 7.17 Extract of how actual practices can be ‘consolidated’ for the CarCo development case......................................................................................................................... 170Figure 8.1 Graphical illustration of CoffeePotCo supply problem .......................................... 185Figure 8.2 Interconnection identified for each process element in the model ...................... 190Figure 8.3 Formulation of the model boundary (CoffeePotCo) .............................................. 191Figure 8.4 Promoted, core and simplified process elements for the CoffeePotCo validation case......................................................................................................................... 194Figure E.1 Retailer plan and place order in BeerCo development case .................................. 268Figure E.2 Fulfil order in BeerCo development case ............................................................... 269Figure F.1 Flowchart of computer simulation program for CoffeePotCo ............................... 270 11
    • List of tables in thesisTable 2.1 Selection of contributions that meet search terms in each academic database ......... 29Table 2.2 Classification of simulation approaches....................................................................... 35Table 3.13 Similarities between an iterative triangulation and grounded theory method .......... 61Table 3.24 Design questions and issues to address the requirements ......................................... 71Table 3.3 5 Criteria for assessing a process framework or methodology ...................................... 73Table 3.4 6 Summary of cases used to develop and validate the methodology............................ 76Table 4.17 Approaches to conceptual modelling .......................................................................... 81Table 4.28 Research contributions on simulation model simplification (advice and methods) ... 84Table 4.39 Potential pitfalls in simulation related to conceptual modelling ................................ 86Table 4.410 Reasons for increasing complexity (some consideration in the SCM domain) ............ 87Table 4.511 Methods used to document CM with examples in the SCM literature ....................... 90Table 5.1 Four requirements for a conceptual model ................................................................. 96Table 5.2 Platts (1994) characteristics of successful strategy formulation methodologies ...... 100Table 5.3 Identification of the complexity of a supply problem ................................................ 103Table 5.4 Identification of the detail of supply chain improvements ........................................ 104Table 5.5 Aims and requirements for the SCM2 ........................................................................ 106Table 6.1 Proposed stages for conceptual modelling in general suggested in the literature ... 110Table 6.2 Incorporating model simplification advice and methods into a methodology .......... 114Table 6.3 Documentation and validation requirements for the SCM2 ...................................... 116Table 6.4 Role of domain knowledge in conceptual modelling ................................................. 117Table 6.5 Comparison of supply chain process reference models ............................................ 120Table 6.6 Domain knowledge offered by SCC SCOR model ....................................................... 121Table 6.7 Examples of two typical supply chain problems ........................................................ 122Table 6.8 Example of SCOR detail extracted for improvements ............................................... 122Table 6.9 Example of extracting SCOR performance measures ................................................ 123Table 6.10 Key concepts to be included in the design of the SCM2 ............................................ 126Table 6.11 Linking key concepts, conceptual modelling process with phases in the SCM2 ........ 128Table 6.12 Outline of the methodology: Phases, inputs and outputs......................................... 130Table 7.1 Detailed steps for phase one of the SCM2 ................................................................. 140Table 7.2 Description of the objective(s) of study ..................................................................... 141Table 7.3 Illustration of the description of the improvements selected ................................... 142Table 7.4 Illustration of the description of the supply problem setting .................................... 143Table 7.5 Illustration of how each improvement could achieve each objective ....................... 143Table 7.6 Detailed steps for phase two of the SCM2 ................................................................. 146Table 7.7 Description of the supply chain metrics..................................................................... 147Table 7.8 Description of calculation and data source requirements for each metric ............... 149Table 7.9 Description of the nature of measurement for each metric in both development cases .................................................................................................................................... 149Table 7.10 Detailed steps for phase three of business methodology ......................................... 150Table 7.11 List of processes at three levels of process detail that represent each SCIO ............ 152Table 7.12 List of actors associated with each business process ................................................ 152Table 7.13 Detailed steps for Phase 4 of the SCM2 ..................................................................... 154Table 7.14 Detailed steps for phase 5 of the SCM2 ..................................................................... 159Table 7.15 Detailed steps for phase 6 of the SCM2 ..................................................................... 167Table 7.16 Model components, definitions and examples (in the BeerCo development case) . 171Table 7.17 Detailed steps for phase 7 of the SCM2 ..................................................................... 174Table 7.18 Aligning the SCM2 to meet the requirements for an ‘effective’ model ..................... 178Table 7.19 Meet the requirements of ‘good’ methodologies ..................................................... 180Table 7.20 Meet the requirements for CM of supply chain problems........................................ 181Table 8.1 Statement of the supply problem (CoffeePotCo) ...................................................... 186Table 8.2 Statement of each objective to be measured (CoffeePotCo) .................................... 188 12
    • Table 8.3 Statement of how each process represents each improvement (CoffeePotCo) ....... 189Table 8.4 Promoted process elements and simplified inputs (CoffeePotCo) ............................ 192Table 8.5 Candidate process elements promoted in each round (CoffeePotCo)....................... 193Table 8.6 Summary of the feasibility criteria to be examined ................................................... 195Table 8.7 Summary of the utility criteria to be examined ......................................................... 196Table 8.8 Key observations from an analysis of the information requirements for the SCM2 .. 198Table 8.9 Key observations from an analysis of the information provided from the SCM2 ...... 199Table 8.10 Evaluation of ‘facilitation’ when using SCOR ............................................................. 203Table 8.11 Summary of the usability criteria to be examined .................................................... 205Table 8.12 Opportunities to automate aspects of the SCM2 ...................................................... 207Table 9.1 Research contribution made by this thesis................................................................ 211Table 9.2 SCM2: Procedure and key concepts for SCM applications ......................................... 214Table 9.3 Summary of issues for future testing ......................................................................... 223Table 9.4 Revisiting Robinson (2006a; 2006b) issues in CM ..................................................... 226Table A.1 Principle/observations when designing phase one ................................................... 251Table A.2 Principle/observations made that included the design of phase two ....................... 252Table A.3 Principle/observations made that influenced the design of phase three ................. 253Table A.4 Principle/observations made that influenced the design of phase four ................... 254Table A.5 Principle/observations when designing phase five .................................................... 255Table A.6 Principle/observations when designing phase six ..................................................... 257Table A.7 Principle/observations when designing phase seven ................................................ 258Table D.1 Model components for ‘Retailer plan and place order’ (BeerCo development case) 266Table D.2 Model components for ‘Wholesaler Receive and fulfil order’ (BeerCo developmentcase) .................................................................................................................................... 267Table G.1 AS-IS Scenario in CoffeePot Co validation case for the ‘Customer’ ........................... 271Table G.2 AS-IS Scenario in CoffeePotCo validation case for the ‘Warehouse’ ......................... 272Table G.3 AS-IS Scenario in CoffeePotCo validation case for the ‘Factory’ ................................ 273Table G.4 TO-BE Scenario in CoffeePotCo validation case for the ‘Factory’ .............................. 274Table H.1 Evaluation of how the SCM2 uses information........................................................... 275Table I.1 Issues for testing the ‘usability’ of the SCM2 ............................................................. 277 13
    • Chapter 1 IntroductionChapter one discusses the context of the research project for the development, refinement andpreliminary validation of a simulation conceptual modelling methodology for supply chainmanagement applications (termed the ‘SCM2’). It describes the background to the research,research objectives, questions and programme to address each objective, justification forresearch focus, main body of the thesis, and the extent of the scope and definitions used in theresearch project.The research is bounded within the ‘simulation’ literature with a focus on the ‘conceptualmodelling’ stage of a simulation project in the context of ‘SCM’ applications. The researchobjectives and questions address the need to form a specification for, develop and refine andinitially validate the feasibility and utility of the SCM2 proposed in this thesis. A five stage researchprogramme is designed to realise the aims and objectives of this research and address each of thequestions posed. This includes reviewing existing conceptual modelling practices (stage I,presented in chapter four), forming the specification for the SCM2 (stage II, presented in chapterfive), outlining the design for the SCM2 (stage III, presented in chapter six), detailing and refiningthe design for the SCM2 (stage IV, presented in chapter seven) and a preliminary validation of theSCM2 (stage V, presented in chapter 8). The research programme adopts an iterativetriangulation method to systematically iterate between extensive literature review, existing caseevidence and intuition. Three typical and complex supply problems are used to refine andpreliminarily validate the methodology.1.1 Research backgroundThe research focuses upon the creation of simulation conceptual models for supply chainapplications. The methodology is developed within the supply chain management discipline forparticipants undertaking the conceptual modelling stage as part of a simulation project. Thisfocus is original and significant because the need for a greater understanding of conceptualmodelling is required; particularly the development of structured approaches, as no simulationconceptual modelling methodologies exists in the SCM domain. Both the wider discipline and theparticular focus of this thesis are briefly discussed to provide some background to the project.The origins of the use of the term ‘supply chain management’ (SCM) can be traced back to theearly 1980s (Houlihan, 1987); over the last three decades the prominence and importance of thediscipline has grown at an escalating rate. During this period, similar terms such as ‘networksourcing’, ‘supply pipeline management’ and ‘value chain management’ have been subjects of 14
    • interest, for both theory and practice (Christopher, 2004; Hines, 1994; Lamming, 1996; Saunders,1995, 1998; Croom, Romano and Giannakis, 2000).There has also been some debate over whether supply chain management is itself adistinguishable discipline in its own right (e.g. Croom et al., 2000; Harland, Lamming, Walker,Phillips, Caldwell, Johnsen, Knight and Zheng, 2006). Harland et al., (2006) judged SCM to be anemerging discipline, providing evidence that existing research contributions lack quality oftheoretical development, discussion and coherence. In relation to practice, there is widespreadagreement that SCM is critical to organisational performance (e.g. Tan, Kannan and Hardfield,1999; Kannan and Tan, 2005; Li, Ragu-Nathan, B., Ragu-Nathan, T.S., and Subba-Roa, 2006).Additionally as a field of study, it has gained significant momentum, as new opportunities exist todevelop new theories, concepts and tools that could be applied in practice.Despite the popularity of the term ‘SCM’ both in academia and in practice there has beenconsiderable confusion as to its meaning (Mentzer, Dewitt, Keebler, Min, Nix, Smith and Zacharia,2001). Some authors have defined SCM in operational terms involving the flow of materials andproducts, some view it as a management philosophy and some view it in terms of a managementprocess (Tyndall, Gopal, Patsch and Kamauff 1998). Mentzer et al., (2001) reviewed, categorisedand synthesised a view of what constitutes SCM from definitions used in both research andpractice in order to reduce this ambiguity. Mentzer et al., (2001, pg. 4) contended that a supplychain can be defined as a set of three or more entities (organisations or individuals) directlyinvolved in the upstream and downstream flows of products, services, finances, and/orinformation from a source to a customer. This definition is similar to Christopher’s (2004)definition as it highlights the structure, linkages and flows in a supply chain. In relation toChristopher (2004) he also highlights how processes and activities ‘add value’ to a product andservice. From a strategic management perspective, ‘value’ concerns what Tan, Kannan andHanfield, (1998) describes as the utilisation of resources and capabilities to build competitiveadvantage. The term ‘supply strategy’ has also been suggested as a way to move SCM from apredominantly operational domain (relating to the flow of material and information) to one thatalso considers strategic aspects (Harland, Lamming and Cousins, 1999).Simulation has been used as a method to evaluate the complexity of supply chain problems (e.g.Ridall, Bennet and Tipi, 2000; Huang, Lau and Mak, 2003; Van der Zee and Van der Vorst, 2005). Itis regarded as the proper means for supporting decision making on supply chain design (Van derZee and Van der Vorst, 2005). One important component of a simulation modelling process is the 15
    • need to create a conceptual model. However it is the least understood aspect in the process(Law, 1991; Robinson, 2004a; 2004b, 2008a; 2008b). The need to formulate the problemprecisely has appeared in all descriptions of how to conduct a simulation project (e.g. Shannon,1975; Law and Kelton, 2000), although perhaps the first use of the term ‘model conceptualisation’can be found in Musselman (1994). After this period the term and discussions of conceptualmodelling practice have become more common (e.g. Robinson and Bhatia, 1995; Balci, 1997; Law,2003) and, more recently, some definitions have been offered (e.g. Banks, 1999; Robinson, 2004a;2004b; 2008a; 2008b).A simulation model in the context of evaluating supply chain problems can be defined using Pidd’s(1998) definition. A simulation model for SCM applications is a representation of the supplysystem, used to investigate possible improvements and the effect of these improvements in thereal world setting of the supply problem. The conceptual model is a non-software specificdescription of the simulation model to be developed (Robinson, 2004a; 2004b). It describes thesupply chain problem in terms of the objective of the study, improvements selected to improveperformance within its defined supply setting, the content of the model and any assumptions andsimplifications incorporated into the model. More specifically, using Banks’ (1991) terms, thecontent concerns the relationships between the components and structure of the supply system.These are described in terms of the scope (the components and relationships that need to beincluded in the model to define its structure) and detail necessary to represent the actualpractices to be modelled.1.2 Research aims, objectives and programmeThe aim of the research presented in this thesis is to: “Develop, refine and preliminarily validate a methodology that utilises domain- knowledge combined with a procedure that can be followed to create a simulation conceptual model for SCM applications”This aim is fulfilled by achieving three research objectives: 1. Objective One – Document a specification of the requirements for creating simulation conceptual models for SCM applications 2. Objective Two - Develop and refine a methodology that can meet the specification of the requirements for creating simulation conceptual models for SCM applications 3. Objective Three – Preliminarily validate the initial feasibility and utility of the methodology to create simulation conceptual models for SCM applications 16
    • A five stage research programme has been designed which contributes to the attainment of eachof the research objectives noted previously. An iterative triangulation method is justified toground theory development using existing case applications. This method is used to apply theSCM2 to typical and complex supply problems to firstly refine and secondly preliminarily validatethe procedure to be followed that incorporates the use of domain-knowledge. The processiterates between case evidence, reviewed literature and intuition to develop knowledge prior torigorous testing so that the SCM2 can be extended into a cohesive theory (testing is noted asfurther work).The first objective identifies a specification of the requirements for a simulation conceptualmodelling methodology for SCM applications. To achieve this two research questions are posed: 1. How are simulation conceptual models created in the context of supply chain applications? 2. What is the specification of a simulation conceptual modelling methodology for evaluating supply chain problems?These questions form the basis of stage I (Review of existing conceptual modelling practice,discussed in chapter four) and stage II (Required specification for the SCM2 to be developed,discussed in chapter five) of the research programme. A review of existing conceptual modellingpractice in the domain of SCM demonstrates the need for a methodology that can be followed forSCM applications. Following on from this, a specification is detailed for an effective conceptualmodel, characteristics of a good methodology, and the requirements for evaluating supply chainproblems.The second objective develops and refines a methodology that can meet the specification of therequirements for creating simulation conceptual models for SCM applications. To achieve thisobjective, one research question is posed: 3. Can a simulation conceptual modelling methodology be developed to meet the required specification?This question forms the basis for stage III (outline design for the methodology, discussed inchapter six) and stage IV (detailed and refined design for the SCM2, discussed in chapter seven) ofthe research methodological programme. The methodology is grounded in existing conceptualmodelling practice and ten key concepts are identified to be incorporated into a general processfor conceptual modelling. The methodology is refined through the application of two typical and 17
    • complex supply chain development cases before the revised design is aligned to show that itmeets the specification of the requirements.The third and final objective provides a preliminary validation of the initial feasibility and utility ofthe methodology to create simulation conceptual models for SCM applications. To achieve thisobjective, one research question is posed: 4. Can the methodology be followed (feasibility) and aid a user (utility) to create a simulation conceptual model for a SCM application?This question forms the final stage, stage V (preliminary validation of the SCM2, discussed inchapter eight) of the research methodological programme. This addresses two of Platts (1993)criteria for testing a methodology, process, or framework. It is argued that both the feasibilityand utility of the methodology can be initially validated by applying it to a different supply chainproblem. The validation case is a supply chain problem which has been evaluated by a simulationapproach and published in the academic literature (see Taylor, Love, Weaver and Stone, 2008). Itis used to compare the actual practices that have been identified by the methodology with themodel components and interconnections that are included in the computer model. The validationcase is also used to suggest how the feasibility and utility of the methodology should be furthertested and how further work should include tests for its ‘usability’. The discussion also identifiesand considers some opportunities to develop a web-based application tool to improve theaccessibility and efficiency of the methodology.1.3 Justification for the research focusEffective SCM is critical to any organisation’s ability to compete effectively (Stewart, 1997), whichhas led to organisation’s seeking ways to improve supply chain performance. The difficulty whenevaluating supply chain problems is that they are inherently complex and dynamic systems (e.g.Davis, 1993; Levy, 1994; Beamon, 1998). Simulation is one approach that is often cited as amethod that can be used to evaluate complex and dynamic systems (e.g. Ridall et al., 2000; Huanget al., 2003; Van der Zee and Van der Vorst, 2005); the extent of research that has used simulationas a method to evaluate supply chain problems is great.In a typical supply chain project there is one stage that is often regarded as the most important:the process of creating a conceptual model (Law, 1991). Robinson (2008b) points out that there issurprisingly very little written on the subject; except in Robinson’s (2004b) simulation text. Evenwhen looking at this text only a handful of pages are dedicated to the subject and in the widerliterature there is a distinct lack of research contributions. Research can be identified on 18
    • understanding the importance of ways of thinking of tackling a simulation problem (e.g. Nance,1994; Robinson, 1994; Brooks and Tobias, 1996). This has yet to deliver structured approaches forcreating a conceptual model. Although some guiding principles, methods for simplifications, andframeworks for completing the stage as part of a simulation project can be found. In an attemptto remedy this situation a stream was organised at the Operational Research Society SimulationWorkshop in 2006. Robinson (2008b) noted that this stream represented the highest number ofconcentrations of papers on this topic in comparison to previous journals or conference papers.This was a major motivation for this research, particularly as the majority of work was at aconceptual (early) stage. In addition the majority of contributions was based on manufacturingproblems and had not explicitly addressed the needs of SCM.This thesis demonstrates that the complexity and dynamic behaviour inherent in supply chainspresents a different set of requirements. The problems are not confined to a single organisationand the improvements that an evaluator may wish to experiment with are much wider and, onthe whole, different from manufacturing problems. This thesis also suggests ten key conceptsthat could form the basis of a methodology for creating simulation conceptual models for SCMapplications. In particular, the research argues that there is a significant opportunity to utilisedomain knowledge from a published supply chain process reference model (e.g. Supply ChainCouncil SCOR model) aligned with a general process for conceptual modelling.The intention of the work is to enable relevant and significant advances for conceptual modellingas an area that requires further research, practice and the teaching of simulation. Themethodology requires further work to enable an application to be developed that incorporatesthe methodology, made accessible for potential users to benefit from. In addition to this primarycontribution a number of secondary contributions are suggested that should provide avenues forfurther study and advancement.1.4 Outline of the thesisThe thesis is organised into nine chapters and four main parts, as depicted in figure 1.1. Theseparts include the introduction, development of the research aim, objectives and programme,research programme implementation and conclusions. 19
    • Introduction • Chapter 1: Introduction to research project Development • Chapter 2: Research issues in simulation of research conceptual modelling for SCM applications aim, objectives • Chapter 3: Research programme for and developing, refining and preliminary programme validating the SCM2 • Chapter 4: Stage I: Review of existing simulation conceptual modelling practice • Chapter 5: Stage II: Forming the specification for the SCM 2 • Chapter 6: Stage III: Outline Research design for the SCM2 programme • Chapter 7: Stage IV: Detailed implementation and refined design for the SCM2 • Chapter 8: Stage V: Preliminary validation of the initial feasibility and utility of the SCM2 • Chapter 9: Conclusions and future Conclusion implications of the researchFigure 1.1 Overview of the thesis structure and research programmeThe contribution of the remaining chapters in this thesis can be summarised:Chapter 2 Discusses the research issues in simulation conceptual modelling for SCM applications. It demonstrates that there is a gap for the development of the SCM2 and that is both original and significant. The importance of evaluating supply chain problems to improve organisational performance is discussed. This is followed by highlighting the complexity of evaluating supply problems. Simulation is argued as one major approach that can address the complexity of supply chain problems. An aspect of a simulation project that is not well understood but is of crucial importance is the process of conceptual modelling. There are no guidelines available to follow in order to create a conceptual model for SCM applications, therefore the development of a methodology is argued as a way to address this need. The benefits to both research and practice are identified. 20
    • Chapter 3 Presents an overview of the research programme and methods to address the aim and objectives of the research project. The stages that should be included in the programme are justified and suitable methods are identified for each stage.Chapter 4 Presents the implementation of stage I of the research programme by reviewing existing simulation conceptual modelling practice in the context of SCM applications. The chapter establishes the need for the methodology. Approaches to conceptual modelling are identified and reviewed to show that no methodology exists that delivers the aim of this research. It also discusses the problems encountered in a simulation project that could benefit from a greater understanding and structured methods for conceptual modelling. The methods of communicating and representing the conceptual model and how conceptual models have been validated are identified as two aspects that warrant greater discussion.Chapter 5 Presents the implementation of stage II of the research programme by forming a specification for the SCM2. The methodology is founded upon existing conceptual modelling practice and the requirements for, an effective conceptual model, a good methodology and for conceptual modelling within the domain of SCM. The specification is detailed so that the methodology can be developed to meet the requirements.Chapter 6 Presents the implementation of stage III of the research programme by outlining a design for the SCM2. The chapter brings together and suggests ten key concepts from a review of the design issues for each of the requirements identified in chapter five. The proposition developed in the chapter is that a procedure for the SCM2 can utilise domain knowledge from a supply chain process operations model to enable a more focused and efficient process.Chapter 7 Presents the implementation of stage IV of the research programme by detailing a developed and refined design for the SCM2. Two typical and representative supply chain development cases are used to refine the methodology. The ten key concepts identified in chapter six are incorporated into a procedure for the methodology. Each of the phases is discussed in turn so that the specific steps, information needs, participation requirements and points of entry can be 21
    • detailed. The chapter concludes by aligning the detailed design to demonstrate that the specification presented in chapter five has been met.Chapter 8 Presents the implementation of stage V of the research programme by preliminarily validating the initial feasibility and utility of the SCM2 to a different supply chain problem. The validation case is used to walkthrough the steps to demonstrate that they can be followed to create a simulation conceptual model. The validation only considers the phases up to the point that the actual practices to be included in the model are detailed, after this point existing modelling practice is adopted. It also enables a comparison between a successful computer model, which has been published in the literature, to be compared to a list of actual practices identified by the methodology. Issues for future testing are discussed and an opportunity to simplify and automate aspects of the process in a tool that utilises published domain knowledge is considered.Chapter 9 Concludes the thesis and discusses the future implications for the research. It details the primary and secondary contributions made by this thesis. The research aim and objective is reviewed to demonstrate that they have been met and that the research programme was both suitable and rigorous. Limitations of the work are described and implications for further study are identified.1.5 Delimitation of scope and definitionsThe research focuses upon the creation of simulation conceptual models for supply chainapplications rather than conceptual modelling in general. The implication of this is that themethodology presented in this thesis is intended for participants who are undertaking asimulation project with a supply chain problem. The analysis and information provided by themethodology would be different in other domains (e.g. manufacturing, service). Nevertheless,outside this scope the research has many implications for the key concepts incorporated into themethodology that could be applied in other domains (e.g. how to formulate the model boundary).Within this scope there are a number of considerations that need to be raised: 1. Definition of a supply problem 2. What constitutes a simulation conceptual model for SCM applications 3. Limitations of the research programme 22
    • The term ‘supply problem’ is used to incorporate the improvements that have been selected toimprove performance for a given objective within the setting of the supply problem. This term isused as it identifies that a supply problem can be made up of a range of improvements (e.g.improve supply chain visibility), to achieve a range of supply chain performance measures (e.g.responsiveness, cost) within the setting of the supply problem (e.g. linkages between suppliersand customers). In relation to the latter, conceptual modelling involves formulating anunderstanding of what should be included within the simulation study. This presents an issue ofdetermining only the necessary model components and interconnections that represent theactual practices of the real world problem. The term should not be confused with the term supply“chain”, or even “network”. A supply chain/network has a specific definition which includes the‘entities directly involved in the upstream and downstream flows of products, services, finances,and/or information from a source to the customer’ (Mentzer et al., 2001, pg. 4). Thisdemonstrates that the term ‘supply problem’ defines more than the structure and flows in asupply system but also how it is to be improved and how performance will be measured.The research is bounded within the ‘simulation’ literature with a focus on the ‘conceptualmodelling’ stage of a simulation project. Definitions do exist for conceptual modelling in generalbut there is considerable debate into what is described by a simulation conceptual model(discussed in section 2.1). The majority of the work in this thesis is underpinned by the majoradvances made by Robinson, most notably in his 2004 text on ‘simulation practice andapplication’ and associated publications. These have considered effective conceptual modelling(Robinson, 1994) issues for conceptual modelling research and practice (2006a; 2006b; 2008a)and the development of a general framework (Robinson, 2004a; 2004b; 2006a; 2006b) which hasuntil recently been illustrated (Robinson, 2008b). Robinson’s definition for a conceptual model isadopted in this thesis and used to further a definition for what constitutes a methodology thatcan be followed to create a conceptual model for SCM applications.A conceptual model is defined as: ‘...a non-software specific description of the simulation model that is to be developed, describing the objectives, inputs, outputs, content, assumptions and simplifications of the model’ Robinson (2004b, pg. 65)In the context of this thesis the methodology delivers: ‘A methodology that offers a prescribed procedure that guides the participants undertaking the conceptual modelling stage of a simulation project, to create a non- software specific description of the simulation model to be developed, in the context of SCM applications’ 23
    • The definitions provide some useful distinctions that have shaped this research project. Thisincludes that the definitions view the process of conceptual modelling as independent fromparticular simulation software. The intention of this research is to not be biased by any particularsoftware used by the researcher. However, when describing the model components a modellermay have a particular simulation worldview (Pidd, 2004b; Owen, Love and Albores, 2008) whichwill have a bearing on the way in which the computer model to be developed is described. Forthis reason the methodology incorporates general terms and practice for describing thecomponents in the model. The implication of this is that only the original aspects of themethodology are applied and tested.The preliminary validation is used to illustrate the initial feasibility and the utility of themethodology. The actual practices to be included in the computer model are compared to thecomponents and interconnections that form the design of the computer model presented inTaylor et al., (2008). The supply problem evaluated in Taylor et al., (2008) is simulated using adiscrete-event simulation approach. The research notes to be able to generalise the feasibilityand utility of the methodology it would require further applications in different industrial contextsand with actual participants. This would also involve testing the general usability of themethodology.1.6 Chapter summaryThe aim of this research is to develop, refine and preliminarily validate the initial feasibility andutility of a simulation conceptual modelling methodology for SCM applications. The researchobjectives are designed to realise this aim. These include: Documenting a specification of the requirements for creating simulation conceptual models for SCM applications Developing and refining a design of the methodology that meets the specification Validating the initial feasibility and utility of the methodology.The research focuses on creating conceptual models that describe how a supply problem can bedescribed so that a computer model can be developed. This is identified as an original andsignificant area for research as no methodologies exist that can meet the research aim.Particularly there is a need to develop structured approaches that can guide participants throughthe process of conceptual modelling as part of a simulation project within the domain of SCM. 24
    • A five stage programme has been designed to achieve the aim and objectives set out in this thesis.This includes a review of existing conceptual modelling practice (stage I, implemented in chapter4), forming the specification for the SCM2 (stage II, implemented in chapter 5), outlining a designfor the SCM2 (stage III, implemented in chapter 6), detailing and refining the design of the SCM2(stage IV, implemented in chapter 7) and a preliminary test of the SCM2 (stage V, implemented inchapter 8). An iterative triangulation research approach is adopted to iterate between anextensive literature review, application of the methodology to three representative and typicalsupply chain problems and intuition. Two existing cases are used in the design and refinementstages, and one to illustrate the initial feasibility and utility of the methodology.The methodology is developed for the purpose of creating a conceptual model for supply chainapplications, not for general purposes. The preliminary validation is used to illustrate that theactual practices to be represented in the computer model can be derived by following the steps aslaid down in the methodology. The components and relationships between them, that form thedescription of the computer model developed in Taylor et al., (2008) are compared to the actualpractices described by following the methodology to discuss any similarities, omissions orsignificant differences. Future testing is outlined in this thesis to improve the validity and widerapplicability of the methodology in different applications and involvement of potential users. 25
    • Chapter 2 Research issues in conceptual modelling for SCMapplicationsThis chapter identifies and discusses the relevant research issues in conceptual modelling for SCMapplications. The aim is to demonstrate that a gap exists for a simulation conceptual modellingmethodology for SCM applications that is original and significant. This gap is filled by developingand preliminary validating a simulation conceptual modelling methodology for SCM applications.This chapter is structured to demonstrate this gap by considering the following research issues: Scope and selection of contributions in literature review (section 2.1) – States that the research is bounded within the simulation conceptual modeling literature with a particular focus on SCM applications Importance of evaluating supply chain problems (section 2.2) - Discusses the importance of evaluating supply problems as one significant way to improve performance Complexity of evaluating supply chain problems (section 2.3) – Demonstrates that evaluating supply chain problems is extremely complex Role of simulation to evaluate supply problems (section 2.4) – Identifies that simulation is one approach that can address the complexity of supply problems. The range of approaches used in simulation is overwhelming and the amount of research using simulation is great. Role of conceptual modelling in simulation projects (section 2.5) - Identifying that conceptual modelling is an important and critical aspect in a simulation modelling process. Understanding of conceptual modelling for SCM applications (section 2.6) - Demonstrating that conceptual modelling is the least understood aspect of a simulation project and no guidelines exist for SCM applications. A gap exists in the literature that can be filled by the aim and focus of this thesis. Usefulness of a conceptual modelling methodology for SCM applications (section 2.7) - Proposes that a methodology would be a useful way to guide participants through a complex supply problem to describe how it could be modelled Benefits of developing a conceptual modelling methodology for SCM applications (section 2.8) - Showing that a methodology would yield benefits to practitioner users 26
    • 2.1 Scope and selection of contributions in literature reviewThe scope of the literature review gathers contributions on ‘conceptual modelling’ for ‘simulation’purposes within the domain of ‘SCM applications’. The term ‘conceptual modelling’ has howeverbeen used much more widely in the general management literature. In general a conceptualmodel is a ‘set of concepts, with or without propositions, used to represent or describe (but notexplain) an event, object, or process’ (Meredith, 1993). The description is also used as a means ofcommunicating a set of requirements between stakeholders involved in a project. Using thisgeneral definition there are a number of application areas that have used the term ‘conceptualmodelling’; examples include: Architecture, engineering and construction – e.g. Krause, Luddeman and Striepe (1995) for industrial design; Turk (2001) on conceptual product modeling; Shane (2005) on conceptual modeling in urban design and city theory Business management – e.g. Carrol (1979) for conceptual modeling of corporate performance and Parasuraman (1985) describe a conceptual model of service quality Computing and web engineering – e.g. Thompson (1991) personal computer utilisation Information systems development – e.g. Olive (2007) for conceptual modelling of information systems; Mendes et al., (2006) for conceptual modelling of web applications and Schewe and Thalheim (2005) for conceptual modelling of web information systems Research methods – e.g. Meredith (1993) discuss theory building through conceptual methods; Hair et al., (2007) discuss conceptualisation and research design in general.There are three notable differences that distinguish ‘simulation conceptual modelling’ from theapplication areas noted above. This includes the domain to be represented, scope and level ofabstraction and the process to be followed to create a conceptual model. For instance, anarchitectural conceptual model could include a model replica of a bridge to a particular scale. Insimulation conceptual modeling the requirement is to describe the computer model to be built.This includes the inputs, outputs, content (involves determining the scope and level of detail),assumptions and simplifications (Robinson, 2004). The process to identify these requirementsmoves from a problem situation, through model requirements to a definition of what is going tobe modeled and how it is to be done (Robinson, 2008a). A procedure for simulation conceptualmodeling must provide guidelines on how this is to be achieved, which is heavily dependent uponthe domain (e.g. supply chain) being represented.One particular approach that has been used in the context of simulation conceptual modelingincludes Checkland (1981) ‘soft system methodology’ (SSM) to determine the simulation study 27
    • objectives (see Kotiadis, 2007). SSM includes a stage for building a conceptual model to describeactivities and processes from a root definition (problem statement). In this instance, theconceptual model is represented as a rich picture that captures a human system of issues, actors,problems, processes, relationships, conflicts and motivations. Kotiadis (2007) argues that thestudy objectives are the most critical part of a simulation study which benefits from using SSM asa problem structuring method. This is however, only the first step of the process of creating asimulation conceptual model. SSM does not explicitly guide a modeller through the decisionsnecessary to determine the scope and level of detail (model content) or even incorporate anynecessary assumptions and simplifications into the model design (e.g. specific techniques such asaggregate model components).The literature review selection criterion has focused upon conceptual modelling for the purposesof simulation, particularly in the SCM domain using the terms shown in table 2.1. It was alsonecessary to include a wider search for operations management research as this is often used asan umbrella term. The key words ‘supply chain’ were adopted over ‘supply chain management’ toprovide a more exhaustive list. Secondly, more specific searches were undertaken to establish amore focused body of knowledge that discusses ‘conceptual modelling’. The term ‘conceptualmodel’ was also searched recognising that ‘conceptual modelling’ relates to the process thatcreates a ‘conceptual model’. The literature was searched in four primary academic databasesused in management research along with the WinterSim conference (annual simulationconference) and a dedicated Workshop that addressed a call for more research into simulationconceptual modelling (Operational Research Society Simulation Workshop, 2006). Theconference contributions accounted for some of the earlier and latest contributions on simulationconceptual modeling. 28
    • Table 2.1 Selection of contributions that meet search terms in each academic database Search terms Simulation Simulation Simulation Simulation Academic Simulation AND AND AND AND Simulation Simulation literature AND “conceptual “conceptual “Conceptual “Conceptual AND Supply AND database “conceptual modelling” modelling” model” AND model” AND chain Operation modelling”1 AND AND supply Operation “Supply chain” operation chainABI/Inform Global 499 226 11 0 1 9 1 Proquest EBSCO (Business 408 313 9 2 2 12 1 SourceComplete) Emerald 88 50 7 1 1 2 1 Science 45 56 161 19 1 44 17 Direct Informs 727 1,720 72 32 16 131 60 online22.2 Importance of evaluating supply chain problemsManaging supply-chain operations is critical to any company’s ability to compete effectively(Stewart, 1997). Over the past two decades there has been an acceleration of interest in theanalysis, management and control of supply chains (e.g. Gattorna and Walters, 1996; Beamon,1998; Petrovic, 2001; Christopher and Towill, 2002; Persson and Olhager, 2002; Shepherd andGunter, 2006; Gunasekaran and Ngai, 2008; Fildes, Goodwin, Lawrence and Nickolopoulos, 2009).Whether it is by coordination of activities through the supply chain or by recognising thecapabilities of immediate suppliers, understanding supply chain dynamics has a significant impacton performance (e.g. Tan et al., 1999; Kannan and Tan, 2005; Li et al., 2006).Supply chain management represents one of the most significant paradigm shifts of modernbusiness management by recognising that individual businesses no longer compete as solelyautonomous entities, but rather as supply chains (Christopher, 1992, 1998; Lambert and Cooper,2000; Spekman, Spear and Kamauff, 2002; Cousins and Spekman, 2003; Chen and Paulraj, 2004).This has led both researchers and practitioners, to consider improvements and practices outsidethe boundaries of an organisation (with suppliers and customers in a network, chain orpartnership) and ways to effectively manage and control the supply chain. The term ‘supplystrategy’, coined by Harland (1997), has been one significant attempt to move away from atraditional view of the flows between suppliers and customers to one that considers a moreholistic approach to managing the entire supply network. As a field of study and practice therehas been a whole host of other attempts to move research from an embryonic stage (suggested1 UK spelling is ‘modelling’ while in US dictionary it is spelt ‘modeling’; accounted for in the search2 Wintersim and Operational Research Society Simulation Workshop (accessed via www.informs-sim.org) 29
    • by Handfield and Melnyk, 1998; Chen and Paulraj, 2004); to one that has more scientificdevelopment and recognition as a discipline in its own right (Croom et al., 2000).There has also been a considerable interest to describe supply chain management, its activities,practices and ways to measure supply chain performance. In earlier years this was a distinct issuein SCM (New, 1997; Tan, 2001) but has been dramatically improved with recent researchcontributions. Most notably the advent of supply chain process frameworks developed by theGlobal Supply Chain Forum (e.g. Cooper, Lambert and Pagh, 1997; Croxton, Garcia-Dastugue,Lambert and Rogers, 2001; Lambert et al., 2005) and the Supply Chain Council (SCOR v.9, 2008)with considerable input from industry may have been a catalyst. These frameworks have beenused to describe, analyse and evaluate improvements to a supply system in order to improvedecision-making on ways to improve supply chain performance (e.g. Arns, Fischer, Kemper andTepper, 2002; Bolstorff and Rosenbaum, 2003; Wang, Huang and Dismukes, 2004; Ball, Love andAlbores, 2008; Persson and Araldi, 2009).The ability to evaluate the potential performance of supply chain opportunities is a criticalcomponent of the supply chain improvement process. The challenge companies’ face is how bestto evaluate the potential of the host of supply chain improvement options that could be pursued(Weaver, Love and Albores, 2006; 2007). Many of these improvement options have beendiscussed in the literature (e.g. Supply Chain Council 2008; Van der Vorst and Beulens 2002;Christopher, 1998; Berry et al., 1994). The fact that the Supply Chain Council suggests 420different improvement options, demonstrates the considerable scope of the evaluation challenge.Even when this number is reduced (e.g. Van der Vorst and Beulens (2002) presents a generic listof 21 supply chain redesign options) it still presents a challenge to identify the most suitableoptions based on the improvements in performance an organisation would realise.Companies have far too often attempted to optimise their own value chains, without consideringthe effect of these decisions on their suppliers or customers (Chopra and Meindl, 2004). Forinstance, Cooper et al., (1997) have shown that sub-optimisation of a company’s ownperformance rather than optimising the performance of the entire supply network, by integratingits goals and activities with other organisations, can destroy value-creating opportunities.Approaches (e.g. methods, frameworks, methodologies) that could aid in the process ofevaluating the value-creating potential of implementing alternative supply chain improvementsfor an organisation and its members in a supply network would be useful. They could help to 30
    • maximise an organisation’s performance and the benefits received up (towards the ultimatecustomer) and down the supply chain.2.3 Complexity of evaluating supply chain problemsA definition of a supply system was offered in section 1.1. It recognised that the entities comprisea number of actor (or roles, facilities) that make up the structure of the supply and demand chainin which an organisation (e.g. manufacturing, retail or third sector) sits between. The complexityof the supply chain arises from the number of echelons in the chain and the number of actors ineach echelon (Beamon, 1998).The supply system can vary in complexity (e.g. size). Harland (1997) identified different levels ofsupply, consisting of supply within the boundary of the firm (a process view), supply in dyadicrelationships, supply in an inter-organisational chain and supply in an inter-organisationalnetwork, each of these levels involve different degrees of complexity. The complexity is alsocompounded by the way in which actors within a dyad, chain or network can interact. As Levy(1994) points out that the interactions are strategic in sense as a decision made by one actor takeinto account anticipated reactions by others, thus it reflects recognition of interdependence. Thishighlights that inter-organisation behaviour can also increase the complexity of a supply system(e.g. interconnectedness between actors).Over the years the research and practice of supply chain management has grown in meaningthrough what Harland et al., (1999) describes as an externalisation beyond the boundary of theorganisation. Traditionally purchasing and supply management has been viewed as a firm-basedset of activities dealing with transactions between customer and supplier relationships (Baily andFarmer, 1985). Later work in the 1980s attempted to elevate the purchasing function from beingconsidered operational and clerical, to a strategic level (e.g. Spekman, 1981; Caddick and Dale,1987). A supply strategy involves more than just material, transaction and information flow, itshould take more of a holistic approach to managing the entire supply network (Harland et al,1999). Harland et al., (1999) further points out that this would include aspects such asinterrelationships between organisational roles, network configurations, governance, integrationand collaboration.As part of developing a supply strategy an organisation will adopt and implement one or many ofthe various supply chain improvement options, within the boundaries of the organisation andbetween suppliers and customers within the supply network. A supply problem is therefore made 31
    • up of these selected supply chain improvements, to achieve a supply chain objective, within thesupply setting that is specific to the actual organisation undertaking a study. Evaluating supplyproblems is inherently complex and presents challenges in terms of the scope and level of detailin which they should be analysed (Albores, et al., 2006; Weaver, et al., 2006). Owing to, forexample, a great variety of policies, conflicting objectives, and the inherent uncertainty of thebusiness environment, this is not an easy task (Alfieri & Brandimarte, 1997).2.4 Role of simulation to evaluate supply chain problemsSimulation has often been cited as a method that could present the greatest potential in studyingsupply chain as its complexity obstructs analytical evaluation (e.g. Ridall et al., 2000, Huang et al.,2003, Van der Zee and Van der Vorst, 2005). It is often regarded as the proper means forsupporting decision making on supply chain design (Van der Zee and Van der Vorst, 2005). Onereason for this is that it may be used to support the quantification of the benefits resulting fromsupply chain management (Kleijnen, 2005).A simulation model is a representation of the system of interest, used to investigate possibleimprovements in the real system, or to discover the effect of different policies on that system(Pidd, 1998). In this context, the system is a supply chain or network and simulation is used toevaluate the impact of different sets of supply chain improvements on the potential performanceof that system within its supply setting.The benefits of using simulation as a means to evaluate supply chain problems have often beencited. These include that simulation is the only approach that can holistically model the supplychain (Tang, Nelson, Benton, Love, Albores, Ball, MacBryde, Boughton and Drake, 2004) and canhandle stochastic properties (Hae Lee, Cho, Kim and Kim, 2002; Persson and Olhager, 2002). Thisis because it can be used to understand the overall supply chain process and characteristics usinggraphics/animation (i.e. model elements and relationships), able to capture system dynamics andfacilitate decision-making by minimising the risk of making changes without fully understand theimpact of various alternatives on performance (Chang and Makatsoris, 2001; Van der Zee and Vander Vorst, 2005). For instance, simulation is good for modelling the impact of variation such asforecast error, supplier reliability and quality variance (Biswas and Narahari, 2004). A classicexample of understanding the effect of dynamic behaviour (e.g. process delays, lead times,planning policies) in the amplification of demand signal, often known as the ‘bullwhip effect’ firstdescribed by Forrester (1961). 32
    • 2.4.1 Range of approaches used in simulationThe range of approaches used in supply chain simulation is overwhelming. Van der Zee and Vander Vorst (2005) point out that in the past decade, a large number of simulation tools for supplychain analysis have been developed internally (e.g. CSCAT in Ingalls and Kasales, 1999),commercially (e.g. e-SCOR in Barnett and Miller, 2000; Albores et al., 2006), or concernapplications of general-purpose simulation languages (e.g. Arena in both Kelton, Sadowski andSadowski, 1998 and in Persson and Araldi, 2009).There are a number of classifications of both modelling and simulation approaches suggested inthe SCM literature (e.g. Hicks, 1997; Beamon, 1998; Min and Zhou, 2002; Kim, Tannock, Byrne,Cao and Er, 2004; Kleijnen, 2005; Weaver et al., 2006; Owen et al., 2008). Min and Zhou (2002)present a detailed taxonomy of modelling and simulation techniques building upon previous workby Beamon (1998). Kim et al., (2004) used Min and Zhou’s (2002) taxonomy to review techniquesfor modelling supply chain in an extended enterprise, although they focused upon supply chainmanagement software and how these might be selected.A study by Kleijnen (2005) provides a more specific survey of supply chain simulation tools andtechniques and a discussion of some methodological issues. Kleijnen (2005) found that there arefour main simulation types for supply chain management: spreadsheet simulation, systemdynamics, discrete-event and business games. On the other hand a discussion by Owen, et al.,(2008) did not include spreadsheet simulation or business games but detailed how agent basedmodelling is an emerging approach for evaluating supply chain problems. In more recent years,new tools and techniques have been made available commercially largely due to the rise inpopularity of the SCOR process reference model. These have predominantly focused upon DES(e.g. Gensym eSCOR; see Barnett and Miller, 2000; Albores et al., 2006; Persson and Araldi, 2009)and adding simulation capabilities to existing static process modelling enterprise managementsuites (e.g. Mote Carlo capabilities in Proforma and Aris process enterprise modelling suites; seePoluha, 2007).In relation to DES tools, these can be distinguished into identifiable classes that include process,enterprise, manufacturing and supply chain specific simulation tools or techniques. Albores et al.,(2006) and Weaver et al., (2007) showed that each of these classes have different competenceswhen evaluating supply chain problems. It is important to distinguish these as tools specific tosupply chain management are emerging, while a lot of research is conducted using existingprocess (e.g. Process 2000 used in Benton, 2009), enterprise (e.g. suggested by Tang et al., 2004) 33
    • and manufacturing led packages (e.g. Witness used in Albores et al, 2006; Arena in Persson andAraldi, 2009) which have been established for many years.Figure 2.1 presents a classification of the different simulation approaches in light of the abovediscussion. Supply chain simulation approaches Multi-agent based Spreadsheet System dynamics Discrete-event simulation Business games simulation (SD) (DES) modelling Business process Manufacturing Supply chain wide Enterprise wide DES wide DES DES DESFigure 2.1 Classification of supply chain simulation approachesSource: Synthesised and extended from past contributions by Beamon (1998); Min and Zhou (2002) and Kleijnen (2005); Albores et al.,2006; Weaver et al., 2007; Owen et al., (2008)2.4.2 Extent and usage of simulation for researchThe amount of research to evaluate supply problems using simulation approaches is great. It isevident that simulation is not only a useful tool for evaluating supply problems, but has beenextensively used in the literature for research purposes. Table 2.2 lists each of the approachesidentified in section 2.3.3 and shows representative examples of the approaches being usedspecifically for SCM applications. The majority of examples that could be identified used discrete-event approaches, followed by system dynamic and some recent examples of multi-agent basedmodelling. The approaches are described in this section in the context of how they have beenused to analyse supply problems. 34
    • Table 2.2 Classification of simulation approaches Simulation Approach Use in SCM research Spreadsheet simulation Sounderpandian, 1989; Chwif, Barretto, Saliby, 2002; Disney and Towill, (2003a; 2003b) Forrester, 1961; Towill, 1996a; 1996b; Ashayeri and Keij, 1998; Beamon, 1998; Angerhofer and System dynamics (SD) Angelidis, 2000; Van der Pol and Akkermans, 2000; Otto and Kotzab, 2003; Spengler and Schroter, 2003; Ge, Yang, Proudlove, Spring 2004; Higuchi and Troutt, 2004; Fiala, 2005 Swaminathan, Smith, Sadeh, 1998; Gjerdrum, Shah, Papageorgiou, 2001; Lau, Wong, Pun, Chin, Multi-agent based modelling 2003; Cavalieri, Cesarotti, Introna, 2003; Lou, Zhou, Chen, Ai, 2004; Van der Zee, Van der Vorst, (MABM) 2005; Datta, Christopher, Allen, 2007. Business Tumay, 1995; Chang and Makatsoris, 2001; Albores, Ball and MacBryde, 2002; Ball, Albores and process DES Macbryde, 2004; Reiner, 2005; Melao and Pidd, 2006. Some supply chain problems have been evaluated using manufacturing-wide DES a review by Manufacturing Albores et al., (2006) considers their functionality. Other sources include: Lee and Lau, 1999); wide DES Ingalls and Kasales, 1999; Miller and Pegden, 2000; Taylor, Robinson and Ladbrook, 2003; Greasley, 2006. Discrete- Towill, 1991; Towill, Naim, Wikner, 1992; Wikner, Towill, Naim, 1991; Bhaskaran,1998; Petrovic, event Roy and Petrovic, 1998; Chang and Makatsoris, 2001; Petrovic, 2001); Persson and Olhager, simulation 2002; Huang and Gangopaghyay, 2004; Chan and Chan, 2005; Holweg, et al., 2005; Manzini et (DES) Supply chain al., 2005; Truong and Azadivar, 2005; van der Zee and van der Vorst, 2005; Bandinelli et al., wide DES 2006; Iannoni and Morabito, 2006. Some applications specifically use SCOR e.g. Barnett and Miller, 2000, Hermann, Lin, Pundoor, 2003; Persson and Araldi, 2009. Albores et al., 2006 includes some detail to draw logical conclusions. Enterprise wide The merits of developing enterprise DES was discussed in Tang et al, 2004 but it was difficult to DES find examples in a supply chain context. Kleijnen, 1980; Kleijnen and Smits, 2003; strategic games i.e. beer game (e.g. Sterman, 2000; Business games Sodhi, 2001; Simchi-Levi, Kaminsky and Simchi-Levi, 2003; operational games (e.g. Riis, Smeds and Landeghem, 2000)2.4.2.1 Spreadsheet simulationSpreadsheets are often too simple and unrealistic (Kleijnen, 2005) however they are widely usedfor corporate modelling (Plane, 1997; Powell, 1997). Smith (2003) analysed a number of supplychain scenarios showing that spreadsheets can typically be used for static, average analysis tocompute basic time varying conditions. Disney and Towill (2003a; 2003b) have used aspreadsheet to model vendor managed inventory (VMI) in a supply chain. To improve thecapability of spreadsheet simulation linear and non-linear optimisers as well as Monte Carlosimulation add-ins (e.g. @Risk and Crystal ball) have been developed (Powell, 1997).2.4.2.2 System dynamics (SD)System dynamics (SD) is another approach to model supply chains and achieve significantperformance improvement. It is an approach that is holistic and can accommodate the real world(Towill, 1996b). It focuses upon the way in which system structures affect system behaviour at amore macroscopic level, so it is less concerned with detail unlike DES (Pidd, 2004a; 2004b). SDviews companies as a system with six types of flows (materials, goods, personnel, money, ordersand information; output flows such as fill rates and average WIP and stocks (e.g. WIP at a givenpoint of time). SD assumes that managerial control is realised through the changing of statevariables (e.g. production and sales rates), which change flows, and hence stocks (Kleijnen, 2005).The fundamental assumption in system dynamics is that behaviour is a result of structures – both 35
    • inside and in its environment (Pidd, 2004a; 2004b). This includes the feedback loops and delaysthat are present in the system being observed.It was Forrester (1961) who developed industrial dynamics, which he later coined ‘systemdynamics’. He studied a four link supply chain which included a retailer, wholesaler, distributorand factory. His study examined how the links within the supply chain react to deviations betweenactual and target inventories. Forrester (1961) found ‘common sense’ strategies may amplifyfluctuations in the demand of final customers, up in the supply chain. Further work has focusedon identifying this amplification as one of the bullwhip effects (e.g. Lee et al., 1997a; 1997b;Disney and Towill, 2003a; 2003b). Other examples include supply chain re-engineering (Berry etal, 1994; Towill, 1996a), information sharing (e.g. Fiala, 2005), time compression (Towill, 1996b)and demand volatility in the supply chain (Anderson, Fine and Parker, 2000).2.4.2.3 Business gamesBusiness games are used to allow users (e.g. managers, students etc.,) to operate a simulated‘real’ world system in its environment. Business games can be either strategic in nature or focuson operational issues (Kleijnen, 2005). One of the advantages of business games is that humanbehaviour (i.e. the decisions made by a user) can be evaluated on the impact of the simulatedsystem. Games have been used widely for educational purposes including in OM (e.g. Riis et al.,2000) and SCM (e.g. Sterman, 1992; Anderson and Morrice, 2000; Kaminsky and Simchi-Levi,1998) and for research purposes has focused primarily on the Beer Game (e.g. Mason-Jones andTowill, 1997; Kimbrough, Wu and Zhong, 2002; Hieber and Hartel, 2003), which is one case usedto develop and refine the SCM2.2.4.2.4 Multi-agent based modeling (MABM)Multi-agent based modelling (MABM) is an emerging tool, especially when compared to the useof SD and DES models. This may be due to a lack of a consistent set of definitions for key conceptssuch as what an agent actually is, as well as a philosophy of its application (Schieritz and Milling,2003; Borschev and Filipov, 2004; Owen et al., 2008). MABM models systems comprised ofautonomous, interacting agents such as agent behaviour in supply chains (Macal et al., 2005).Swaminathan et al., (1998) identify different agents (i.e. manufacturers, transportation, suppliers,distribution centres, retailers and end-customers) in a supply chain context and provide eachagent with an ability to use a subset of control elements. The control elements are used to helpin decision making at the agent level by utilising various policies (e.g. inventory, just-in-timerelease and routing algorithms) for demand, supply, information and materials control within thesupply chain (Swaminathan et al., 1998). Applications of MABM include examining the behaviour 36
    • of entities in the system and their relationships such as studying the dynamics of supply chaincompetition (e.g. Akkermans, 2001; Allwood and Lee, 2005) and product allocation andscheduling (Kaihara, 2003).2.4.2.5 Discrete event simulation (DES)Discrete-event simulation is one of the most popular modelling techniques (Robinson, 2005). Itemploys a next-event technique to control the behaviour of the model (Pidd, 2004a; 2004b). TheDES method has been used for over 50 years, with an array of software packages and successfulapplications reported (Taylor and Robinson, 2006). Kleinjen (2005) notes that DES has twodistinct characteristics: it represents individual events (e.g. arrival of an individual customer order)and it incorporates uncertainties (e.g. customer orders arrive at random points in time). SD onthe other hand has a more aggregated view including flows and most SD models do not haverandomness, yet SD models behaviour remains counter-intuitive because of the non-linearfeedback loops (Kleinjen, 2005). Owen et al., (2008) adds that the most fundamental differencebetween SD and DES is the treatment of time, which is continuous in SD and discrete in DES.Banks, Buckley, Jain, Lendermann, (2002) surveyed a number of simulation studies in SCMconducted at IBM and Virtual Logistics. They discuss both strategic and operational uses of DES,distributed SCM simulation and commercial packages for SCM simulation. A more comprehensiveliterature review of applications of DES in a supply chain context has been discussed by Terzi andCavalieri (2004). They find that DES has been applied across a range of objectives including supplynetwork design, strategic decision support and analysis of supply chain processes.Table 2.2 demonstrates that DES in a supply chain context have used business process DES; usingexisting manufacturing-wide DES and specific supply chain wide tools and techniques.Additionally an enterprise simulator which includes true financial evaluation across the supplychain has been discussed in the literature (e.g. Tang et al., 2004; Ball et al., 2008). Ball et al.,(2004) suggested the most comprehensive approaches currently available are domain specific;these include those based on SCOR (e.g. Gensym eSCOR in Barnett and Miller, 2000; Persson andAraldi, 2009) and the IBM Supply Chain Analyser (Bagchi, Buckley, Ettl and Lin, 1998).2.5 Role of conceptual modelling in simulation projectsThis section considers the role of conceptual modelling in simulation projects, which is theparticular focus of this thesis. The previous discussions have demonstrated that evaluating supplyproblems is important and argued that one approach which is used extensively to evaluate their 37
    • potential includes simulation. Even outside of the domain of SCM, simulation is used widely and alot has been written on how to undertake a simulation project including its stages, someapproaches and practices have been suggested. One particular stage that is fundamental in allsimulation projects but little is written on the subject, involves the process of conceptualmodelling. With all the research and extensive use of simulation it is surprising that conceptualmodelling as an area of study has until recently not been researched in much detail. This sectionconsiders the following arguments: Importance of conceptual modelling in a simulation project (section 2.5.1) Key debates in conceptual modelling that could be addressed by research (section 2.5.2) Definition of what constitutes a conceptual model for SCM applications (section 2.5.3)2.5.1 Importance of conceptual modelling in a simulation projectA simulation project is a process of interpretive, developmental, and analytical steps (Pritsker,Sigal, and Hammesfahr, 1989; Law and Kelton, 1991; Musselman, 1994; Banks, Carson, Nelsonand Nicol, 2005). Musselman (1994) notes that these steps, which are intrinsic to all simulationprojects, generally include problem formulation, model conceptualisation, data collection, modelbuilding, verification, validation, analysis, documentation and implementation.Conceptual modelling is one step of a simulation project, which sits between the problem hasbeen formulated and the development of the computer model. The conceptual model is derivedfrom an understanding of the problem situation in the real world (Robinson, 2008a). It is anabstraction of the real-world system under investigation that concerns the relationships betweenthe components and structure of the system (Banks, 1999). It is from this description that acomputer model can be coded and built.Proper development of the conceptual model is critical to the success of a simulation project(Pace, 2000a; 2000b). This is because the explicit statements of assumptions and detaileddescriptions of the relationships included in the conceptual model ensure that the modeldevelops in accordance with the problem statement (Manuj, Mentzer and Bowers, 2009). Inaddition to this the documentation provided from the conceptual model stage can be used tovalidate that the problem is “reasonable” for the intended purpose of the model (Sargent, 2005;2008).2.5.2 Key debates around the nature of conceptual modellingIn general, the notion of conceptual modelling, as expressed in the simulation and modellingliterature is ‘vague and ill-defined, with varying interpretations as to its meaning’ (Robinson, 38
    • 2008a, pg. 280). Robinson (2008a) does however attempt to identify the key facets of conceptualmodelling, but notes that there is no complete agreement, these include: Conceptual modelling is about moving from a problem situation, through model requirements to a definition of what is going to be modelled and how Conceptual modelling is iterative and repetitive, with the model being continually revised throughout a modelling study The conceptual model is a simplified representation of the real system The conceptual model is independent of the model code or software (while model design includes both the conceptual model and the design of the code) (first cited in Fishwick, 1995) The perspective of the client and the modeller are both important in conceptual modelingThere is little agreement and not much discussion on defining a conceptual model. There ishowever a widespread understanding that all simulation models are simplifications of reality(Zeigler, 1976). It is at the conceptual modeling stage that the issue of how to abstract anappropriate simplification of reality is made (Pidd, 2003). More specifically, Pace (2000a; 2000b)states a conceptual model as a simulation developer’s way of translating modelling requirements(what is to be represented by the simulation) into a detailed design framework (how it is to bedone), from which the software specific requirements (e.g. hardware, systems/equipments) thatwill make up the simulation can be built. This presents a key question, of what is a definition of aconceptual model in the context of evaluating supply chain problems (addressed in section 2.5.3).There is agreement that the early stages of a modelling study are not just visited once (e.g. Balci,1994; Willemain, 1995). It is an iterative process that is not only continuously revised during theconceptual modelling phase but also in the life-cycle of the simulation project. Robinson (2008a;2008b) supports this view by suggesting that conceptual modelling is not a one-off process, butone that is repeated and refined a number of times during a simulation study. This presents twoissues: firstly, when to exit from the conceptual modelling stage and, secondly revising theconceptual model during the implementation of the computer model. These issues have notbeen discussed in the literature (except descriptions of the qualities for a conceptual model) butare critical to a conceptual modelling methodology. For instance a conceptual model shouldminimise the risk of building an inaccurate and non-credible computer model. Therefore,iteration is fundamental in the conceptual modelling stage and, although iteration may benecessary in later stages, it is less desirable and may demonstrate weaknesses in the conceptualmodel described, or even, how it was created. 39
    • Another key facet includes the independence of the modelling requirement from one that isskewed by the software requirements and capabilities. Once the conceptual model is defined,Fishwick (1995) suggests it can then be refined into a more concrete executable model.Therefore, the conceptual model is separate from model execution and, as Robinson (2008a)highlights, is not concerned with how the computer-based model is coded. The modeller uses theconceptual model to detail how it is to be implemented (Pace, 2000a; 2000b) with a particularmodelling approach (e.g. DEDS, SD) or, more specifically, tool or technique (e.g. Simul8, Process2000). This is an important consideration as a simulation approach or even a tool or techniquemay shape and affect the way in which a supply problem is to be represented. This is counterintuitive to the aim of a conceptual model that represents actual practice in the simplest and mostaccurate way.The last consideration presented by Robinson (2008a) is how both the perspective of the modellerand client needs to be reflected in the conceptual model. The end result is an agreementbetween the modeller and the client of what the simulation model needs to include and do inorder to accurately represent the supply problem in the most suitable way. In the process themodeller needs to extract from the client the actual practices in the ‘real system’ and determinehow they are to be modelled. This interpretation is a challenge and will require a series ofiterations based upon interactions between the client and the modeller. The relationship and rolebetween the client and modeller has not been widely discussed (except in Robinson, 2008a) andthe role of domain specific knowledge needed in the process of conceptual modelling. This isaddressed in later chapters explicitly as a key opportunity in the developing of a SCM2.2.5.3 Defining conceptual modelling for supply chain problemsThe key debates surrounding the nature of conceptual modelling provide some insights into whatshould be included in the process of conceptual modelling and how a conceptual model should bedescribed. It is useful to consider what constitutes a conceptual model before defining what ismeant by the process of creating a conceptual model. The reason for this is that discussions haveconsidered the former while the process is dependent upon what information it needs to provide.Three of the key debates in conceptual modelling noted by Robinson (2008a) are important to anunderstanding of what constitutes a conceptual model. The conceptual model is a simplifiedrepresentation of the real system; independent of any software-requirements and is viewed fromboth the client and modellers perspective. Robinson (2004b, pg. 65) has to some extentaddressed these issues in a definition for a conceptual model. This was later reinforced in 40
    • Robinson (2008a, pg. 282) with a detailed examination of recent definitions and requirements inconceptual modelling: a non-software specific description of the simulation model that is to be developed, describing the objectives, inputs, outputs, content, assumptions and simplifications of the model’.There are two considerations that are not necessarily addressed in Robinson’s (2004b; 2008a)definition. The first consideration has previously been pointed out by Haddix (2001), that a keycontributory factor that adds to the confusion over defining a conceptual model is whether theconceptual model is an artefact of the user or the designer. The other includes the alternativeway in which the content of a model can be described. Robinson’s definition is deliberatelygeneral and not biased to a particular worldview or application domain. However, the debatesand challenges for conceptual modelling are hugely dependent upon different modeller’sworldviews and application domain particularly when describing the content of the model.In relation to the first consideration, Robinson (2008a) does consider two different types ofconceptual model: 1. domain-orientated model - provides a detailed representation of the problem domain (actual practice) 2. design-orientated model - describes in detail the requirements of the model, which is used to design the model code (modelling practice)The two different types of conceptual models present a gap between the real worldunderstanding of the subject matter experts (SME’s) and the modeller(s) who design and build thecomputer simulation model. The domain-orientated model describes the problem as it exists inthe real world. Usually this is defined from the perspective of the client and extracted byinterviewing subject matter experts who are knowledgeable about the real system being studied.Another view is that a process reference model could be used to describe a supply chain; this ideais one of the central themes developed in this thesis. Taking a Supply Chain Council SCOR viewthis would include a description of the business processes and activities; relationships betweenprocesses and the practices adopted in one or more process.The content of the model (second consideration) can be linked to how the design-orientatedmodel is described. It is when the components of the model that represent the real worldproblem are described that alternative perspectives exist. This can be seen in Pace’s (2000a, pg.329) definition of a conceptual model: 41
    • ‘Describes how the simulation developer understands what is to be represented by the simulation (entities, actions, tasks, process, interactions, etc.,) and how the representation will satisfy simulation requirements’.There might be alternative perspectives of how to describe the content of the model but standardterminology has been described for particular worldviews or even simulation approaches. Forexample, Pidd (2004a, pp. 63 – 66) discusses the standard terminology used in DES. This includesthe labels for the objects that constitute a system to be simulated (e.g. entities, resources);definition of operation in which these objects engage over time (e.g. events, activities andprocesses). In an SD approach, the objects and operations concern stocks, flows, casual loops anddelays while in agents based modelling these include agents, rules and state charts (Owen et al.,2008).There is even less discussion in the literature on how a conceptual model is created. The first keyfacet considered by Robinson (2008a) considered the need to move from problem situation,through model requirements to a definition of what is going to be modelled and that it is aniterative process. This is the closest Robinson gets to a definition for conceptual modellingalthough in Robinson (2004b; 2008a) a framework is presented. Pace (2000a, pg. 329) does offer adefinition for conceptual modelling: ‘Collection of information that describes the simulation developer’s concept about the simulation and its pieces (e.g. assumptions, algorithms, characteristics, relationships and data)’The definition offered by Pace (2000a) leans towards the design-orientated model perspective. Ittakes a view that the problem can be extracted from the real world from the perspective of themodeller (i.e. the modeller makes judgments about the content of the model and howassumptions and simplifications can be incorporated into the model). In Pace’s (2000a) view thiswould concern the relationships in the model, or more specifically the components in the modeland their interconnections. This has two dimensions, which are familiar and well understoodconcepts in the simulation literature (e.g. Brooks and Tobias, 1999; Chwif, Barretto and Paul,2000; Carson, 2004, Law, 2008, as previously published in 2005 and 2006): The scope of the model: The model boundary or the breath of the real system that is to be included in the model (e.g. Law, 2008) The level of detail: The detail to be included for each component in the model’s scopeThe model content is determined, in part, by the inputs and outputs, in that the model must beable to accept and interpret the inputs and to provide the required outputs (Robinson, 2008a). 42
    • The inputs refer to the elements of the model that can be altered to effect an improvement in, orbetter understanding of, the real world. In a SCM context these relate to the characteristics ofthe improvements selected to improve supply chain performance. The outputs therefore are thespecific supply chain measures that can be used to evaluate the performance of theimprovements being observed.The distinct challenge is the ‘conversion’ between the real world in which the supply problemexists (domain-orientated view) and the way in which the modeller wishes to represent the modelcontent (design-orientated view). This includes understanding the problem in enough detail andextracting information from the clients so that the model content can be derived. This is perhapswhy conceptual modelling is often regarded as an ‘art’. It is this conversion that presents thegreatest difficulty in conceptual modelling which would benefit greatly from approaches thatcould help create a conceptual model. These approaches would be hugely dependent upon therequirements for SCM applications. There is also another opportunity that is argued explicitly inchapter six that the process of conceptual modelling could be made more efficient and focused byutilising existing domain knowledge. This is required to formulate the real world problem fromthe perspective of the client and to use standard terminology adopted in SCM practice.2.6 Understanding of CM for SCM simulation applicationsThis section discusses the general understanding of simulation conceptual modelling for SCMapplications. It argues that conceptual modelling is little understood in the literature and evenwhen applied in practice. Particularly within the domain of SCM management there are noguidelines that could aid the participants through the complexity of creating a simulationconceptual model. The section contributes to demonstrating that a gap exists for a methodologythat can provide some guidelines that can be followed specifically for SCM applications. This isdeveloped by considering: General issues in understanding of conceptual modelling in practice (section 2.6.1) Application of the process of conceptual modelling in practice for SCM applications (section 2.6.2)2.6.1 General issues in understanding of conceptual modellingThe extent to which conceptual modelling is understood and conducted is difficult to ascertain.Robinson (2004a; 2004b) who notes that when reviewing previous simulation related conferencesand papers that have used a simulation approach over the previous four decades they did notprovide much description of the conceptual model or how one might be created. In particular 43
    • there is little written justification of the design choices made when formulating the scope anddetail necessary to model a specific supply problem and examples of conceptual models. Threereasons might be attributed to these issues: 1. Conceptual modelling is seen as more of an ‘art’ than a ‘science’ (e.g. Shannon, 1975, 1992; Kleijnen, 1995; Anderson, Richardson and Vennix, 1997; Schmeiser, 2001; Robinson, 2004b; 2006a; 2006b; Banks et al., 2005) 2. Novices devote little time to the conceptual modelling stage, while experts draw upon prior experience build up over a period of time (Wang and Brooks, 2007a) 3. Little guidance offered in texts and research papers on how to create a simulation conceptual model (Robinson, 2004b)The first reason presents a difficultly in defining methods and procedures for the creation of aconceptual model (Robinson, 2006a; 2006b). Schmeiser (2001) makes this point by comparingmodel conceptualisation to the analysis of the model stage which he contends is more of ascience, therefore easier both to teach and to undertake. Conceptual modelling requires specificskills that expert modellers have gained over a period of time. Identifying a greaterunderstanding and some more formal methods for conceptual modelling would have somesignificant benefits (Robinson, 2006a; 2006b). For instance it could enable novices to gain theseskills quicker, averting some modelling failures. Additionally, experts will gain from having a moreformal process for guiding them through the process of conceptual modelling, relying less onhopeful intuition and more on guided practice.There are some studies that review the simulation practices between novices and experts (e.g.Willemain, 1995; Powell and Willemain, 2007; Wang and Brooks 2007a). Each study foundconsiderable differences to the approaches to conceptual modelling and in particular theconceptual modelling stage. Willemain (1995) found that experts would tend to develop an aspectof the conceptual model, then evaluate it and then often revise the conceptual model based onthis evaluation. Powell and Willemain (2007) later found that novices often fall short of what theyconsidered good modelling practice due to over-reliance on data, taking shortcuts, insufficient useof variables and relationships, ineffective self-regulation and over-use of brainstorming. Wangand Brooks’ (2007a) study reinforced these points and surprisingly, contends that novices lackedconsideration of the conceptual model. On the other hand experts devoted much more time tothe step and revisited the conceptual model as knowledge improved during the project. Ward(1989) suggested that what sets truly successful modellers apart is their effectiveness in 44
    • conceptual modelling. The expert modellers were able to draw upon their experiences whichincreased the success of the simulation study meeting objectives and time scales.There is little guidance offered on conceptual modelling in both simulation texts and the wideracademic literature. Robinson (2004a; 2004b) points out that conceptual modelling is probablythe least understood aspect of simulation modelling, with very little written on the subject. Wangand Brooks (2007a) support this view by suggesting there is a lack of empirical studies or data inthe literature on how modellers develop conceptual models and on how conceptual modellingrelates to other modelling topics. Simulation texts show very little discussion on conceptualmodelling (e.g. Greasley, 2004; Pidd, 2004a; Banks et al., 2005) except in Robinson (2004b) whodevotes two specific chapters to conceptual modelling. This is also the case in the literature,where some papers exist of successful simulation projects as a whole (e.g. Law and McCormas,1991; Musselman, 1994; Nordgren, 1995) with some discussion of conceptual modelling.Recently, there has been an attempt to provide some guiding principles and examples ofconceptual modelling most notably after two specific tracks dedicated to conceptual modelling atthe Operational Research Society Simulation Workshop in 2006.2.6.2 Application of the process of conceptual modelling for SCM problemsThe issues surrounding a lack of understanding of conceptual modelling in general arecompounded by the actuality that there is little evidence of the application of the process ofconceptual model for SCM applications. This does not mean that a conceptual model has notbeen described prior to building the computer model. Moreover, they have not been presentedin great deal in research publications. The relevance of a greater understanding of conceptualmodelling is more prevalent due to the nature and complexity of evaluating supply chainproblems which has previously been argued (see section 2.3).Manuj et al., (2009) discuss how to improve the rigour of discrete-event simulation in logistics andsupply chain research. Although, this is in the context of DES, they offer an eight-step process forgeneral use in the design and execution of rigorous simulation work building upon contributionsby Banks (1998) and Law (2008). The review demonstrated that only a few of the eight stepswere covered adequately and specifically highlighted that the conceptual modelling stage hadinadequate coverage and been neglected (i.e. not reported or not sufficiently addressed). Inparticular, Manuj et al., (2009) raised the issue of the critical step of validating a conceptualmodel, suggesting that there is little evidence in the logistics and supply chain literature, both inthe studies they examined in detail, and those that were excluded. It was also difficult to find 45
    • evidence of much detail of conceptual models presented in the literature for the other simulationapproaches (e.g. SD, MABM).The sample of DES studies studied by Manuj et al., (2009) in SCM presented only one study whichhad documentation to show the completion of the conceptual modelling step. Appelqvist andGubi (2005) specified that their model was compared to actual supply chain performance andreviewed a structured walk-through with company management. However, Manuj et al., (2009)point out the conceptual validation and walk-through was done during simulation modelvalidation (at a later stage of the simulation project). More recently, Onggo, Gunal and Maden,(2008) were the only researchers to present a conceptual model of a supply chain problem(modelling a distribution warehouse) and James and Bhasi (2008) classified models for conceptualmodelling of logistic terminals. Three other examples can be found that focus on a leanmanufacturing problem (Van der Zee, Pool and Wijngaard, 2008), views of modellers on theconceptual modelling process (Wang and Brooks, 2008) and the complexity of simulationproblems (Heavey, 2008). It should be noted that the examples noted above have only recentlybeen published; little literature exists in this area prior to 2008.2.7 Usefulness of a CM methodology for SCM applicationsThere is a need for structured approaches that could guide the participants in a simulation projectthrough the process of creating a conceptual model for SCM applications. This would requirefollowing steps that could facilitate a good understanding of a complex supply problem so toformulate the modelling requirements and lead to a definition of the computer model to bedeveloped.A simulation conceptual modelling methodology for SCM applications would be useful in terms of: 1. Incorporating and building upon existing conceptual modelling practice, developed in the context of evaluating supply problems (addressed in chapter four) 2. Meeting the requirements for an effective conceptual model, the characteristics of a good methodology and address the specific needs of the SCM domain (addressed in chapter five) 3. Incorporating key concepts that address how the participants undertaking a conceptual modelling stage of a simulation project for SCM applications can be guided through the process of creating a conceptual model (addressed in chapter six) 46
    • Existing conceptual modelling practice can be viewed in light of the specific needs for creating aconceptual model for SCM applications. This would include understanding the approaches toconceptual modelling in practice (discussed in section 4.1) and incorporating these into a guide.In particular understand how the principles of conceptual modelling, methods of simplificationand a general process can be incorporated into a methodology. A methodology that is foundedupon existing conceptual modelling practice should minimise some of the general pitfalls thatcould be avoided in simulation modelling (identified in section 4.2). It would also include waysand means to communicate and represent the content of the conceptual model (identified insection 4.3). Another important aspect that could be incorporated into a methodology is how tovalidate a conceptual model (identified in section 4.4).A methodology could be developed that meets a specification that details the requirements thatit should include. This would include the creation of an effective model that is simple for thepurpose at hand and creates a conceptual model that is both valid and credible (identified insection 5.1). It addresses the need, of the good characteristics, of a methodology, that includes aprocedure that guides participants through the process of conceptual modelling with theappropriate information (identified in section 5.2). Additionally it would address and capture thespecific needs of conceptual modelling for evaluating a supply problem (identified in section 5.3).A methodology would incorporate some key concepts that are founded on existing practice andideas for improving and identifying ways on how to create a conceptual model for SCMapplications. These ideas are formed by bringing together some important issues in conceptualmodelling such as: Identify how the participants undertaking the conceptual modelling stage of a simulation project should be involved in the process of conceptual modelling (identified in section 6.1) - In particular understanding a domain requirements specific to the client’s perspective of the supply problem and converting this into a design-orientated model that expresses the perspective of the modeller. Identify a general process for creating the conceptual model (identified in section 6.2) - The key concepts identified for SCM applications can be incorporated into this process. Identify the domain needs for creating a conceptual model (identified in section 6.3) – Consider how the knowledge of the SCM domain can improve the process of conceptual modelling and the guidelines that should be followed to make them more detailed, focused and efficient. 47
    • 2.8 Benefits of developing a conceptual modelling methodology for SCM applicationsThe development of a conceptual modelling methodology for SCM applications will yieldsignificant benefits to both practitioners and researchers who use a simulation approach toevaluate supply problems. The primary benefit of completing the conceptual modelling stagesuccessfully is that it will improve the quality of the output from a simulation study (Manuj et al.,2009). This should lead to a model that is simple for the purpose at hand; both valid and credible.There are a host of secondary benefits that can be identified which are significant in their ownright. These can mainly be considered in light of Robinson’s (2006a; 2006b) discussion of the keyissues that should be addressed by the research community to further an understanding ofconceptual modelling and impact upon the practice of simulation modelling more generally. Hesuggests that research that addresses these issues will have substantial benefits to both noviceand expert modellers: help novice modellers obtain modelling skills more rapidly, thus averting some modelling failures experts will gain from having a more formal process guiding their modelling, relying less on hopeful intuition and more on guided practiceEach of the research issues suggested by Robinson (2006a; 2006b) can be considered in light ofthe purpose of this research project and what this research delivers: 1. Developing consensus over the definition of a conceptual model/conceptual modelling – Definition is offered for a conceptual model and the process of conceptual modelling for SCM applications 2. Identifying the requirements for a conceptual model – Identified specifically in the context of SCM applications 3. Development of methods for designing conceptual models including modelling principles, methods of simplification and modelling frameworks – Incorporated into the key concepts for conceptual modelling and aligned to a general process 4. Moving towards standard methods for representing and communicating a conceptual model – Templates are suggested for presenting information, using this information to undertake the necessary analysis and for documenting the conceptual model 5. Developing procedures for validation of a conceptual model – Incorporated into the process and at the final phase of the methodology 6. Investigating effective means for teaching the art of conceptual modelling – Provides a set of guidelines that could be used by both expert and novice users. More so, educate 48
    • novice modellers so that they can become more effective practitioners (Willemain and Powell, 2007)2.9 Chapter summaryThis chapter has discussed the research issues in conceptual modelling for SCM applications. Itdemonstrates that a gap exists for a methodology that utilises domain-knowledge combined witha procedure that can be followed to create a simulation conceptual model for SCM applications.It has been argued that the evaluation of supply problems is of critical importance to anorganisation seeking to identify ways to improve performance. The difficulty involved inidentifying and evaluating improvements is that a supply problem is inherently complex.Simulation is one such approach that can address the complexity of a supply problem and theextent of its use in both research and practice is great.One particular issue in the simulation literature is the need to develop further an understandingof conceptual modelling. This is deemed an important and critical stage of a simulation project.Little is written on conceptual modelling in general and particularly in the domain of SCM. Anarea that is underdeveloped and requires attention for research is that there is a distinct lack ofguidelines that could be followed to create an effective conceptual model. A methodology thatcan aid in the creation of a simulation conceptual model for SCM applications is one way thatcould help fill this gap. It will be useful as it could guide a modeller through a complex supplyproblem to identify how it could be modelled. Additionally it would yield significant benefits toboth research and practice (particularly expert and novice modellers) by addressing to a largeextent the research issues raised by Robinson (2006a; 2006b) and aid in the improvement of theoutput from a simulation study.The following chapter identifies, and justifies, a research methodological programme to realiseeach of the research objectives, and questions, so that an SCM2 can be developed and tested. 49
    • Chapter 3 Research programme for the development andpreliminary validation of the SCM2This chapter describes and justifies the research programme and methods used for thedevelopment and preliminary validation of the SCM2. This is normally called the ‘researchmethodology’ but the term ‘research programme’ is used to avoid confusion between themethodology to be developed and tested and the way in which the research has been conducted.The aim is to design a methodological approach, which addresses the research objectives andunderlying research questions in the most suitable way. The chapter is broken into the followingseven sections before summarising: Justification of methodological approach (section 3.1) – justified by considering the research methodological issues and debates regarding the development of business methodologies. It also identifies the stages necessary to address these issues in the context of the research problem. Research programme and methods (section 3.2) – The research methods adopted in each stage of the research programme are described with discussion on the choice and rationale considered Development and validation case applications (section 3.3) – Discusses issues surrounding the involvement of the researcher, consistency of the process and choices of existing cases to be studied Limitations of the research approach (section 3.4) – Identifies that further applications are required to be tested against the test criteria and the need to test the wider applicability of the methodology Validity and reliability of the research (section 3.5) – Some points are raised to ensure the results of the study are repeatable and the integrity of the conclusions Ethical considerations and issues (section 3.6) – To ensure the work meets with ethical standards for research3.1 Justification of methodological approachThis section reviews and justifies the methodological approach adopted to address the researchaims of this thesis. The end result is a five stage research methodological programme to developthe SCM2. This is identified and justified after considering issues in the following areas: Identification of existing approaches to develop a business methodology (section 3.1.1) – It is argued that no examples can be identified for the development of methodologies for conceptual modelling 50
    • Identification of key methodological issues that have been discussed for the development of the SCM2 (section 3.1.2) – A number of important considerations are identified to establish a strong conceptual base, the need for empirical and theory testing and to ensure the results are relevant to the practicing manager Examination of existing methodological approaches in the field that are related to the development of the SCM2 (section 3.1.3) – Identification from the general research methodology literature some of the key issues to address the issues for developing the business methodology Justification of five stage approach to design a SCM2 (section 3.1.4) - The overall approach includes the need to review existing conceptual modelling practice, form the specification and outline design, refinement of the design and preliminary validation is described and justified.3.1.1 Methodological approaches for the development of methodologiesThere are no guidelines available on research approaches for the development of a conceptualmodelling methodology because it is a novel area for research. Previous discussions ofapproaches that would be useful in the conceptual modelling stage have, on the whole, beendeveloped from experience with very little testing. The most detailed and comprehensiveguidance for conceptual modelling has been presented in Robinson’s (2004b) book entitled‘Simulation: The practice of model development and Use’. The guidelines are based upon theauthor’s experience of nearly 20 years, with developing and using simulation models ofoperations systems, mainly manufacturing and service systems (Robinson, 2008b). Theframework has not undergone any claimed testing, although recently in Robinson (2008b) oneexample (i.e. Ford Motor Company) is used to illustrate the framework but this has not beenpresented as a formal validation. All that is claimed is that the framework would be a usefulapproach to conceptual modelling in general.The research project aims to create scientific knowledge by developing a methodology thatcombines domain knowledge with a procedure to be followed. Therefore, emphasis is placedupon theory building rather than theory testing. The scientific theory building process has beendiscussed in the OM literature (e.g. Meredith, 1993, Handfield and Melnyk, 1998, Voss et al.,2002) but not in the context of developing methodologies. Voss et al., (2002) points out the mainaim of theory building research is to: ‘Identify/describe key variables, identify linkages betweenvariables and identify why these relationships exist’. This is achieved by following the normal cycleof research that moves from description to explanation to testing with continuing iterationthrough the cycle (Meredith, 1993). This thesis does not attempt to test the methodology 51
    • developed. However, confidence in the methodology is improved through a process ofrefinement and preliminary validation. Future work is suggested to iterate between explanationand testing of the methodology with actual users, primary data, and researcher observation untileventual theory can be said to have been developed.3.1.2 Key methodological issues in the area of developing a methodologyThere have been a host of different methodologies developed in the OM and SCM literature ingeneral but little has been written on how one can be developed. One notable exception is Platts(1993) who discussed three shortcomings of existing contributions when considering researchmethodological issues for developing a process framework for formulating and implementing amanufacturing strategy. These three issues are also important considerations for developing aSCM2. These include: 1. Poor conceptual base 2. Low levels of empirical and theory testing 3. Relevance of the results to the practicing managerThe first issue has received attention in the SCM literature in general, noting the need towardsmore theoretical development and discussion required (Croom et al., 2000; Ho, Au and Newton,2002; Harland et al, 2006). Baines (1994) discussed the need for a strong conceptual base as itprovides a foundation on which further work can be built and future contributionscompared. Baines also cites De Bono (1992) in support, who suggested that ignoring existingpractice may have an influence on the originality of the work but it is of high risk if the work is toproceed in a logical manner. A conceptual base can be established by identifying and analysingexisting practice and forming a set of requirements that the SCM2 must meet at the design stage.The second issue has received attention in the SCM literature from Croom et al., (2002, pg. 75)who adds that the ‘inductive-deductive dichotomy is best addressed through constant reflectionof empirical against theoretical studies’. This can be remedied by having a strong theoretical baseand applying the methodology to empirical cases. Testing can be regarded as a separate issue,once the methodology has been designed, developed and refined. It was previously noted thatRobinson’s (2004) process framework has undergone no formal testing. Although as Hill (1987)points out, testing is important and improves the rigour of the research. Iterative reflectionbetween a strong conceptual base (from theory) and empirical data would increase the validity ofthe findings but the reliability is dependent mainly upon the number of applications. 52
    • The third issue (usefulness to the practicing manager) is also relevant for the context of themethodology being developed. ‘Usefulness’ in this context is a methodology that aids a modellerin the creation of a conceptual model as part of a simulation project and for the benefit of theclient. Platts (1993) suggests that there are two research approaches that ensure the scientificrigour of work (internal validity), and relevance to organisations (external validity), these are ofparticular interest to the development of new theory. These include case study research (e.g.Voss et al., 2002) and action research (Couglan and Coghlan, 2002).3.1.3 General methodological issues for developing the SCM2This section considers the general research methodological issues for developing themethodology. Beech (2005) provides a useful map to discuss the various alternativeconsiderations when discussing the research design. This map includes: Ontology (section 3.1.3.1) – concern the nature of reality (Saunders et al., 2007) Epistemology (section 3.1.3.2) – general set of assumptions about the best ways of inquiring into the nature of the world (Easterby-Smith et al., 2004, pg. 31) Methodology (section 3.1.3.3) – combination of techniques used to enquire into a specific situation (Easterby-Smith et al., 2004, pg. 31) Techniques (section 3.1.3.4) – individual techniques for data collection, analysis etc., (Easterby-Smith et al., 2004, pg. 31).3.1.3.1 OntologyThere are two main ontological positions often used to describe differences that may influence aresearcher’s assumption about how social entities operate within the world. Bryman and Bell(2007, pg. 22 – 23) define these as: Objectivism (objective) – implies that social phenomena confront us as external facts that are beyond our reach or influence Constructionism (or subjective) – asserts that social phenomena and their meanings are continually being accomplished by social actors.The social phenomena being studied in this thesis is how a simulation model can be created forSCM applications. The research questions seek to identify the requirements for creating aconceptual model for SCM applications and developing a methodology that meets theserequirements. It can be argued that the social phenomena under study should be viewed from asubjective ontological position. The rationale for this is that conceptual models are producedthrough social interaction and involve a state of revision. The new knowledge created is formed 53
    • via participants in the simulation study taking part in a series of procedures that extract and refinethe information required. The conceptual model is therefore not external from the social actors;it is shaped by those actors.The implication of considering the ontological considerations is that the research method shouldseek to identify how a conceptual model is created for a given supply problem and by actorsundertaking the study. Both Baines (1995) and Platt’s (1993) can be said to have used subjectivepositions when developing their frameworks. The frameworks were developed by applying themto different types of problems and revising the procedure based upon new knowledge created.The involvement of the researcher in the process of studying the phenomena was noted as a keyissue when developing a framework or methodology (this is revisited and addressed in section3.3.1).3.1.3.2 EpistemologyThere is significant debate in both the SCM and OM literature between the different researchepistemological positions that could be adopted to realise the aims of this research project.Beech (2005) suggests four epistemological positions, two linked to an objective ontology(positivist and critical realist) and a further two linked to a subjective ontology (interpretivist andaction research). Generally, two of these approaches have been dominant in the social sciences:positivist and interpretative paradigms (Hussey and Hussey, 1997) which are commonly, but notexclusively, associated with quantitative (in the positivist paradigm) and qualitative (in theinterpretative paradigm) research. These can be defined as: Positivist - research entails the collection of data upon which to base generalisable propositions that can be tested (Pugh, 1983). With this approach the researcher is likely to use existing theory to develop hypothesises and test them so to further develop theory (Saunders et al., 2007). Critical realist - takes a middle view, a compromise which can combine the strengths and avoid the limitations of positivist and interpretivist paradigms (Ates, 2008). Interpretivist - research requires the researcher to grasp the subjective meaning of social action (Bryman and Bell, 2007). It recognises that reality is not objective, but rather a social construction, created within the minds of those individuals interacting (Lee and Lings, 2009). Action research - involves taking action and creating knowledge, or theory, about that action (Coughlan and Coghlan, 2002). 54
    • Both positivist and critical realist epistemological positions would not be suitable for this type ofresearch as they are associated with an objective ontology. There are a number of reasons why apositivist led study would be inappropriate. The key reason is that there is little existing theory onmethods for creating conceptual models, particularly in SCM, and no methodologies exist. Itwould not allow rich insights to understand how a conceptual model can be created. This cannotsimply be reduced to a series of law-like generalisations and the aim of the research is to producea methodology that can aid a user. Guba and Lincoln (1994) provide some other criticisms of apositivist approach that can be considered in light of developing a methodology. For instance, apositivist approach excludes meaning and purpose particularly understanding of how a humanmay create a conceptual model and excludes the discovery dimension in inquiry as the hypothesisis determined in advance leading to less creative input.Both interpretivist and action research epistemological positions fall into a subjective ontology.An interpretivist approach would allow a rich and ideographic description of experiences withintheir contexts (Lee and Lings, 2009). This is also true of an action research approach, however;the researcher would be a participant in the creation of the conceptual model and makeobservations to learn how it might be improved. An interpretivist researcher would notnecessarily seek to influence the creation of the conceptual model. They would look atorganisations in depth and generally appoint to extensive conversations, observations andsecondary data analysis such as company documents and reports in order to overcomegeneralisability critiques (Easterby-Smith et al., 2004, pg. 40). There are some issues aroundaction research being uncertain and sometimes an unstable activity (Coughlan and Coghlan,2002). The approach would require access to the participants undertaking a simulation conceptualmodelling project for SCM applications over a considerable length of time. An interpretivistapproach is preferred at this stage of researching into the creation of conceptual models. Themain reason being that data can be used to develop theory within different contexts, prior togaining access and observing participants or taking part in using the actual methodology.3.1.3.3 MethodologyMethodology concerns the combination of techniques used to enquire into a specific situation(Easterby-Smith et al., 2004). Beech (2005) suggests three alternative approaches that could betaken: Hypothetico-deductive – an approach that should formulate theory or an hypothesis to explain some results and use these to derive further predictions or statements that can be verified or falsified through testing 55
    • Inductive – an approach to the relationship between theory and research in which the former is generated out of the latter (Bryman and Bell, 2007) Co-operative inquiry – a way of working with other people who have similar concerns and interests to the researcher. This is used to make sense of the people’s world, develop new and creative ways of looking at things and learn how to act to change things (Heron and Reason, 2001).The hypothetico-deductive methodology can be discounted as it is generally applied within thepositivist paradigm. Likewise, co-operative inquiry is seen as an action type of research with highlevels of involvement by the researcher (Ates, 2008) which has already been discussed. Aninductive methodology is generally associated with an interpretivist epistemology.At the core of the research is the need to follow an inductive construction of theory fromobservations as part of the theory building process. This includes the identification of a set of keyconcepts that can be incorporated into a design for a SCM2 and additionally, developing aprocedure for addressing how a conceptual model can be created. This corresponds to drawingempirical generalisations by transferring ideas into understanding or laws. This is followed bycreating theories from empirical observations in what Weick (1989) calls a process of ‘disciplinedimagination’ (i.e. series of thought trails establishing conditions and imaginary outcomes inhypothetical situations). The theory in this instance is built by taking each of the empiricalgeneralisations to develop and refine a procedure that utilises domain specific knowledge. Thisrequires addressing ‘how’ the concepts can be incorporated into the methodology and ‘why’ theyaid in the process of creating a conceptual model.3.1.3.4 Methods and techniquesThe Beech (2005) research design map can be used to provide some indication of the alternativemethods and techniques that could be used for this research study, informed by the choices madepreviously. In this case the work falls into the subjective (ontology), interpretivist (epistemology)and inductive (methodology) categories of techniques. Following the Beech (2005) map, threecategories of techniques can be considered: Case study – involves ‘an empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between the phenomenon and context are not clearly evident’ (Yin, 2003, pp. 13). Observations/participation – includes direct observation, participant observation and action research. 56
    • Survey research – involves the collection of information from individuals (through mailed questionnaires, telephone calls, personal interview, etc.) about themselves or about the social units to which they belong (Rossi et al., 1983).3.1.3.4.1 Case-study techniqueA case study method is particularly beneficial when investigating new research areas or subjectswhere existing theory is inadequate (Eisenhardt, 1989). Case-studies are often seen as inherentlyflexible in that the research scope can expand to encompass new issues that may arise as theinvestigation progresses (Beach et al., 2001). They are particularly important for addressing ‘how’and ‘why’ questions (Yin, 2003), taking into account contextual factors but at the same timelimiting the extent of the analysis (Eisenhardt, 1989; Voss et al., 2002; Seuring, 2008). This isimportant as there is a considerable lack of understanding of conceptual modelling for supplychain problems in its real life context. A new approach is being developed and behaviours areunknown. These problems can be studied with a good degree of intensity (Benhasat, Goldsteinand Mead 1987) and detail so that the SCM2 can be refined and validated to show that it worksbefore implementation in practice.3.1.3.4.2 Observation and participation techniquesPlatts (1993) cites Gold (1959) who suggests three different roles that a researcher could takewhen developing a framework or methodology: Direct observation - would involve the researcher being detached from the process and witnessing how participants are undertaking the process Participant observation - involves the researcher taking part in the process to observe but not take part in the way in which the participants are undertaking the process Action research - goes one step further by enabling the researcher to take part in the process and seek to influence the way in which the activities are conducted.Platts’ (1993) argued in the context of developing a framework that the research should set out toactively apply the process that had been developed, both to test it and refine it in practicalsituations. At this earlier stage of development it would be difficult to observe or participate in aproject as no methodology or even guidelines currently exist. It is first necessary to design anoutline of the methodology and apply it so that the detail inherent in the procedure can berefined. Platts (1993) argued that action research would be a suitable method but instead of theresearcher being the ‘consultant’ they should act as a ‘facilitator’. Benton (2009) suggested thatacting as a ‘consultant’ may invalidate the results from the refinement and validation stages. This 57
    • is because the methodology is being altered and modified during the application so that it meetsthe specification of requirements in the context of the supply problem.There are a number of approaches that could be used by the researcher to make observations.This includes using the literature to establish a strong conceptual base on existing practice insimulation conceptual modeling within the context of SCM applications. In addition to this,grounded theory has been noted as an approach to conducting qualitative theory buildingresearch in management (Suddaby, 2006). Although little evidence exists of its explicit use in OMresearch (Binder and Edwards, 2010).Grounded theory is appropriate when research and theory are at their early, formative stage andnot enough is known on the phenomenon to state hypotheses prior to investigation (Auerbachand Silverstein, 2003). The approach offers a ‘compromise between extreme empiricism andcomplete relativism’ by articulating a middle ground in which systematic data collection is used todevelop theories that address the interpretive realities of actors in social settings (Suddaby, 2006,p. 634). Also, similar to a case-study method the major research interest lies in the identificationand categorisation of elements and the exploration of their connections within social settings.Glaser and Strauss (1967, p. 6) suggests that these concepts come from the data, ‘but aresystematically worked out in relation to the data during the course of the research’. Binder andEdwards (2010) highlight that a great variety of sources and evidence can be used (e.g.documents, archival records, interviews, field observations) to categorise a holistic andmeaningful set of characteristics of reality. This is achieved through a process of iteration(analytical induction), so data is collected and analysis is preceded in tandem, repeatinglyreferring back to each other (Bryman and Bell, 2007). This is conducted until theoreticalsaturation is achieved which is when newly analysed data do not warrant any further changes tothe concepts (Binder and Edwards, 2010). 58
    • 3.1.3.4.3 Survey researchSurvey research is a way to collect information from one or more people about the phenomenabeing studied. The method is generally associated with a positivist paradigm in order to achievesystematic observation, interviewing and questioning through predetermined research questionswith the intention of providing standardization and consistency (Fink, 2005; Moser and Kalton,1971). Yin (2003) also notes that the method is appropriate for answering ‘what’ type of researchquestions. A survey method would not be appropriate for addressing ‘how’ a conceptual modelcould be created. Although, two types of survey could be relevant: Exploratory survey - could be used to gain preliminary insight to establish existing practice in conceptual modeling for SCM applications. This type of survey is generally used when there is no model and concepts of interest need to be better understood and measured (Forza, 2002). Forza (2002) notes that in the preliminary stages exploratory survey research can help to determine the concepts to be measured in relation to the phenomenon of interest, how best to measure them and how to discover new facets of the phenomenon under study. Descriptive survey – could be used to understand the relevance of the methodology from the perspectives of potential users. The aim is not theory development but it can provide useful hints for further theory building and theory refinement (Dubin, 1978; Malhotra and Grover, 1998; Wacker, 1998; Forza, 2002).The exploratory survey is not necessary as the literature can be used to provide the conceptualbase on existing practice in conceptual modeling and the outline design for the methodology. Themain aim of the research is to identify ‘how’ the concepts identified in the design can beincorporated into a procedure that utilises domain-knowledge. This requires the design to beapplied in a number of different contexts. The descriptive survey has been noted previously as animportant part of a research design for a methodology or framework but usually is noted asfurther work after the initial development and testing stages.3.1.3.4 Discussion and justification of techniquesCase studies, using the literature and grounded theory would each, or a combination, wouldpresent a useful approach for this research project. As suggested by Platts (1993) there is a needto apply the methodology to a small number of case applications so that the methodology can berefined and validated. Grounded theory can incorporate a set of cases and provides a processthat can be followed to analyse data in an iterative way. A potential problem of conductingoriginal case studies is that it takes considerable time and financial expenses making groundedtheory development relatively inefficient (McCutheon and Meredith, 1993). One approach that 59
    • can address these issues includes a novel inductive approach termed ‘iterative triangulation’which has been used successfully in past research (e.g. Mintzberg et al., 1976 and Kelley, 1986).Iterative triangulation offers a rigorous process and explicit techniques for comparing diverse casesettings and incorporating varied research perspectives to aid in the development of creative,useful and valid OM theory (Lewis, 1998). The method searches for patterns among cases,iterates between case evidence, reviewed literature and intuition to extend and link conjecturesinto a cohesive theory (Lewis, 1998). Data is analysed using existing cases as an efficient andeffective means for theory development. Lewis (1998, pg. 457) argues that ‘analysing existingcases taps an often abundant source of field-based information, while conserving valuableresources that would have been needed to conduct multiple, original case studies’.Larsson (1993) suggests the cases are selected to provide imaginative means to link purposefullyvaried and often seemingly contradictory case situations. Additionally, consider different caseauthors perspectives to potentially reduce researcher bias. This is of particular value as it wouldbe difficult to access primary data from companies to create an original case-study for conceptualmodelling of SCM applications. The rationale for this includes there is a lack of methods andunderstanding of how a conceptual model should be created. The emphasis in this research is toidentify how a procedure can be followed using domain-knowledge, through a process of beingguided by existing practice and intuition.The methodological process of iterative triangulation includes four phases (shown in table 3.1)following many prescriptions for developing theory using ‘disciplined imagination’ described byWeick (1989). Iterative triangulation draws upon many similarities of a grounded theoryapproach and addresses some of the limitations identified when developing a framework ormethodology. These limitations are addressed by initiating research preparation with establishinga strong conceptual base from the literature; applying cases to develop and refine themethodology and concluding the theory development process by evaluating the theoreticalcontributions of theory prior to theory testing. 60
    • Table 3.13 Similarities between an iterative triangulation and grounded theory method Iterative triangulation Similarities with grounded theory process (Lewis, 1997) (Bryman and Bell, 2007)Phase I Groundwork: consists of ground laying stepsthat define methodological and theoretical The process begins with setting research questions,parameters. Clarifying research questions, theoretical sampling and collecting relevant data.constructs of interest, and case search strategiesand selection criteria. The data is coded to generate concepts. There isPhase II Induction: consists of several techniques to movement between earlier steps (iteration), newanalyse case data, to track down patterns and data added if required and constant comparison ofconsistencies and to shape initial conjectures. indicators and concepts to generate categories. The categories are saturated during the codingPhase III Iteration: describes iterative means of process and relationship between categories isrefining the emerging theory by creatively explored so that connections between categoriesgeneralising beyond the data. emerge.Phase IV Conclusion: concludes the theorydevelopment process by evaluating the theory and Further data may be collected to test emergingsuggesting future research possibilities. This phase hypotheses which leads to the specification ofserves to assess theoretical contributions and to substantive theory.begin bridging theory development with theorytesting.In relation to incorporating principles of grounded theory these are also shown in table 3.1. A keydifference is that data is analysed using existing case applications to identify a set of propositionsthat are unproven (conjectures) and thus shape how the procedure is to be formed. Weick (1989)suggests that selecting conjectures becomes more of a matter of judgment determined by theresearcher by conducting mental experiments. The experiences and assumptions made arereviewed with the literature and case data to evaluate the conjectures made, spurring creativity(Lewis, 1998). The majority of the tools to collect data, code and to reach closure in the iterativetriangulation process are taken from a grounded theory method (e.g. theoretical sampling,coding, theoretical saturation).3.1.4 Justification of five stage approachThe design of the research programme needs to address the issues discussed in the previous foursections. No guidelines were found for designing a simulation conceptual modelling methodologyfor SCM applications although some issues were raised for conducting research when designing aframework or methodology. Additionally, it was found that work of this nature is interpretativeand requires the methodology to be applied. This discussion draws on each of these sections andconsiders them in light of some recent examples of frameworks or methodologies developed inthe literature (e.g. Platts, 1993; Baines, 1994; Lee Gan Kai, 2007; Julka, 2008; Benton, 2009) andthe iterative triangulation process. The aim is to identify the stages that need to be included inthe research programme. 61
    • Platts (1993) proposed a three stage approach for researching manufacturing strategy, whichprovides a useful starting point for considering the research design for this project. The degree towhich each of the stages is conducted in the literature varies but some common considerationscan be identified that meet the issues previously discussed. These stages include: 1. Creation of the process based upon existing knowledge – the SCM2 must be grounded in existing theory by identifying a strong conceptual base (corresponds to phase I: groundwork of the iterative triangulation process) 2. Testing (this thesis only claims an initial validation) and refinement through application in a small number of companies – The design of the SCM2 would need to be refined and validated by applying the methodology in a small number of supply chain applications (corresponds to phase II: induction, III: iteration and IV: conclusion of the iterative triangulation process) 3. Investigate the wider applicability of the process by survey – Once the methodology has been initially validated in a small number of supply chain applications, its wider applicability should be sought (not included in the iterative triangulation process)There is consensus in the literature that a methodology needs to be developed based uponexisting practice and refined through application (e.g. Platts, 1990; Baines, 1994; Gan Kai, L.W.,2007; Julka, 2008 and Benton, 2009). This addresses the requirement noted previously that amethodology needs to be built from a strong conceptual base and the ‘groundwork’ initial stageof the iterative triangulation methodological process. Firstly, existing practice would need to beidentified and critiqued and from this base a specification detailing the requirements can beformed. Secondly, a design for the methodology is outlined building upon existing practice andthe requirements. It must be noted here that the term ‘outline’ is usually referred to as ‘concept’in design stages but due to the nature of the methodology being developed ‘outline’ is adoptedfor clarity. In order to cover the development aspects of the design the following three stages arenecessary: 1. Review existing simulation conceptual modelling practice in the context of SCM applications - to establish the need for a methodology 2. Form a specification of the requirements for simulation conceptual modelling in the context of SCM applications – the requirements that the design must meet 3. Develop an outline design - includes the key concepts that need to be implemented into the methodology to meet the required specification.The testing and refinement stage noted by Platts (1993) is open for debate and has beenapproached in different ways. A distinction needs to be made into whether refinement is part of 62
    • the development of the methodology, testing, or both. In the context of the iterativetriangulation process this corresponds to the induction and iteration phases. The aim in thisresearch is to follow the normal cycle of research as described by Meredith (1993) by continuinglyiterating between descriptions to explanation but does not claim that the SCM2 has beenrigorously tested. Meredith (1993, pg. 3) notes that the result is to validate and add confidence toprevious findings or else invalidate them and force researchers to develop more valid or morecomplete theories. In respect to refining the methodology it is evident that the majority ofcontributions seek to refine an initial design formed from existing theory (e.g. Lee Gan Kai, 2007;Chandraprakaikul, 2008) but in some contributions a separate validation or testing stage is lessevident, or non-existent (e.g. Apaiah, 2006; Wong and Johansen, 2007; Lima, 2008; Symeonidis etal., 2008).Testing has been identified as an important requirement to improve the rigour of the study. Inthe contributions that present a testing stage they, on the whole, are described as a ‘preliminary’validation, noting specifically the context in which they have been applied (e.g. Chandraprakaikul,2008 and Benton, 2009) to demonstrate that a process or framework initially works. Thiscorresponds to the final phase of the iterative triangulation process when the methodology isevaluated and future research directions described (e.g. future testing requirements). This isnecessary as the applicability of the methodology can only be claimed within the boundaries ofthe applications (e.g. industry, complexity and detail of supply chain, geography). In order to splitthe refinement from the preliminary validation the following two stages can be identified for thisproject: 4. Detail and refinement of the outline design - so that it meets the specification previously formed 5. Preliminary validation - illustrate the methodology against the validation criteriaThe testing of the wider applicability is usually considered once the initial validation has beencompleted successfully. It was difficult to identify studies that attempt all three stages identifiedby Platts (1993). For instance, Yee and Tan (2004) and later in Yee and Platts (2006) developed aframework, and tool for supply network strategy operation, based upon organisations within asingle supply chain case. A closing comment is made in Yee and Platts (2006, pg. 245) that‘additional work is required to address the issue of the framework validation and tool applicationin wider industries and sectors’ (outlined as an intention for further work). Similarly this is alsothe case in Platts and Canez (2002) development of a process to aid in make vs. buy decisionsusing one case-study. These examples demonstrate that initial testing is required in a small 63
    • number of applications and further work is needed to be able to generalise the findings. It is alsoimportant to be clear on any deficiencies or limitations that need to be addressed at a later stageto identify the need for testing the wider applicability. This point is considered when discussingthe outcome of the preliminary validation and made explicit in the concluding chapter.3.2 Research programme and methodsThe research programme describes the sequence of stages that has been conducted to realise theresearch aim and objectives. Explicit in the aim of this research is to develop and preliminarilyvalidate a methodology that can support a user in the creation of a conceptual model for a givensupply chain problem. The previous section identified the research stages required to realise thisaim. Section 3.2.1 will provide an overview of the research programme and methods, and alignthese to each of the research objectives and questions. The subsequent sections will describe inmore detail the rationale and approach adopted for each stage.3.2.1 Overview of research programme and methodsThe research programme is structured to firstly develop the methodology followed by refinementand validation. This is broken down into a series of five stages that address each of the researchobjectives and questions. The remainder of this thesis is structured to implement each of thesestages as summarised in figure 3.1. Stage I – chapter 4 Methodology Development Review of existing conceptual modelling practice Stage II – chapter 5 Establish the need for a feasible SCM2 Form the specification for the SCM2 Stage III – chapter 6 Establish the required specification for the SCM2 Outline design for the SCM2 Stage IV – chapter 7 Outline design for SCM2 preliminary validation Refinement and Detailed and refined design of the SCM2 Stage V – chapter 8 Detailed and refined design for SCM2 that meets the specification Preliminary validation of the SCM2 A feasible SCM2Figure 3.1 Overview of research programmeThe specification of the requirements for creating simulation conceptual models for SCMapplications (objective one) is addressed by stages I and II of this research. Stage I (implementedin chapter four) answers the first research question that discusses how simulation conceptualmodels are created in the context of supply chain applications. Following on from this stage II 64
    • (implemented in chapter five) answers the second research question by detailing a specificationof a simulation conceptual modelling methodology for evaluating supply chain problems. Thisensures that the methodology is developed on a strong conceptual base which is grounded inexisting theory. The specification is used to demonstrate that the detailed and refined designmeets each of the requirements identified.The methodology is detailed and refined so that it meets the specification of requirements(objective two) in stages III and IV in the research programme. Stage III (implemented in chaptersix) constructs an outline (concept) design of the SCM2 that includes the identification of keyconcepts and a process for conceptual modelling in the context of evaluating supply chainmanagement problems. This is formed by building upon existing practice and in line with therequirements specified in stages I and II. The outline design is detailed and refined by applying itto two representative and typical supply chain problems, addressing research question two inchapter seven. This includes identifying how each key concept was implemented, principles andobservations identified in the refinement, and choices made and incorporated into the SCM2.The final objective is realised by providing a preliminary validation of the initial feasibility andutility of the SCM2 in stage V of the research programme. The aim of stage V (implemented inchapter eight) is to show that the methodology can be followed (feasibility) and aid a user (utility)to create a simulation conceptual model for SCM applications (research question four). Themethodology is applied to a different supply chain problem to illustrate that the steps can befollowed. The validation also draws a comparison between the actual practices to be representedin the model with the model components included in a successfully implemented computermodel.3.2.2 Stage I: Review of existing conceptual modelling practiceStage I of the research programme reviews and provides a critique of existing conceptualmodelling practice. The aim is to establish whether there is a need for a methodology that couldaid a user in the creation of a simulation conceptual model for SCM applications. The key findingdiscussed in the chapter is that no methodology exists for conceptual modelling and that there isa distinct lack of guidelines available in the literature, in particular for SCM applications. Thechapter addresses the following research question: How are simulation conceptual models created in the context of supply chain applications? 65
    • A critical literature review addressed this research question so that the development work wasfounded on a strong theoretical basis. The review focuses upon simulation conceptual modellingwith a particular focus on SCM applications. The chapter establishes what approaches currentlyexist to create conceptual models. Three methods are suggested in the literature: methods ofsimplification, guiding principles, and a general framework. It was found that there lacks a clearbody of literature to provide guidelines or methods on conceptual modelling (except in Robinson,2004b simulation text). This required the review to consider the wider literature on howsimulation projects are conducted so that the approaches that were useful at the conceptualmodelling stage could be distinguished. It was observed that the body of literature on conceptualmodelling was almost absent until Robinson (2006b) presented a paper ‘Issues in conceptualmodelling for simulation: setting a research agenda’. This subsequently led to a number ofsessions held at a workshop in 2008 on the issue of conceptual modelling (UK OR SocietySimulation Workshop, 2008). Conceptual modelling research can be said to be very much in anembryonic stage but research on the general ‘how’ to conduct a general simulation project andthe application of simulation for SCM applications is extensive.Due to the lack of approaches found the next focus of the review was to discuss general problemsencountered in simulation modelling. This identified that many problems are encountered insimulation projects that could benefit from an increased understanding of conceptual modellingand approaches that could be used. The last two sections of the review focused upon the twoimportant aspects of conceptual modelling that would need to be considered in a methodology.The first discusses a mechanism for communicating the conceptual model and how its contentcan be represented. The second is on the topic of ‘conceptual model validation’, which has beensuggested as an important area for study in its own right in order to improve the rigour ofsimulation studies (Manuj, et al., 2009). The first had been covered in detail in Robinson (2004)however; there is a significant lack of discussion on the topic of ‘conceptual model validation’,particularly on the applicability of validation methods at the conceptual modelling stage of asimulation project. To remedy this issue the general simulation validation approaches werereviewed in order to distinguish approaches that are applicable at the conceptual modelling stage.Each of these discussions were useful to understand what approaches could be included in amethodology, the problems it would need to address, how it could provide a mechanism forcommunicating the content and how the conceptual model could be validated. 66
    • 3.2.3 Stage II: Forming the specification for SCM2Stage II of the research programme identifies the requirements that the methodology would needto address in the design stage. This builds upon the review of existing practice but concentratesmore on what the methodology has to deliver. The chapter addresses the following researchquestion: What is the specification of a simulation conceptual modelling methodology for evaluating supply chain problems?The purpose of the methodology is to create an effective conceptual model by following thespecific guidelines that address the needs for evaluating supply chain problems. Therefore theliterature examines three areas to identify a set of requirements that the methodology needs tomeet. These include: 1. Requirements for an effective conceptual model – Robinson (2004) has previously discussed the criteria from which the success of a conceptual model is to be judged. The implications of the discussion show that the methodology will need to build simple models, which are both valid and credible. 2. A requirement for a ‘good’ methodology – A methodology has distinct qualities that differentiate it from a framework, guiding principle, or method of simplifications. Platts (1994) and Platts et al., (1996) identified the characteristics of a procedure, points of entry and project management. The implications of these characteristics are discussed in order to identify what the procedure should deliver, how the methodology should be entered and iteration within the process, and justifies that project management is outside of the scope of the methodology. 3. Requirements for conceptual modelling of supply chain problems – Discusses the requirements for evaluating supply chain problems in the context of simulation conceptual modelling. It specifically identifies the complexity and detail of supply problems and how an objective is measured.The chapter concludes with a set of requirements that make up the specification. Thisspecification is used to guide and align the design of the methodology so that it meets each of therequirements before the preliminary validation. 67
    • 3.2.4 Stage III: Discussion of the outline design for the SCM2Stage III of the research programme discusses an outline design for the SCM2. This stage focusesupon how the methodology will address the requirements established in the specificationdetailed in chapter five. The outline design is further refined and detailed in the following stage,both stage III and IV contribute to the following research question: Can a methodology be developed for a simulation conceptual modelling methodology for SCM applications?The specification detailed in chapter five discusses the requirements for an ‘effective’ conceptualmodel, ‘good’ methodology and for creating conceptual models for ‘SCM’ applications. In thecontext of the purpose of this thesis a number of design issues can be identified for each of therequirements identified. These issues are discussed in turn so that a set of key concepts can beidentified that are specific to developing a SCM2. These key concepts are aligned with a generalprocess for conceptual modelling to identify a unique set of phases that should be included in themethodology.3.2.4.1 Design issues for addressing the requirements for a ‘good’ methodologyThere are three design issues to address the requirements for a ‘good’ methodology. The firstconcerns ‘who’ will use the methodology and ‘how’. The second is to identify a general processfor designing a conceptual model by reviewing existing contributions to synthesis a view of ageneral guide. Finally, the points of entry to the methodology and discussion of the need toiterate between stages in the methodology are considered. Therefore, the literature is examinedto address the three following questions: Who are the participants and how should they be involved in the process of conceptual modelling for SCM applications? What is the general process that participants need to follow? What is the point of entry to a methodology for creating a conceptual model for SCM applications?3.2.4.2 Design issues for addressing the requirements for an effective conceptual modelThere are also two design issues for addressing the requirements for an ‘effective’ conceptualmodel: keeping the model as simple as possible, and building a valid and credible model. The firstis addressed by discussing how a problem is stated and the model boundary formulated. Thereare two issues surrounding the building a valid and credible model: in the creation (i.e.incorporated into the steps), and to evaluate the validity and credibility of the conceptual modelitself. There is relatively little written on either aspect but there has been significant discussion in 68
    • the general simulation literature. For this reason, the critique centres on the applicability of thegeneral discussions in simulation to the conceptual modelling stage. The section addressed thequestion below and contributes to some ideas that are incorporated into the methodology: How can the methodology aid the participants to create a conceptual model that is as simple as possible and both valid and credible?3.2.4.3 Design issues for addressing the domain specific requirementsThe design issue for addressing the requirements for conceptual modeling of supply chainproblems entails identifying any domain-specific needs. The focus is on how information can beextracted from particular sources (principally the client and the modeller). The thesis argues thatthere is an opportunity to extract information from published sources to make the process ofconceptual modelling more efficient and focused. This is achieved by discussing the role ofdomain knowledge in conceptual modelling in light of each of the requirements identified inchapter four. One source is distinguished to present a great opportunity to extract domainknowledge, namely a ‘process reference model’ that is specific to the SCM domain.A number of process reference models for the SCM domain are critiqued to identify those thathave potential to provide the domain knowledge necessary for conceptual modelling. Theevaluation uses the criteria offered by Becker et al., (2003) who presents six main qualities for aneffective business model (i.e. correctness, relevance, economic efficiency, clarity, comparabilityand systematic design). The SCOR model is selected as a reference model that offers the greatestpotential to provide domain knowledge for conceptual modelling when compared to alternatives.The information extracted from SCOR is discussed in terms of the detailed descriptions that itoffers (e.g. business processes, performance attributes and metrics, practices) and how this wouldbe useful for conceptual modelling. Although SCOR has been used for simulation purposes,particularly for designing templates for model re-use (e.g. Albores, et al., 2007) and for buildingsimulation applications (e.g. Barnett and Miller, 2000; Albores, et al., 2006; Chatfield, Harrisonand Hayya, 2006; Persson and Araldi, 2009) there has been little discussion of how it can beutilised in the conceptual modelling stage.To discuss the merits of extracting the domain knowledge from SCOR five archival case studies areused to identify two typical supply problems that have been implemented and evaluated in theliterature. Yin (2003) states that archival case studies can be valid and of high quality, supportedby Lewis (1998) in the OM domain and Sachan and Datta (2005) for SCM applications. The two 69
    • supply chain problems considered are vendor managed inventory (VMI), and collaborative,planning, forecasting and replenishment (CPFR) along with two objectives: to improve supplychain costs and delivery performance. These archival cases were extracted from five researchcontributions: Disney and Towill, 2003a, 2003b; Reiner and Trcka (2004); Chang, Fu, Lee, Lin andHusueh, 2007; Sari (2008) and Southhard and Swenseth (2008). Using a number of archival casesis beneficial as it shows how SCOR can be used to extract information for two improvements andtwo objectives.3.2.4.4 Presentation of outline design for SCM2Ten key concepts are synthesised and described from the analysis of the design issues for theSCM2. Each of the key concepts identified are linked to a general process for conceptualmodelling so that the specific phases to be included in the methodology can be established. Theinputs (e.g. information requirements) and outputs (e.g. information provided) from each phaseare discussed in light of the information that needs to be extracted from the client, modeller, andthe SCOR process reference model. The chapter concludes with an outline design of themethodology to be detailed and refined in stage IV of the research programme.3.2.5 Stage IV: Discussion of the detailed and refined design of the SCM2Stage IV discusses the detail and refinements made to the outline design of the SCM2 and alignsthe design to the specification presented in chapter four. This stage also contributes to theresearch question that develops a simulation conceptual modelling methodology for SCMapplications. It corresponds to the induction and iteration phases in the iterative triangulationmethod.The question to be addressed is ‘how’ a conceptual model can be created for SCM applications.Two development cases were selected of typical and representative supply problems that areboth contextually rich and exploratory (detailed and justified in section 3.3). The cases were usedto detail and refine the methodology with illustrations to justify the choices made in the design.The induction and iteration stage was conducted by analysing the case data based upon a set oftypical design questions/issues that were identified in phase III of the research methodologicalprogramme. The typical design questions/issues along with the aims for the requiredspecification is shown in table 3.2. These correspond directly to guiding the researcher toperform different applications in order to address the needs of the required specification. Thisresulted in a list of principles and observations that influenced the design. Each of the designchoices that address the questions and issues are documented and validated in appendix A. 70
    • Table 3.24 Design questions and issues to address the requirements Aim Requirement Typical design questions/issues How can the core components of the model be identified? Keep the model as How can the model boundary be determined?Meet the simple as possible How can simplification methods be incorporated into the methodology?requirements for an How can the accuracy of the descriptions provided from the steps be checked foreffective model Build a valid and correctness? credible model How can the accuracy of the final conceptual model be checked for correctness? How can a conceptual model be created, what steps need to be followed to Procedure incorporate the key concepts identified?Meet the How should each of the steps be carried out?requirements of How are the participants involved in each step of the methodology? Participationgood methodologies How can information be extracted from the participants when necessary? How should the methodology be entered? Points of entry How should iteration occur within the methodology? Supply chainMeet the How can SCOR be used to describe a supply chain improvement? improvementsrequirements for Supply chainconceptual How can SCOR be used to describe a supply chain objective? objectivesmodelling of supply Supply chain How can SCOR be used to identify the interconnections between components andchain problems setting the supply setting?The two development cases are applied to all phases except the final phase (document andvalidate) and step 6.2 (describe the model components). The validation checks within themethodology phases are completed. The final stage re-checks each of the in-phase validationsteps and performs a hypothesis test and an expert review of the model content. The latter isillustrated using representative examples from both of the two development cases. The rationalefor applying the cases in full up to step 6.2 is that it is at this point that the work is novel.Step 6.1 describes the ‘domain-orientated’ model which includes a description of the actualpractices, relationships between practices at the desired scope and level of detail. This has yet tobe explored in the literature but how to describe the model components is well documented andcovered in most simulation text books (e.g. Pidd, 2004; Robinson, 2004). The methodology instep 6.2 and the latter final validation procedure reflects existing practice. At this point the way inwhich a modeller will represent the model components may depend upon their simulationworldview or experiences using different simulation approaches. It can be argued that the holisticvalidation procedure is novel but it is also a considerable area of study in its own right. Theillustrative examples are described using standard discrete-event simulation terminology (e.g.Pidd, 2004; Robinson, 2004) for process based simulation approaches (e.g. Witness, Simul8,Gensym e-SCOR).3.2.6 Stage V: Preliminary validation of the SCM2Stage V of the research programme implements the preliminary validation of the SCM2. It wasnoted in section 3.1 that the refinement and the validation application should be separated toavoid confusion, and, to provide a fresh structured walkthrough of a new supply problem afterthe specification had been met. This stage concludes the ‘iterative triangulation’ process by 71
    • evaluating the methodology against a set of criteria to demonstrate the SCM 2 is both ‘feasible’and has ‘utility’.To conduct this stage the SCM2 is applied to the validation case to show that it can be followed todescribe the actual practices to be included in the model (feasibility). This description is thencompared with an actual computer model to demonstrate that there are no major omissions orsignificant differences (utility). Stage V addressed the final research question and identifies areasthat require future testing by addressing: Can the methodology be followed (feasibility) and aid a user (utility) to create a simulation conceptual model for a SCM application?The validation case only assesses the novel areas of the methodology. This means the steps arefollowed up to the point that the actual practices to be included in the model (domain-specificmodel) are described. It was not necessary to evaluate steps 6.2 and the final validationprocedure for the same argument presented in section 3.2.5 (e.g. due to the steps mirroringexisting practice, different worldviews). There are alternative approaches that could beconsidered to complete this stage. One approach would be to survey, or interview, typicalparticipants to ask whether the output from the methodology is correct. An alternative includescomparing the output from the methodology with a completed and successful computer model.The second option is preferred as this allows a direct comparison to be made between each actualpractice to be represented and the model components and interconnections included in thecomputer model.To validate the methodology some criteria for evaluation is required. Platts (1993) suggested thatthe prime aim for assessing a process framework or methodology is to determine whether theprocess did provide a practical, procedural step. Platts (1993) lists three criteria for assessmentwhich have been posed as a series of question in Platts et al., (2001) and some sub-criteria havebeen suggested in Tan and Platts (2002) shown in table 3.3. These have been discussedspecifically for a process framework for manufacturing strategy but the criteria has been used formethodologies and different industrial contexts. This includes manufacturing action plans (Tanand Platts, 2002), human performance modelling methodology (Baines and Kay, 2002), make orbuy process framework (Canez et al., 2000), process framework for selecting supply systemarchitecture (Benton, 2009). 72
    • Table 3.3 5 Criteria for assessing a process framework or methodologyPlatts (1993) criteria/ Feasibility Usability Utilityquestions posed in Platts et Could the methodology be How easily could the Is the methodology worthal., (2001) followed? methodology be followed? following? Availability of Clarity RelevanceTan and Platts (2002) sub- information Ease of use Usefulnesscriteria Timing Appropriateness facilitation participationThe methodology is preliminarily validated against the feasibility and utility criteria; the usabilitycriteria are outside the scope of this thesis. Usability cannot be assessed at the preliminaryvalidation stage as it requires the evaluation of how potential users of the methodology followedthe steps laid down in the methodology. The emphasise at this stage is to demonstrate that theSCM2 could work in practice and be able to create a useful conceptual model. This is particularlyimportant as the methodology presented in this thesis is novel and original and little discussionhas been placed in the area of conceptual modelling for SCM applications (e.g. incorporatedsimplification methods, principles). In addition to this the utilisation of SCOR for conceptualmodelling has yet to be considered so emphasis is also placed on ‘how’ it can be incorporated.The ease in which SCOR and a process for conceptual modelling can be used to create aconceptual model is noted as an area for future work. Although, it is demonstrated that usingSCOR with a process of conceptual modelling can potentially provide the opportunity to make theprocess more focused and efficient (in section 6.4). To demonstrate that the usability criteriahave been met future tests are required with actual facilitations and participants in differentindustrial settings. This is considered in a discussion of issues for future testing and somesuggestions for undertaking these tests are noted in section 8.6.3.3 Theory building through existing case study applicationsStages IV and V both include development and validation case applications as part of theinductive and refinement stages of an iterative triangulation approach. The previous discussionfocused upon how the analysis was conducted but not on the specific issues presented whenusing case applications. There are three initial issues that need to be considered when developinga business methodology suggested by Platts (1993): 1. the involvement of the researcher 2. the consistency of the process 3. the choice of cases to be studiedThese are discussed in turn in the following section along with a description of how data wascollected from the secondary cases. 73
    • 3.3.1 Involvement and reflexivity of the researcherThe aim of stages IV and V is to develop, refine and validate the methodology through application.This is similar to Platts’ (1993, pg. 9) research which set out to actively apply the process that hadbeen developed, both to test it and refine it in practical situations. The choices between directobservation, participation and action research was previously discussed in section 3.1.4.2. Theapproach adopted is to use existing cases to apply the methodology, as it is designed, and that theresearcher can reflect and evaluate the initial feasibility and utility of the methodology.The role of the researcher is to follow the phases identified in the outline design and identify thespecific steps that need to be conducted in order to meet the specification of requirements. Theaim was not to impose the views of the researcher on the process but to consider the designissues that are necessary to create a conceptual model for SCM applications. This is necessarybecause little guidance exists that could be observed in practice; the design issues have yet to beconsidered.Platts (1993) does note that this level of involvement may present a bias but is necessary whenrefining and validating a developed methodology or process. Potential bias is reduced byinvolving more than one researcher in two of the cases and one case is a traditional teaching caseused extensively for research purposes (discussed in more detail in 3.3.3). An advantage of usinga development case is that the researcher has collected data from published sources and in onecase a team of researchers (FUSION research group) to ensure independence in the applicationstage. Additionally, the validation case is detached intentionally from the development cases sothat the methodology can be applied without adjustment and after the required specification ismet. The application of the methodology is conducted consistently and the design issues areevaluated and later justified based upon showing that the methodology can be followed to createa conceptual model. This is the focus of the following section.3.3.2 Consistency of the processThe second research issue was the ‘choice between applying the process consistently across thechosen companies [case applications], or, by developing and refining the process as experiencewas gained’ (Platts, 1993, pg. 10). Although the first approach will allow the comparison of themethodology the second is more appropriate as it enables the modification of the SCM2 in light ofexperience gained. Platts (1993) noted this would be more robust and useful at the end of theresearch. The refinements were conducted using the experience from two development casesbefore a separate validation case is compared to an actual computer model. The application wasalso improved by utilising the same research, although, it is recognised that different participants 74
    • following the methodology are required, in future testing, to improve the validity and robustnessof the findings. Additionally, each of the applications has been documented to either define thesteps in the methodology, provide the rationale behind the design decisions or to evaluate thepreliminary validation criteria.3.3.3 Choice of supply chain application casesOne of the most important choices in the research design is the selection of supply chainapplications, number of cases and how they should be used. In terms of selecting cases there areno clear guidelines except Platts (1993) who presents a choice between consistently findingsimilar applications and validating the methodology in different situations. Eisenhardt (1989)adds that the cases should highlight polar extremes. Other advice includes the cases shouldrepresent all or most of the constructs of interest to maximise theory coverage within cases andbe ‘open’ to interpretation (e.g. lots of detail and description) (Lewis, 1998).The question of the number of cases and how they should be used has been considered broadly inthe OM literature (e.g. Lewis, 1998; Meredith, 1998; Voss, Tsikriktsis, Frohlich, 2002) and widerliterature (e.g. Eisenhardt, 1989; Yin, 1994) but not in the context of developing a methodology.In order to address this question related research can be considered in particular PhD theses thathave used application cases to develop a methodology.A review of SCM and OM related PhD theses demonstrated different approaches and number ofcases being selected when developing a methodology. The development and refinement stagehad been separated from the validation of the methodology. For instance, considering thenumber of development cases the differences include: one developmental case (e.g. Baines, 1994;Benton, 2009) although in Benton (2009) it was used to illustrate the framework not refine it, twodevelopmental cases (e.g. Julka, 2008) and Lim Yan Gaun (2007) were found to have the highestnumber of developmental cases with four. In terms of validation cases: Baines (1994) and LeeGan Kai (2007) used a single organisation, Julka (2008) used two, Benton (2009) had three and LimYan Guan again had four. Some interesting observations include Julka (2008) who used the sametwo cases to develop and validate. Benton (2009) placed emphasis using three cases to evaluatethe framework against Platts (1993) test criteria after illustrating the methodology with one case.Lim Yan Guan (2007) who had the most cases explicitly noted that they were used to eitherdevelop, or test a methodology but not applied with much depth.Yin (1994) suggests that two or more case studies support replication and the empirical resultsare considered more potent. Voss et al., (2002) contend that the fewer the case studies, the 75
    • greater the opportunity for depth of observation, although a multi-case study (3 – 30 cases) canaugment external validity. It can also be argued that the cases should be separated to developand validate the methodology. The developmental work is novel and requires a great level ofdepth therefore two cases have been selected and one validation case to walkthrough themethodology. This is different to Benton’s (2009) approach because it places a greater emphasison the development and refinement stage of the research.The first development case selected is a traditional teaching case that has been described as agood illustration of a real-life supply chain (Sterman, 1989; Disney and Towill, 2003a; 2003b), richenvironment and realistic decision rules. The second is an industrial case of a different but typicalautomotive problem which is extremely detailed and complex. Both development cases allow forall the essential features of the methodology to be applied. The industrial case provides a greaterlevel of detail and description that can be used to explore the many improvements and objectivesdefined in the SCOR model (many of which have been piloted in the automotive sector). Thedevelopment cases cannot be argued as polar extremes but include different types of problemand complexity. A validation case is included to walkthrough the final design of the methodologyafter it had met the required specification. The validation case is selected because it offers theopportunity to compare and evaluate any significant differences between an actual publishedsimulation model and that of the conceptual model. The purpose and detail of each developmentand validation case are described later in the thesis and summarised in table 3.4.Table 3.4 6 Summary of cases used to develop and validate the methodologyCase Type Industry Background Sources Teaching case used extensively in the Sterman (1989); MA systems (Accessed Food and simulation literature (e.g. Sterman, 12/04/2007); MIT Beer Game (AccessedBeerCo D beverage 1989; Wilkner et al., 1991; Simchi- 12/04/2007); Swiss Federal Institute of Technology, Levi et al., 2003; Disney et al., 2004) Zurich Business Game (Accessed 12/04/2007) The case represents a detailed and complex supply chain problem of a Collaboration between Aston, Strathclyde andCarCo D Automotive simplified seat supply chain. FUSION Liverpool in the FUSION research group research was funded by ESPRC. The main source of data providing detail from a Fictionalised case presented in Taylor number of manufacturers of coffee makers came Small kitchenCoffeePotCo V et al., (2008) of a coffee pot from Ulrich and Pearson (1998). Other sources for appliances manufacturer. cost and time data included Zeng (2003) and Zeng and Rossetti (2003).D = Development case; V = Validation case3.3.4 Data collection methodsThe data for each of the development and validation cases were collected from various differentsources. These were used to describe and define a supply chain problem and used in thisresearch to create a conceptual model that can address the problem. Yin (2003) defines sixsources of evidence in case-based research that include documentation, archival records,interviews, direct observation, participation observation and physical artifacts. All cases were 76
    • documented in qualitative and quantitative forms. The BeerCo and CoffeePotCo developmentcases have been described in detail in the literature and have been developed by and used by anumber of researchers. They are both fictionalised cases taken from published sources.The CarCo development case used interviews considerably with individuals from each of thecompanies in the supply chain. This is important to ensure ‘investigator triangulation’ thatstrengthens the validity of the detail presented in the case. The members of the FUSION researchteam also directly observed the process in each of the manufacturing plants in the seat supplychain. The data was collected by researchers that were independent of the research conducted inthis research project. This is important as the data collected was not biased by the researcherundertaking the application of the methodology.3.4 Limitations of research approachThe research design has a number of limitations, both in the design and most notably in the areathat the methodology has not been rigorously tested. Each of the issues identified are consideredin more detail in the further work section of the concluding chapter. The key issues include: 1. Number and selection of cases used - Three existing cases were used based upon secondary data, two to develop (one of which is an industrial case) and one to validate the methodology against the validation criteria. Yin (2003) suggests three cases is sufficient for literal replication (prediction of similar results) but more cases are required for theoretical replications (predicts contrasting results but for predictable reasons). In particular, further applications should include original cases (using primary data) to further refine the methodology as part of a continuing development and learning journey until eventual theory can be claimed. 2. Future testing is required to generalise and improve the validity of the findings – The preliminary validation only demonstrates that the methodology is initially feasible and has utility. Further work is required to test the methodology by observing participants independent of the researcher and in different industrial contexts. 3. Wider applicability of the methodology – The methodology developed and preliminarily validated does not claim to be widely applicable at present. Once the process had been tested in more industrial contexts it would be useful to improve the methodology by asking potential users how the process can be enhanced. 77
    • 3.5 Validity and reliability of the researchIt is important to consider the validity and reliability in case based research (Yin, 1981, 1984;Eisenhardt, 1989; Voss et al., 2002). Reliability concerns the question of whether the results ofthe study are repeatable and validity concerns the integrity of the conclusions that are generatedby the research and that a study must be replicable (Bryman and Bell, 2007).The reliability of the methodology is addressed in this thesis by following a detailed researchmethodological programme, addressing the key issues when developing a business methodology.It was argued in this thesis that the refinements of the methodology are conducted in light ofmeeting the required specification and that the initial evaluation should be independent of therefinement made when applying the developmental cases. It is noted in future work thatdifferent potential users of the methodology is needed to test that the methodology would befollowed to deliver an appropriate output that is useful to address the context of the supplyproblem.The validity of the methodology is enhanced through the forming of a conceptual base usingexisting contributions evaluated in the literature and existing cases: two developmental and onevalidation case. This is supported by Eisenhardt (1989) who states that tying the emergent theoryto existing literature can enhance the internal validity as well as generalisability and thetheoretical level of theory building. Data, theory and method triangulation was achieved toreduce any bias when establishing the conceptual base as a variety of theories and sources wereevaluated when conducting the literature review combined with archival cases. Although two ofthe cases were fictionalised, one (CoffeePotCo) was based upon data extracted from publishedsources that have used a variety of data collection methods observing an industrial manufacturingenvironment and related shipping times and costs. In the case of the CarCo development casedata was collected from different methods (documentation, interview and direct observation) andusing different investigators.3.6 Ethical considerations and issuesEthical considerations are important when conducting and presenting research. This includes thedata collected direct from organisations, individuals and the various research outputs examined.The cases used do not disclose any details which have not been authorised and all names oforganisations and their products have received name changes within eachdevelopment/validation case. The University and the researcher have full academic membershipof the Supply Chain Council so that the model can be utilised for the purpose of this project. 78
    • 3.7 Chapter summaryThe research programme and methods have been justified and described in this chapter to meetwith the research objectives and underlying questions. Firstly, a five-stage approach wassynthesised from existing research on methodologies, frameworks and processes. Stages I - IIIwere identified to develop and IV – V to refine and test the SCM2: Stage I: review of existing conceptual modelling practice (chapter 4) Stage II: forming the specification for the SCM2 (chapter 5) Stage III: Outline design for the SCM2 (chapter 6) Stage IV: Detailed and refined design of the SCM2 (chapter 7) Stage V: Preliminary validate of the SCM2 (chapter 8)The research methods adopted in each of the stages have been discussed, justified and detailed inline with the iterative triangulation method (i.e. literature, case evidence and intuition). Threeexisting cases were argued, two for the development of the SCM2 and one case used for thepreliminary validation. The refinement and validation involved the application of the SCM2 to thecases utilising the same researcher for consistency. The refined design was compared to therequired specification formed by a review of existing practice. The preliminary validation wasused to show the initial feasibility and utility of the SCM2. Future testing is identified to generaliseand improve the validity of the initial findings. In addition, test the usability of the methodologyin different application setting and with actual users. 79
    • Chapter 4 Review of existing CM (Stage I)This chapter reviews existing conceptual modelling practice in general and more specifically in theSCM domain. The review contributes to documenting the required specification for the SCM2 andaddressing the research question: How are simulation conceptual models created in the context of supply chain applications?The chapter is structured to address the research question noted above by discussing: Approaches to conceptual modelling in practice (section 4.1) – Three existing approaches are identified in the literature. One approach that has yet to be suggested is a methodology; it is shown that none exist for SCM applications, or in more general. Problems encountered in simulation modelling (section 4.2) – A range of problems are identified that are specific to the conceptual modelling stage which could be minimised if a methodology were to be available. Communicating and representing the conceptual model (section 4.3) – Identification of the methods that can be used to represent and communicate a conceptual model between the stakeholders involved in the simulation project. Validation of conceptual models (section 4.4) – Identifies that very little guidance is available for validating conceptual models. Existing methods for validating a simulation model are critiqued to identify methods appropriate at the conceptual modelling stage.4.1 Approaches to conceptual modelling in practiceThis section seeks to identify the approaches that have been used for conceptual modelling inpractice. Robinson’s (2006a; 2006b) survey distinguished three basic approaches in guiding theanalyst to a definition of a conceptual model that have further been detailed in Van der Zee, Pooland Wijngaard (2008). These include principles of modelling, methods of simplifications andmodelling frameworks. Table 4.1 lists and defines each of the approaches and provides examplesof them being used in the context of SCM applications. 80
    • Table 4.17 Approaches to conceptual modelling Approach to conceptual Definitions Examples in the literature modelling Pidd (1999); Morris (1967); Powell (1995a; Advocate an evolutionary development of models (e.g. start small 1995b); Pritsker (1998); Law and KeltonPrinciples and simple, adapt and extend the model (2000); Nydick, Liberatore and Chung, incrementally) (2002) Morris (1967); Zeigler (1976); Ward (1989); Work the other way around by suggesting ways for model pruning. Yin and Zhou (1989); Musselman (1994); While either approaches offer relevant assistance for conceptual Courtois (1985); Sevinc (1990); Innis andMethods of modelling, they do not a-priori address the creation of conceptual Rexstad (1983); Robinson (1994); Piddsimplification model, i.e. the identification of elementary model components (1999); Brooks and Tobias (2000); Thomas appealing to a domain and to project stakeholders. and Charpenti (2005); Chick (2006); Chwif, Paul and Barretto (2006); Brooks (2007) Distinguish themselves from the above approaches by specifying a Nance (1994); Guru and Savory (2004); Van procedure for detailing a model in terms of its elements, itsFrameworks der Zee and van der Vorst (2005); Robinson attributes and its relationships. Examples include the general case (2004a; 2004b; 2008a; 2008b) of systems representation and domain related cases.Extended from the work by Robinson (2006a; 2006b) and Van der Zee, Pool and Wijngaard (2008)Two other approaches have been omitted from the classification presented in table 4.1. The firstincludes not using any formal methods for creating a conceptual model. One reason for this isthat some modellers might argue that the emergence of modern simulation software hasreduced, even removed, the need for conceptual modelling (Robinson, 2004a; 2004b). Tocounteract this argument the general problems that could be avoided at the conceptual modellingstage, if a structured approach is adopted, are discussed in greater detail (see section 4.2).The second is a methodology that can be distinguished from a framework, as it provides aprescribed approach, with a detailed step by step guide, and suggests relevant tools andtechniques to be used for each step to deliver a specific outcome. Similar to Van der Zee et al.,(2008) definition of a framework they are generally designed and applied within a particulardomain. A methodology should be able to guide a user through the complex process of describingthe supply problem, identify the actual practices to be included in a conceptual model, how theseactual practices are to be represented by the components in the computer model and utilise waysin which to represent and communicate the model. It is argued in this thesis that a detailedmethodology is domain specific due to the nature of describing and evaluating a supply problem.No methodologies can be identified for conceptual modelling specifically for SCM application andeven in general.4.1.1 Principles in conceptual modellingThere have been a number of research contributions that have provided a set of guiding principlesfor simulation modelling (c.f. table 4.1). The majority of discussions do offer some advice or pointout the key issues when creating a conceptual model although very little could be found to bewritten in a supply chain context. Pidd (1999) is one notable contribution that has been cited byother authors (e.g. Robinson, 2004a; 2006, 2008a, 2008b; Willemain and Powell 2007). He states 81
    • six principles in a ‘rough guide for modelling’: Model simple, think complicated; be parsimonious,start small and add; divide and conquer: avoid mega-models; use metaphors, analogies andsimilarities; do not fall in love with data and modelling may feel like muddling through.There is agreement by authors that the central theme and aim in simulation modelling is to createsimple models through evolutionary development (Chwif et al., 2000; Brooks and Tobias, 1999;Brooks, 2006; Robinson, 2004a; 2008a; 2008b). Ward (1989) has provided some rationale for whyclient managers may prefer constructive simplicity. He states that it is not only a convenient wayof ensuring transparent models, but also for reasons related to motivation, time constraints,implementation and involvement of third parties.The most difficult aspect of a conceptual modelling project and related to the principle of ‘modelsimple’ is to determine the most appropriate scope and level of detail (Law, 1991). Efforts havebeen made to address this difficulty by studying ways to avoid the generation of complex models(e.g. Chwif, Barretto and Paul, 2000). Chwif et al., (2000) cites Pidd (1996) who compounds thismessage by declaring that complicated models have no divine right of acceptance and lists otherresearchers who have reinforced this message (e.g. Ward, 1989; Yin and Zhou, 1989; Innis andRexstad, 1983; Law, 2008; Musselman, 1994; Robinson, 1994 and Pegden, Shannon and Sadowski,1995). Methods of simplification are the focus of the following section (section 4.1.2) and themethodology incorporates a method to determine the most suitable complexity and detail in thedesign chapters (chapter 6 and 7).4.1.2 Methods of simplificationThe aim of model simplification is to ‘reach a point when the study’s objectives can be addressedby the model, then no further detail should be added’ (Robinson, 2004b pg. 87). To achieve thisthere are some ideas and methods that could be embraced to simplify a model that correspondsto two different types of simplifications presented in Brooks (2007): Type one simplifications - simplifications that are apparent from a good knowledge of the real system. These simplifications can be applied when choosing the initial conceptual model. Type two simplifications – those that are derived from analysis of the model, such as sensitivity analysis, or detailed examination of the model behaviour and could not be seen easily without such analysis.Conceptual modelling concerns ‘type one simplification’ and should avoid as much as possible theneed for ‘type two simplifications’. It is generally regarded that a conceptual model needs to be 82
    • created before a model is implemented. The problem resides in what a modeller constitutes as aconceptual model, how rigorous the analysis was undertaken and how it was adequatelydocumented. Another major factor is that decisions at the conceptual modelling stage can onlybe considered subjectively in relation to the ‘probable’ impact upon model accuracy. Type twosimplifications are as a result of direct experimentation on a computer model. This is, of course,developed from the description of the conceptual model. At the conceptual modelling stagethere are likely to be decisions about the scope and detail that would be difficult for a modeller toqualify. In these cases the only method available for making such decisions is to use a prototypingmethod (e.g. Powell, 1995a; 1995b; Pidd, 1999; Robinson, 2004a; 2004b). This can be classed as a‘type two simplification’ but only on a subset of the model.The literature provides a vast amount of advice and methods on how to simplify a model. Table4.2 lists each of the different types of advice and methods on how to simply a model andreferences each corresponding author. The contributions can be categorised by methods andtechniques for simplifying models (e.g. Morris, 1967; Zeigler, 1976; Robinson, 1994; Chwif, Pauland Barretto, 2006), common advice on model simplifications (e.g. Ward, 1989; Brooks, 2007) andsome guiding principles and criteria on model simplification (e.g. Ward, 1989; Courtois, 1985;Pidd, 1999). The table also adds additional value by distinguishing between the advice andmethods that are applicable for type one and type two simplifications. It shows that the majorityof advice and methods are applicable to type one simplification and thus are candidates forinclusion in a methodology. 83
    • Table 4.28 Research contributions on simulation model simplification (advice and methods) Brooks and Tobias (2000) Robinson (2004a; 2004b; Innis and Rexstad (1983) Chwif, Paul and Barretto Type two simplifications Type one simplifications Thomas and Charpenti 2006; 2008a; 2008b) Yin and Zhou (1989) Musselman (1994) Robinson (1994) Brooks (2007) Zeigler (1976) Morris (1967) Ward (1989) Chick (2006) Pidd (1999) (2005) (2006) Advice on model simplification/methodModelling process: startsimple then add complexity X X X X X X X X X Xand detailMake variables into X X X XconstantsAggregate variables/groupcomponents with certain X X X X X X X X X X Xshared characteristicsRestrict/eliminate variables X X X X X X X X X XStrengthen assumptions X Xand restrictionsExclude/drop unimportant X X X X X X X X X Xcomponents in the modelUse random variables to X X X X Xdepict parts of the modelQuantify relationships(connections) between X X X X X XvariablesLimit the amount ofuncertainty in the X X X X X Xmodel/Reduce randomnessAssume a well-definedobjective function or set ofdecision criteria/ include X X X X X X X Xonly output measures ofinterest (experimentalframe)Split models (divide largermodels into smaller X X X X Xcomponents)/ use morethan one modelUsing analogies X X XProper use of data X X XThe majority of the findings presented in table 4.2 are offered in general and are not specific to aparticular domain although some discussions reside within the manufacturing literature (e.g.Brooks and Tobias, 2000; Thomas and Charpenti, 2005). The advice does not constitute a guide,or set of procedures, to aid in the creation of a conceptual model. For example, there are someimportant questions identified by Robinson (2008a; 2008b) that need to be addressed to build asimple model - When should more detail be added? When should elaboration stop? It can beargued that there is a difference between offering a set of principles, or advice, and guidingsomeone through the process.The other key area includes reducing the scope and level of detail. This can be achieved by either,removing components and interconnections that have little effect on model accuracy, or by 84
    • representing more simple components and interconnections while maintaining a satisfactory levelof model accuracy (Robinson, 2004b). Some of the less cited advice on model simplificationwould also be extremely useful in a supply chain context. For instance, supply chains areinherently complex and simulation is a good method to examine their behaviour. Methods thatare able to reduce complexity (e.g. interconnections between actors and business processes),behaviour and uncertainty in a model would be extremely useful.4.1.3 Modelling frameworksA modelling framework specifies a set of procedures for detailing a model in terms of itselements, its attributes and its relationships (Van der Zee et al., 2008). The procedure laid downin a framework usually provides a conceptual structure to follow in order to complete the purposeof what it is set out to do.There have been some notable attempts to define a framework for conceptual modelling withinparticular domains, or purposes. The earliest work can be found in the military domain byShannon (1975) and more recently, Pace (1999, 2000a; 2000b) has explored a similar approachthat depicts four key stages. Nance and Arthur (2006) identify the potential to adopt softwarerequirements engineering approaches for simulation model development. Additionally, Brooksand Tobias (1996) propose a framework for conceptual modelling but do not expand on the idea.Two contributions have expanded in more detail on a process framework for conceptualmodelling. This includes Robinson (2004a) who described in his text a general process frameworkfor conceptual modeling. Also within the SCM domain Van der Zee and Van der Vorst (2007) havedefined an object-orientated modelling framework. The text ‘Simulation: The Practice of ModelDevelopment and Use’ by Robinson (2004b) provided a large part of the motivation for thisresearch study.The framework proposed by Van der Zee and Van der Vorst (2007) is aimed at an objectorientated implementation of the computer based simulation model (Robinson, 2008a; 2008b).The authors focus on a simulation reference model and library of building blocks to be used in asimulation tool. There is no process described on how to create a conceptual model, rather itprovides a modelling language and simulation tool for exploring supply chain problems. The toolis similar to that provided commercially, such as Gensym e-SCOR (Barnett and Miller, 2000) andSDI supply chain builder (Phelps, Parsons and Siprelle, 2001).Only Robinson (2004a) provides detail on the stages of a conceptual modelling project. Even inthis case Robinson (2006) points out that no guide exists to aid a modeller through how to bring aconceptual model into existence. The work by Van der Zee and Van der Vorst (2007) also does 85
    • not provide a guide on how to create a conceptual model. For instance, the modeller will stillneed to decide which modelling blocks to use from a predefined library and how they are to beconfigured. Therefore it can be concluded that the major weakness of existing approaches is thatthey do not address the ‘how’ and do not comprehensively incorporate existing practice, whichcan aid in the creation of a conceptual model for SCM applications.4.2 Problems encountered in simulation modellingThere are many problems encountered in simulation modelling that would benefit from a greaterunderstanding and usage of following a structured approach to the creation of a conceptualmodel. The problems encountered in general for simulation have been discussed in detail in theliterature (e.g. Law and McComas, 1986; Sadowski, 1989; Musselman, 1994; Ulgen, Gunal andShore, 1996; Carson, 2004). Table 4.3 presents some of these that may relate to, or would havebeen influenced by, the conceptual modelling stage of a simulation project.Table 4.39 Potential pitfalls in simulation related to conceptual modelling Source McComas (1986) Sadowski (1989) Carson, (2004) Musselman, Ulgen et al., Law and (1994) (1996) Potential pitfall related to conceptual modellingNo clearly defined goals and purpose that should be kept in mind for the whole study X X X XNo common understanding on the project scope and goals, questions to be addressed, and even X X X Xnot to be addressed, communicated between the stakeholders in the studyInvolve key decision makers XToo much detail in the model X XAllowing complexity to creep into the model X X XNot defining the ‘real’ problem adequately XToo much time spent concentrating on the model building rather than focusing on the problem X XEnsure that the model is verified and validated (in the context of the computer model) X X XDocument the model and supply evidence of any justifications made X X XNot knowing when to stop XThe value of identifying the potential pitfalls in simulation is to understand how they can beavoided at the conceptual modelling stage. A methodology that aids a user in the creation of aconceptual model should incorporate concepts and procedures that can avoid the pitfallsoutlined. This includes: Ensuring that the ‘real’ problem is adequately described Facilitate communication between the various stakeholders in the project Determine the model content to address the model objectives (e.g. correct scope and detail) 86
    • Incorporating procedures for validating the conceptual model (little discussion was identified regarding the validity of a conceptual model being a pitfall, all discussions related to the computer model, this is addressed in section 4.4) Documentation of the description of the model to be developed so that the model can be represented and communicated Defined and structured set of steps to guide a user through the process and knowing when to stop One of the pitfalls noted above concerns what has been regarded as the central aim of conceptual modelling: determining the complexity and level of detail. Chwif et al., (2000) provide some technical and non-technical (related to human nature) reasons for increasing the complexity of a model. These reasons are listed in table 4.4, along with some comments and considerations of various issues that might arise when evaluating supply chains using a simulation approach. The main consideration that can be identified is that the majority of problems encountered are domain specific (e.g. supply chain actors, processes and activities). This provides some further justification for a domain specific methodology that can address these needs. Table 4.410 Reasons for increasing complexity (some consideration in the SCM domain) Reasons for increasing complexity Consideration of some issues in supply chain simulation (Chwif, Barretto and Paul, 2000) 1) The show-off factor (e.g. incorporating unnecessary detail Difficulties include too many actors represented in the model, supply chain or features to impress processes and activities that do not improve the accuracy of the model. stakeholders) 2) Include all syndrome (e.g. The actors, processes to be included in the model and which inputs can beNon- insecurity of models in what simplified.technical should be included) There are some advanced simulators available for simulation modelling (e.g. Gensym e-SCOR). In the case of e-SCOR it includes a template of all critical SCOR 3) Possibility factor (e.g. making processes and performance metrics. In a supply chain problem it is not necessary to use of computer power) use all the functionality for a given problem but the additional functionality may lead to this principle being disregarded Inability to agree on the conceptual model requirement between stakeholders in the 1) Lack of understanding of the project. In particular in a supply chain context it is difficult to obtain data from real system suppliers in a supply chain due to commercial reasons. Building the model ‘as close to reality as possible’ is difficult in supply chain 2) Inability to model correctly the management as the processes stretch across organisational boundaries and problem (conceptual model) functions. Only the critical processes and workflows between these processes needTechnical to be included. 3) Inability to translate or code The modeller not being totally acquainted with the functionality of simulation correctly the conceptual model software or lack of good programming skills. Additionally, general simulators (e.g. into a computerised model or Witness, Simul8) have been used for manufacturing problems but supply chain lack of simulation problems involve different set of entities, relationships and modelling logic Poorly defined objectives lead to over complex models (Innis and Rexstad, 1983; Yin 4) Unclear simulation objectives and Zhou, 1989) 4.3 Communicating and representing the conceptual model A conceptual model can be represented in different ways (e.g. component list, process flow diagram, logic flow diagram) and is usually communicated as part of a simulation project 87
    • specification. The simulation project specification is the key means of communicating theconceptual model, with the various stakeholders in the simulation study, on how the modeldesign reflects the context of the modelling project in the real world (Robinson, 2004a; 2004b).Essentially, the specification not only includes the conceptual model but also contains projectrelated information. The project management outcomes are not considered in the scope of thisthesis, emphasis is placed upon the creation of a conceptual model.There have been other terms used for documenting the outputs of the earlier stages of asimulation project. It is important that these should not be confused with the common definitionpresented in this thesis on what constitutes a conceptual model (C.f. section 2.4). Earlier work indefining simulation steps may not have necessarily used the term ‘conceptual model’ (e.g. Lawand McComas, 1991; Nordgren, 1995). Musselman (1994) is one of the earliest contributors whodescribed the need for ‘model conceptualisation’. It was after this period that these termsappeared more commonly in work that describes the different stages in a simulation project (e.g.Robinson and Bhatia, 1995; Balci, 1997; Law, 2003).In the earlier work predating the term ‘conceptual model’, Law (1991) described an ‘assumptiondocument’ which is still in use today but has a more specific meaning. The assumptionsdocument becomes the specification for the actual computer model (Nordgren, 1995). It includesinformation on the system operating procedures and control logic data to specify modelparameters and input probability distributions (Law and McComas, 1991). This is beyond thescope of what is now known as conceptual modelling. It can be more clearly aligned with thedocumentation at the ‘model coding’ stage, which directly precedes model building and isconducted after the conceptual model has been created.4.3.1 Simulation project specificationRobinson (2004a; 2004b) details what should be included in a simulation project specification.This includes the background to the simulation study, objectives of the study and the rationale foraddressing the problems using a simulation approach, data requirements, time-scales andmilestones for the study and estimated costs (Robinson, 2004a). The specification is a ‘live’document which is continuously updated in light of some improved understanding of the realsystem, or how it can be represented in a computer model.Other than Robinson (2004a; 2004b; 2006; 2008a; 2008b) it is difficult to identify any evidence ofa ‘simulation project specification’ being described, or cited, in the simulation literature. Thisdoes not necessarily mean that they are not used in practice. It is expected that there needs to be 88
    • some means of communication between the modeller and the client on the purpose of theproject, which should contain details of the model to be developed. The majority of the literaturehas focused on how a conceptual model can be represented, rather than a document which canbe circulated between the stakeholders in the project. The focus of this thesis is primarily on thecreation, documentation and validation of the conceptual model, rather than the project-relatedaspects of a conceptual modelling project.4.3.2 Representing the conceptual modelThe conceptual model can be documented by one or more methods depending upon how themodeller wishes to represent the content of the model. These representations can be included inthe simulation project specification along with any written text that describes the model andprovides rationale for the decisions made in its conceptualisation.Wang and Brooks (2007b) surveyed simulation users asking which methods are used to documenta conceptual model. They identified, in order of preference, those included: process flowdiagram, list of assumptions and simplifications, logic diagrams, component lists, activity cyclediagrams, UML (unified modelling language), text description and visual display. Table 4.5 listseach of these methods in order of preference, adds a description of their purpose and providesexamples of them being used for SCM applications using a simulation approach. 89
    • Table 4.511 Methods used to document CM with examples in the SCM literature Documentation Percentage of Purpose Examples in the SCM literature method participants Hines and Rich (1997); Naim, Childerhouse, Useful way to understand any business Disney and Towill, (2002); Persson and Olhager,Process flow process and show interrelationships (2002); Reiner and Trcka (2004); Hwarng, Chon, 63%diagram between activities in a process (Greasley, Zie and Burgess, (2005); Van der Zee and Van 2009) der Vorst (2007); Taylor, Love, Weaver and Stone (2008)List of A list of assumptions and/or all Bhaskaran, (1998); Petrovic, Roy and Petrovicassumptions and 57% simplifications made during the process of (1998); Persson and Olhager, (2002); Hwarng etsimplifications creating the conceptual model al., (2005) Uses the same standard symbols used in process flow diagrams to represent the Bhaskaran, (1998); Chan and Chan (2005);Logic diagram 31% logic of the model rather than the process Hwarng, et al., (2005) flow (Robinson, 2004a; 2004b) Lists the components to be included in the Bhaskaren, (1998); Lee et al., (2002);Component list 22% model content with some description Changchien and Shen (2002) An activity cycle diagram are used in discrete event simulation to develop theActivity cycle Changchien and Shen (2002); Ryan and Heavey 19% structure of a model (Hill, 1971) bydiagram (2006); Van der Zee and Van der Vorst (2007) identifying how various entities in the model interact through simulated time. Language for modelling object systems Alfieri and Brandimarte (1997); Gunasekaran,UML 14% based on object-orientated modelling (1999); Biswas and Narahari (2004) methods (Evans and Clark, 1998). Mason-Jones and Towill (1999); PetrovicText description 5% Text description of the conceptual model (2001); Jammernegg and Reiner (2007); Taylor et al., (2008) A diagram or figure which displays theVisual display 2% Non-identified conceptual model graphicallyOther 8% N/A N/ANone 5% N/A N/A*Respondents listed more than one methodSource: Extended from Wang and Brooks (2007b) survey of simulation usersMany examples could be identified in the SCM literature of a model being described using aprocess flow diagram and a description of the assumptions and simplifications incorporated into acomputer model. The popularity of a process flow diagram may be due to the widespreadapplicability of the method within the field of operations and supply. Additionally, it is oftenregarded necessary for publications to provide some rationale on how the model was simplified(although most contributions did not provide an explicit list).It was difficult to find many examples in the SCM literature of the other documentation methodsbeing included in research papers, except in the case of activity cycle diagrams. These have beenused as a specific means for representing discrete-event simulation models (Hill, 1971) and havebeen described in detail in Pidd (1998; 2003). Robinson (2004a; 2004b) notes that activity cyclediagrams share some characteristics and sit between process flow diagrams and logic flowdiagrams as all three are visual representations and provide, in part, the logic of a model. In thecase of logic flow diagrams there is some commercially available software which supports themethod (e.g. IGrafx Process 2000, an example is shown in Albores et al., 2006) and activity cyclediagrams can be used when programming a model but not necessarily if using a simulationpackage. 90
    • It is clear that methods are used to represent a conceptual model in SCM applications usingsimulation. For instance, Wang and Brooks (2007b) indicate that only 5% of the respondentsanswered with a response that stated no documentation was used. However, it can be noted thatthere is little attempt in research papers to describe and justify a conceptual model which shouldreceive greater attention to improve the rigour of SCM simulations studies in the future (Manuj etal., 2009).4.4 Validation of conceptual modelsValidation of simulation models has received a lot of attention in the literature. A general view ofmodel validation has been offered by Balci (1994, pg. 2) who states that it refers to ‘substantiatingthat the model, within its domain of applicability, behaves with satisfactory accuracy consistentwith the study objectives’. Alternative definitions have focused upon establishing ‘confidence’ inemulating the real system (e.g. Love, 1980), building it ‘right’, or a ‘correct’ model, for thepurposes at hand (e.g. Pace, 1999; Sargent, 2005; 2008) and similar to Balci (1994) some havebeen more specific by stating that in relation to the terms used it should be ‘significantly accurate’(e.g. Carson, 1986; Robinson, 1997).There have been two prominent focuses in the literature in the area of validating simulationmodels. For instance, Love (1980) suggests that ‘confidence’ in a model utility is a gradual taskthroughout a simulation project as well as at the final testing phases. These views are alsonotable in two recent contributions presented by Law (2008) and Sargent (2005; 2008) at theWinter Simulation Conference. Sargent’s (2005; 2008) discussion concentrates predominatelyupon final testing techniques, whilst Law (2008) presents some key final stage validationtechniques and aligns ideas for building valid models with a traditional view of a simulationproject. The validation techniques presented by Sargent (2005; 2008) are claimed to describe themajority of methods, although he notes, that some differences in definitions may exist (earlierwork has been founded upon key contributions such as Conway, (1963); Herman, (1967) andZeigler, (1976) and has been updated on numerous occasions since 1979. Both papers reflectsome of the more recent key contributions in the area (e.g. Carson, 1986, 2002; Law andMcComas, 2001; Banks et al., 2005).The validation of conceptual models is one area of the literature in which there is little discussion,especially on what constitutes conceptual model validation and which validation tests areapplicable. This is surprising; as Robinson (2008a; 2008b) asserts that the need for conceptualmodel validation is well documented and references Ward (1989) and Sargent (2004), in support 91
    • of this view. More recently, and in the context of SCM simulation, this view is also shared byManuj et al., (2009) as an essential way in which to improve the rigour of simulation studies.There have been some attempts at defining what constitutes conceptual model validation andalso discussions of existing methods that have been applicable. For instance, Sargent (2005, pg.135) suggests that the ‘validation of a conceptual model concerns the determination that theconceptual model is reasonable and correct for the intended purpose of the model’. Robinson(2006, pg. 6) takes a similar view that a conceptual model should be ‘sufficiently accurate for itsintended use’. This is in line with existing convention on model validation but the area in whichthere is a lack of clarity and consensus is in terms of which methods are applicable to fulfil thesedefinitions. The main reason for this is that not all the methods listed and described by Sargent(2005; 2008) and those authors before him (e.g. Banks, Gerstein and Searles, 1988; Kleijnen,1999; Balci, Nance, Arthur and Ormsby, 2002; Carson, 1986, 2002; Law and McComas, 2001;Banks et al., 2005) focus upon the evaluation of a computer model results and/or its behaviour.Two methods have been suggested by Sargent (2005; 2008) for validating conceptual models.These include ‘face validity’ and ‘traces’. It must be noted that there is no support other than inSargent (2005; 2008) for the ‘traces’ method and only Sargent himself uses the term ‘facevalidity’. Sargent (2008, pg. 162) defined these validation tests in relation to the conceptualmodelling stage as: Traces – ‘tracking of model entities through each sub model and overall module to determine if the logic is correct and if the necessary accuracy is maintained. Any errors found in the conceptual model results in revisions and re-iteration through the validation step’. Face validity – ‘individuals knowledgeable of the real system being observed evaluate the conceptual model to determine if it is correct and reasonable for its purpose (e.g. examining the flowchart or graphical model, or set of model equations)’.The traces method requires the relationships between entities to be defined in detail. It is morecommonly associated with the verification procedures for coded models. Using Sargent’s (2005;2008) definition of what constitutes a conceptual model (C.f. section 2.5) he allows for thedetailed logic to be developed. This is outside the scope of the definition of a conceptual modelpresented in this thesis and more widely in the literature (e.g. Pace, 1999; Robinson, 2008a;2008b). What is clear is that both Sargent (2005; 2008) and Law (2008) distinguish between‘problem definition’ resulting in a conceptual model and that of the specification that assures that 92
    • the software design, and any programming, implements the conceptual model satisfactorily in thecomputer system. This detail is specific to a particular software or programming language. Forthis reason alone the tracing method cannot be conducted at the conceptual modelling stage.There is a consensus in the limited number of contributions that highlight the need to involve‘subject matter experts’ in the process of conceptual modelling and to determine the‘correctness’ of the conceptual model as an end validation step (e.g. Pace, 1999; Robinson, 2006;Law, 2008; Sargent, 2005; 2008). Sargent (2005; 2008) is the only author to suggest specifically‘face validity’ in the context of conceptual modelling. Although, the other authors previouslycited do not use this term, they recognise that there is a need to obtain feedback from subjectmatter experts on the conceptual model by checking that it is ‘sufficiently accurate for itsintended use’. Therefore, there is an imperative need to involve the relevant stakeholders in theprocess (discussed in detail in section 5.1 and 6.1) and an expert review as a final validation test.Earlier contributions have concentrated on using hypotheses independent of the model’sprogrammed structure (e.g. Hermann, 1967, Zinnes, 1966). Love (1980, pg. 405) presents a viewof hypothesis testing in light of Hermann’s (1967, pg 223) discussion, as an “examination ofconnections between system elements, so as to determine whether the model reproduces thoserelationships”. For instance Hermann (1967, pg 223) states that ‘If X is observed to bear a givenrelationship to Y in the observed universe, then ‘X should bear a corresponding relationship to Y’in a valid operating model’. Hermann (1967) notes that the hypotheses made about therelationships and entities could be either ‘stated as researchable hypothesises’ (a rationalistview), or even ‘empirically verifiable propositions defined from them’ (an empiricist view).Interestingly, Balci (1986) cites Gass’ and Thompson’s (1980) term ‘theoretical validity’ and notesthat this has been later used by Sargent (1985) under the name of ‘conceptual model validity’.This discussion has shown that only the ‘theoretical validity’ of a conceptual model can be testedat the conceptual modelling stage, as all other tests require either a computer model, or themodel logic to be defined in detail. Hermann’s (1967) term ‘hypothesis validity’ can be used toavoid any confusion in the literature and there is a fundamental need to have an ‘expert’ reviewof the conceptual model (e.g. individuals knowledgeable about the actual system being observed). 93
    • 4.5 Chapter summaryChapter four has identified and reviewed existing practice of conceptual modelling for SCMapplications. This corresponds to completing stage I of the research methodological programmeand addresses the research question that considers how simulation conceptual models arecreated in the context of supply chain applications. This has included, identifying the variousdifferent approaches to conceptual modelling in practice, reviewing the problems encountered insimulation modelling which could benefit from a greater understanding of conceptual modelling,different means of communicating and representing a conceptual model and identifying ways inwhich a conceptual model can be validated.The key findings identified in this review are that there is no methodology for creating aconceptual model. There are many guiding principles offered in the simulation modellingliterature that relate to the conceptual modelling stage, methods for simplification and a limitednumber of frameworks. A review of potential pitfalls (or problems) in simulation studies couldbenefit greatly from successfully completing the conceptual modelling stage as part of asimulation project. There are numerous ways of representing a conceptual model and it isgenerally communicated as a significant component of the simulation project specification.The chapter also argues the importance and need to validate a conceptual model. In this areathere is consensus about the importance and need for validating a conceptual model, but this hasyet surfaced into a significant discussion, of which validation methods may be relevant in theconceptual modelling stage. The discussion proposed a hypothesis test as a credible means ofvalidating a conceptual model, coupled with the need to have an expert review.The review of existing practice is intended to not only demonstrate that no methodologies exist inthe context of the purpose of this thesis, but to consider some of the issues that might need to beincluded in the methodology to be developed. The next chapter builds upon the existing practiceto identify the required specification for the methodology to be developed in this thesis. 94
    • Chapter 5 Forming the specification for the SCM2 (Stage II)Chapter five delivers stage II of the research programme by forming a specification for the SCM2.It explicitly addresses the research question which seeks to identify the required specification for aSCM2. Stage one (detailed in chapter four) along with this stage satisfies the first researchobjective: A documentation of the required specification for a methodology for creating simulationconceptual models for SCM applications. The specification is determined by considering threedifferent requirements: Requirements for creating an effective conceptual model (section 5.1) – The qualities that a conceptual model can be evaluated against are reviewed (e.g. Willemain, 1994; Brooks and Tobias, 1996; Robinson, 2004a; 2004b; 2006; 2008a; 2008b; Brooks 2006) and the fundamental need to ‘keep the model as simple as possible’ is argued. Requirements for a ‘good’ methodology (section 5.2) – The characteristics of a ‘good’ methodology are reviewed using Platts (1994) general criteria and a review of existing methodologies in the SCM and OM domain (e.g. Checkland, 1981; Platts and Gregory, 1990; Bennett and Forrester, 1993; Agarwal and Shakar, 2002; Naim et al., 2002; Bolstorff and Rosenbaum, 2003; Blackhurst, Wu and O’Grady, 2005). Lessons are drawn for developing a methodology for conceptual modelling. Requirements for conceptual modelling of supply chain problems (section 5.3) – Identification of the domain specific needs for conceptual modelling of supply chain management related problems (i.e. the range of improvements, range of supply chain performance measures and setting of supply chain problems).The specification detailing the requirements that the methodology to be developed must meet isdocumented at the end of this chapter (section 5.4). These requirements are compared to themethodology that has been developed and refined with two typical SCM applications to ensurethat each requirement set out in this chapter are met.5.1 Requirements for an ‘effective’ conceptual modelIt is important to detail some requirements for creating an effective conceptual model. Thissection considers the four requirements for a conceptual model using Robinson’s (2004a; 2004b;2008a; 2008b) criteria, and the fundamental need to keep the model as simple as possible (asdiscussed in section 4.1). The purpose of this section is to identify how a methodology can play arole in creating a conceptual model that meets the criteria and is as simple as possible for thepurpose at hand. 95
    • 5.1.1 Four requirements of a conceptual modelRobinson (2004b) identifies four requirements to be considered when creating an effectiveconceptual model. These requirements include the validity, credibility, utility and feasibility of aconceptual model. Table 5.1 lists each of these four requirements and notes the definitionprovided by Robinson (2004b). It also demonstrates that Robinson’s requirements have beensynthesised from previous related discussions. In particular, Willemain (1994) lists five qualitiesfor a good model and Brooks and Tobias (1996) present a more comprehensive and detailed list ofeleven performance criteria. Both Willemain (1994) and Brooks and Tobias (1996) discussions arediscussed in terms of the simulation project as a whole. Only Robinson (2004a) has discussed thespecific requirements of a conceptual model.Table 5.1 Four requirements for a conceptual modelFour requirements of a Definition Other notable contributions in conceptual model (provided by Robinson, 2004b) support of the requirement A perception, on behalf of the modeller, that the conceptual model Willemain (1994); KleijnenValidity will lead to a computer model that is sufficiently accurate for the (1995); Brooks and Tobias (1996); purpose at hand Law (2008) A perception, on behalf of the clients, that the conceptual model willCredibility lead to a computer model that is sufficiently accurate for the purpose Robinson (2004b); Law (2008) at hand A perception, on behalf of the modeller and the clients that theUtility conceptual model will lead to a computer model that is useful as an Willemain (1994) aid to decision-making within the specified context A perception, on behalf of the modeller and the clients, that theFeasibility Willemain (1994) conceptual model can be developed into a computer modelExtracted from Robinson (2004b, pg. 67) along with a note of other contributions in support of eachWillemain (1994) survey of modelling experts identified ‘validity’ as the most importantrequirement of a simulation project. This was followed by utility, feasibility and then, leastmentioned, was credibility (termed ‘aptness’ for the client’s problem). Surprisingly, credibilityreceived little attention but both this criterion and validity concerns the ‘probable’ accuracy fromthe perspective of the modeller (validity) and the client (credibility). The question that needs tobe addressed is whether the conceptual model is an accurate representation of the system understudy (Kleijnen, 1995). More specifically, Willemain (1994) notes that this means that theconceptual model contains all the essential features (e.g. scope and level of detail).Utility is different from both the credibility and utility criteria as it determines whether the actualmodel is ‘useful’. Robinson (2004a; 2004b) points out that different conceptual models could bedesigned which would meet the validity and credibility requirements (it is sufficiently accurate)but may hold different degrees of ‘utility’. This may affect whether the model is understandableby both the client and the modeller, may not be economic in terms of data collection andcomputation, and there are issues concerning the accessibility to those who may wish to use themodel (Gass, 1983). 96
    • Feasibility concerns whether the conceptual model can in fact be developed into a computermodel. More specifically, this concerns the time and cost to build the model (including datacollection, verification and validation), running the model, analysis of the results of the model andany hardware requirements (e.g. computer memory) of running the model (Willemain, 1994;Brooks and Tobias, 1996). At the conceptual modelling stage only a subjective assessment can bemade in relation to how the conceptual model is to be developed into a computer model.The methodology to be developed can be used to build valid and credible models. Validity andcredibility can be incorporated into the methodology as the aim of the process is to create an‘accurate’ representation of the problem at hand. The ‘probable’ accuracy of the model isimproved by formulating the model boundary and determining the most appropriate level ofdetail to address the model objectives from the perspective of both the modeller and client.Feasibility and utility concern the end output: the description of the computer model to bedeveloped. This description is determined after an accurate model boundary and level of detailhas been formulated. It is only at the final stage questions, about how ‘useful’ and ‘feasible’ thedescription is to lead to a computer model, can be addressed. Additionally, the methodology isitself evaluated in the preliminary validation against the Platts (1993) ‘utility’ and ‘feasibility’criteria. For these two reasons, the requirement is limited to only building a valid and crediblemodel in addition to keeping the model simple.5.1.2 Building ‘valid’ and ‘credible’ modelsLaw (2008) contends that there are many ideas for building valid and credible models throughoutthe duration of the whole simulation project. Three of the ideas relate specifically to a successfulcompletion of the conceptual modelling stage. Law (2008) does not use the term ‘conceptualmodel’ but describes that the first stage of a simulation project is to ‘formulate the problem’followed by the ‘documentation of an assumptions document’. The three issues relating toformulating the problem include: 1. Formulating the problem precisely 2. Interacting with the decision-maker on a regular basis throughout the simulation project 3. Interview appropriate subject matter expertsThese are important considerations when designing a methodology for conceptual modelling,particularly to address the domain specific needs for SCM applications. The complexity of supplyproblems, needs and role of stakeholders in the process present a unique set of requirementsspecific in the area of SCM. An ill-defined problem will lead to a model that may have an 97
    • inappropriate scope and level of detail. Additionally, not involving decision-makers andindividuals who hold knowledge about the real system will affect the actual validity of thecomputer model. Therefore, when designing a methodology for creating valid conceptual modelsof supply chain applications, there is a need to explicitly address the need to formulate theproblem based upon an understanding of supply chain problems and the role of stakeholders inthe process.5.1.3 Fundamental need to keep the model ‘simple’It has previously been argued in section 4.1 that there is consensus in the simulation communitythat the central aim is to ‘keep the model as simple as possible to meet the objectives of study’(Robinson, 2004b, pg. 66). The amount of research on the topic was shown to be great for bothguiding principles in modelling (section 4.1.1) and methods of model simplification (section 4.1.2).The majority of which were shown to be applicable at the conceptual modelling stage.There are a number of advantages cited for developing simple models which include ease of useand functionality (e.g. Little, 1970; Innis and Rexstad, 1983; Salt, 1993; Chwif et al., 2000;Robinson, 2004a; 2004b) and greater transparency (e.g. Little, 1970). For instance, a simplemodel is easier to code, validate and analyse, easier to ‘change’ and the time can be seriouslyreduced (Chwif et al., 2000). In terms of the transparency of a model, it needs to be simple tounderstand, at least in outline form and should be easy to manipulate and control (Little, 1970).This is desirable because trust is important between the modeller and the client. This is easier toestablish if the client can appreciate the overall working of the model and understand what themodel can and cannot do and why (Pidd, 2003).There must be a balance between the credibility of a model and its simplicity (Chick, 2006).Brooks and Tobias (1996) point out that if a model is too simple it may be unrealistic and itsresults will be, at best, of little use, and at worst, misleading. Two reasons for this may be due tothe assumptions made about the system and the exclusion of behaviours and/or modelcomponents that will have an effect on model accuracy. The methodology must, therefore,provide a mechanism for deciding the most appropriate complexity and level of detail withoutmaking the conceptual model too simple or complex.5.2 Requirements for ‘good’ methodologiesA methodology can be defined as a repeatable process that can be used, and reused, any numberof times and is made up of best practices, rules, guidelines and templates (Murch, 2005). Theprocess specifically guides a user to an approach and solution that is appropriate to their context 98
    • (Checkland, 1981). In the case of this research, the context is the creation of a conceptual modelfor supply chain applications. A methodology would therefore provide a step by step procedureon how to create a conceptual model in a structured way that can be repeated to obtain morepredictable results each time.Section 4.1 identified that no methodologies exist for creating conceptual models. However, thecharacteristics of methodologies can be established by reviewing existing literature that haspresented methodologies to deal with real-world complexity. Methodologies have beensuggested for general problem solving such as ‘hard’ systems approaches often termed ‘systemsengineering’ and ‘soft’ system approaches (e.g. Checkland, 1981). In the context of operationsmanagement, Platts and Gregory (1990) presented a manufacturing audit in the process ofstrategy formulation and Bennett and Forrester (1993) suggested a methodology for the designand implementation of market-focused production systems. Specifically in the SCM domain,methodologies have focused on diagnosing and improving supply chain performance (e.g.Agarwal et al., 2002; Naim et al., 2002; Wu and O’Grady, 2004; Bolstorff and Rosenbaum, 2003)and selecting supply chain configurations (e.g. Wang et al., 2004; Blackhurst et al., 2005).Platts (1994) suggested the desirable characteristics for successful strategy formulationmethodologies. These include procedure, participation, project management and point of entry,which are broken down in table 5.2. The procedure (or guide) is the key requirement of amethodology which should embed participation, project management and points of entry. Thisincludes suggesting the necessary phases in the methodology and the associated steps. For eachstep in the methodology it should suggest what information is required and what information itprovides with the use of suggested tools and techniques to perform each step. These stages andsteps should follow a logical process, so the points of entry should be defined based on meetingclearly defined expectations. Platts (1994) also notes that a methodology should provide projectmanagement characteristics such as the resourcing needs and constraints with agreed timescalesfor completion. 99
    • Table 5.2 Platts (1994) characteristics of successful strategy formulation methodologies Procedure Participation Project management Point of entryWell defined stages of: Individual and group to Adequate resourcing, identify: Clearly defined expectations; achieve: Understanding and agreementGathering information; Managing group; of managing groupAnalysing information; Enthusiasm; Supporting group;Identifying improvements; Understanding; Operating group Commitment from managing Commitment and operating groupsSimple tools and techniques; Agreed timescaleWritten record Workshop style to: Agree objectives; Identify problems; Develop improvements; Catalyse involvement; Decision making forumSource: Platts (1994)Existing SCM methodologies focus heavily on the procedure and neglect participation, projectmanagement and point of entry characteristics. This observation was also made by Benton (2009)who reviewed methodologies and frameworks for selecting supply chain architecture.Methodologies such as Naim et al., (2002) and Bolstorff and Rosenbaum (2003) suggested a rangeof tools and techniques (e.g. process mapping, cause and effect diagrams) for specific steps,described how individuals or groups achieve each step and provided some project managementconsiderations. Methodologies such as Blackhurst et al., (2005) and Agarwal et al., (2002) arelimited to a particular tool (e.g. probability applied trans-nets; analytic network process basedmodels) and do not attempt to suggest project management needs and points of entry.The characteristics of procedure, participation and point of entry are also relevant in amethodology for creating a conceptual model. Significant research activity and testing with actualusers would be required in order to incorporate project management characteristics, so this isomitted as a requirement for the purposes of this thesis. A procedure will define each of thestages necessary to define the problem and create a conceptual model of that problem, usingappropriate tools or techniques. It should make explicit how the modeller and the client interactto complete each step of the defined procedure. Additionally, clearly define the expectationsfrom each step and how each step should be entered after the completion of a previous step.5.3 Requirements for conceptual modelling of supply chain problemsThis section identifies the specific requirements for conceptual modelling of supply chainproblems. The role of the methodology is to capture the complexity and detail of supply chainproblems for a given objective within the supply setting. The implications of this is to define whatcomplexity and detail needs in the context of SCM, the range of objectives to measureperformance and how the interconnections between them and the supply setting can be 100
    • determined. The characteristics underlying theses requirements are identified from discussions inthe general supply chain literature and particularly in simulation contributions.5.3.1 Handle the complexity and detail of supply chain improvementsThe methodology needs to be able to handle the complexity and detail of supply chain problems.This is important when describing an improvement within the supply setting of the problem.Firstly, complexity is described followed by the detail of supply chain improvements.5.3.1.1 Complexity of supply chain improvementsA previous discussion argued that supply chains are inherently complex and dynamic systems (C.f.section 2.2). A review of the literature in general for SCM and, specifically, for simulationapplications, demonstrates that there are a number of discussions that describe the complexity ofa supply chain as a system. These characteristics are shown in table 5.3 along with the authorsthat have described complexity for SCM applications and secondly specifically in the simulationliterature.On the whole definitions or discussions of complexity in a SCM context capture the complexity ofmultiple firms, multiple business activities, and the coordination of those activities across businessfunctions and actors in the supply chain. Beamon (1998) describes a definition for supply chaincomplexity suggesting it arises from the number of echelons in a chain and the number offacilities in each echelon. This is supported by Cooper et al., (1997) who distinguish between thehorizontal length and vertical height of a supply chain. Both these definitions describe the size ofthe supply chain structure but do not consider the linkages between supply chain actors andbusiness processes. Harland (1996; 1997) does offer a definition for the different types oflinkages in the supply chain which refers to the integration of supply activities within firms, indyadic relationships, in chains of firms and in inter-organizational networks. Common to all theselevels is the flow of supply and the activities and decisions associated to that flow.The supply chain business processes have also been described in the literature, most notably insupply chain process reference models (e.g. Supply Chain Council SCOR model, 2008; GlobalSupply Chain Forum framework presented in Croxton et al., 2001). These include processes forplanning, executing (i.e. source, make, deliver and return) and governing a supply chain. Forinstance, SCOR defines five different process types: source, make, deliver and return for theinformation flow and physical flow and plan to co-ordinate the other four (SCOR, 2008; Perssonand Araldi, 2009). 101
    • The SCM2 will need to be able to describe a complex supply problem and formulate the boundaryof the model in its supply setting which will include: Size – number of tiers of actors in a supply chain (horizontal length) and number of suppliers and customers within each tier (vertical height) Business process types – describe the range of supply chain planning, execute (e.g. source, make, deliver and return) and govern process types Linkages between actors and business processes – describe the linkages between actors and business processes 102
    • Table 5.3 Identification of the complexity of a supply problem Support in the general SCM Support in the SCM simulation literature literature Characteristic Property 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9Horizontal complexity length - number of tiers X X X X X X X X X X X number of suppliers andVertical complexity X X X X X X X X X X customers within each tier number of operating locations or degree ofSpatial complexity X dispersion among members within the systemEdges connecting the Number of edges X Xnodes connecting nodes level of coupling between firms in the supply networkintangible measure of as evidenced in the Xcomplexity closeness of working relationship Supply within the firm boundary; supply in a dyadic relationship; supply in aLevels of supply X X X inter-organizational chain; supply in an inter- organizational network convergent (assembly); divergent (arborescent);supply chain structure X conjoined; general (network) managed business process links; monitored businessProcess links among process links; not managed X Xsupply chain partners business process links; non- member business linksTypes of supply chain Primary; secondary Xpartnership Information flows; productsupply chain flows X flow Logistics; marketing & sales;Business functions finance; R&D; production; & X X X X purchasing Customer relationship management; customer service management; demand management;Supply chain business order fulfillment; X X X X X X X X X Xprocess types manufacturing flow management; procurement; product development and commercialization; returnsUncertainty N/A Xtechnological intricacy N/A Xorganizational systems N/A XDynamic complexity Dynamic; static XSources: (1) Harland et al. (1999); (2) Cooper et al. (1997); (3) Lambert, Cooper and Pagh, (1998); (4) Milgate (2001); (5) Choi and Hong(2002); (6) Webster (2002); (7) Supply Chain Council (2008); (8) Value Chain Group ( 2008); (9) Holweg and Helo (2008); (10) Vlajic, vander Vorst and Hendrix (2008); (11) Weaver, Love, and Albores. (2008); (12) Stewart (1997); (13) Beamon (1998); (14) Beamon (1999);(15) Beamon and Chen (2001); (16) Min and Zhou (2002); (17) Gardner and Cooper (2003); (18) Reiner and Trcka, 2004; (19) Tang et al(2004); (20) Weaver, Love, and Albores (2007)5.3.1.2 Detail of a supply chain improvementA supply chain improvement can also be described by the level of detail required to implementthe improvements within its supply setting. Generally in the simulation literature, a definition for‘detail’ is rarely attempted (Brooks and Tobias, 1997) but discussions do exist in general, mostnotably in supply chain process reference models which describe supply chain business processes. 103
    • Interestingly, the advent of the SCOR model has led to a number of simulation applications usingthe reference model to evaluate supply chain problems using simulation and develop simulationtools specific to the SCM domain (e.g. Van der Zee and Van der Vorst, 2005; Albores et al., 2006;Persson and Araldi, 2009). These discussions of detail of supply chain improvements are shown intable 5.4.Table 5.4 Identification of the detail of supply chain improvements Supported in the Support in SCM simulation literature general SCM literature Property Sub-properties 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Impact of customers’ variability on internal operations; generalLevel of product and customer Xaggregation categories; interactions with customerSupply network Level 0 supply network Xdecomposition architecture level 1 strategic processes; levelProcess 2 tactical processes element;decomposition and level 3 operational processes or X X X X X X X Xdetail task; level 4 activities; level 5 actionsdecision making Strategic; tactical; operational X X X X X X X XlevelsSources: (1) Lampel and Mintzberg (1996); (2) Cooper et al. (1997); (3) Stewart (1997); (4) Supply Chain Council (2008); (5) Value ChainGroup (2008); (6) Beamon and Chen (2001); (7) Van Landeghem and Persoons (2001); (8) Kleinjnen (2005); (9) Barnett and Miller(2000); (10) van der Vorst et al. (2000); (11) Gardner and Cooper (2003); (12) Tang et al (2004); (13) Kleinjnen (2005); (14) Weaver,Love, and Albores (2008)Brookes and Tobias (1997) suggest, when applied to a model, the level (or amount) of detailusually means an assessment of the extent to which the observable system elements and theassumed system relationships are included in the model. A model is said to be detailed if itcontains most of the elements and interactions thought to exist in the supply system beingmodelled. There are two different alternative views that are common in the literature, one is ofprocess decomposition and the other includes different decision-making levels. The differentdecision-making levels do not necessarily easily transcribe into the elements that constitute asupply system but this is clearly possible when decomposing business processes.The detail of a supply chain problem can be described using the different levels of processdecomposition. SCOR describes the system elements in terms of business processes at differentlevels of detail. Although it does not attempt to describe a supply chain structure, it is used todescribe a supply problem from an organisation perspective connected by business processesacross a supply chain. This is unique to a supply chain problem because it captures the complexityof the actors, business processes and linkages that were previously described. 104
    • The SCM2 will need to be able to describe the detail of the components in the scope of the model.SCOR can be used to describe the different level of process decomposition (Supply Chain Council,2008). However, Weaver et al., (2008) notes that SCOR does not attempt to describe theconfiguration of actors in the structure of a supply chain. This is an important consideration whenevaluating supply chain problems so this level is added to those offered by SCOR: Level 0: Supply network composition - Structure of the supply chain (e.g. actors) Level 1: Business process types – Describes the role an actor plays in a supply chain Level 2: Process categories – Describes an organisations operation strategy through the configuration they choose for the supply chain Level 3: Process elements – Describes how an organisation ‘fine-tunes’ its operations strategy Level 4: Implementation level – Describes the activities and actions required to implement specific supply-chain management practices that are unique to an organisation5.3.2 Address a range of supply chain objectivesA supply problem is not only described by its complexity and detail but by how it will bemeasured. These performance measures are determined by the type of performance attributeand the detail necessary to evaluate a problem (Weaver et al., 2007). A review of currentdescriptions of supply chain performance measures by Shepherd and Gunter (2006) argued therehave been relatively few attempts to systematically collate measures for evaluating theperformance of supply chains. They identified the following groups of discussions: Whether they are qualitative or quantitative (Beamon, 1999; Chan and Qi, 2003) What they measure: cost and non-cost (Gunasekaran et al., 2001); quality, cost, delivery and flexibility (Schonsleben, 2004); cost, quality, resource utilization, flexibility, visibility, trust and innovativeness (Chan and Qi, 2003); resources, outputs and flexibility (Beamon, 1999); supply chain collaboration efficiency; coordination efficiency and configuration (Hieber, 2002); and, input, output and composite measures (Chan and Qi, 2003) Their strategic, operational, or tactical focus (Gunasekaran et al., 2001) The process in the supply chain they relate to (e.g. Chan and Qi, 2003; Huang et al., 2004; Li et al., 2005b; Lockamy and McCormack, 2004; Stephens, 2001)The supply chain management literature particularly, simulation related, focus upon a single or asmall number of performance measures necessary to evaluate a supply problem. Beamon (1999)suggested a comprehensive performance measurement system should place emphasis on three 105
    • separate types of performance measures: resource measures (generally cost), flexibility measures (generally customer responsiveness), and output measures (how well the system reacts to uncertainty). The Supply Chain Council SCOR model v.9 (SCC, 2008) have described a 166 page detailed document which cover Beamon’s (1998) categories, resource measures (i.e. cost/expenses, assets/utilisation); flexibility (i.e. the same) and output (i.e. responsiveness, reliability) which are further broken down into three levels of decomposition and link to business practices. They also cover the categories described above, identified by Shepherd and Gunter (2006). Performance measures are deployed within each business unit and internal business process (Bititci, Mendibil, Martinez and Albores, 2005). However, it is important to note that some measures consider the overall supply chain (e.g. total supply cost of all actors, Beamon, 1998) but there is relatively little discussion on pan-supply chain measures. For instance, Stone and Love (2007); have suggested the need for further research into a performance metric framework that includes measures for the impact of overall supply chain performance. The SCM2 will need to be able to describe the range of supply chain performance attributes at the required level of detail both within the business process, business unit and across the whole supply chain. 5.3.3 Identify interconnections with the supply setting The model boundary is formulated by identifying the interconnections between the components that describe the improvement and objective. This is the considerable challenge for conceptual modelling and is one of the aims in the outline design chapter. A method that could identify the critical and necessary links between the improvement and objective is at the heart of the methodology. 5.4 Chapter summary The chapter has considered three aims and their associated requirements that should be incorporated into a methodology for simulation conceptual modelling of SCM applications. These aims and requirements are shown in table 5.5. 2 Table 5.5 Aims and requirements for the SCM Aim Requirement Keep the model as simple as possible;1 Meet the requirements for an effective conceptual model Build valid and credible models Provide a procedure;2 Meet the requirements of good methodologies Note who should participate in each step; Embed points of entry Handle the complexity and detail of supply chain problems; Meet the requirements of the requirements for conceptual Address a range of supply chain objectives;3 modelling of supply chain problems Identify interconnections in the model and within the supply setting 106
    • The first aim of the methodology to be developed is to meet the requirements for an effectiveconceptual model. There are two key requirements identified. One is a fundamental need tokeep the model as simple as possible and the other is to build valid and credible models. To builda simple model for the purpose at hand was shown to have wide acceptance and importanceplaced within the simulation community. Similarly a conceptual model must be an ‘accurate’description of the computer model to be developed from both the client’s and modeller’sperspective.The second aim is to meet the requirements of a ‘good’ methodology. Three requirements wereidentified which included the need to provide a procedure, note who should participate in eachstep and points of entry need to be embedded. It was identified that a structured procedure forcreating a conceptual model of SCM applications is at the heart of this thesis, participation andpoints of entry need to be included in the procedure.The final aim is to meet the requirements of what constitutes a supply chain problem. Threerequirements were identified which include being able to handle the complexity and detail ofsupply chain improvements; address the range of supply chain objectives and identify theinterconnections in the model and within the supply setting. 107
    • Chapter 6 Outline design for the SCM2 (Stage III)The outline design contributes to the development of the SCM2 (stage III of the methodologicalprogramme) and addresses one part of research objective two (to develop a methodology forcreating a simulation conceptual model for SCM applications). It builds upon the previousforming of the required specification (stage II presented in chapter 5) and existing conceptualmodelling practice (stage I presented in chapter 4) in order to present an outline design for theSCM2.The core proposition argued in this chapter is that a methodology provides a guide that can befollowed by the participants, to create an effective conceptual model, with the aid of a SupplyChain Operations process Reference model (SCOR). This encapsulates the primary contribution toknowledge presented in this thesis. This is developed by considering the key design issues foraddressing the requirements that the methodology needs to meet (presented in chapter five).This includes: Design issues for developing a ‘good’ methodology (section 6.1) – Discusses a general guide for conceptual modelling (section 6.1.1), whom the participants are and how they are involved in the process of conceptual modelling (section 6.1.2) and the points of entry (section 6.1.3). Design issues for developing an ‘effective’ conceptual model (section 6.2) – Discusses ideas for incorporating existing simplification advice and methods into the methodology (section 6.2.1) and how a methodology can aid in the documentation and validation of a conceptual model (section 6.2.2). Design issues for the domain specific needs for creating a conceptual model (section 6.3) – Discusses the importance and role of domain knowledge in the creation of a conceptual model. Using SCOR for conceptual modelling (section 6.4) – Discusses the opportunity of using SCOR as one source of domain knowledge to enable a more efficient and focused process.The aim of the discussions is to synthesise a set of key concepts that can be incorporated into thedesign of the methodology (section 6.5). Each of the key concepts is linked to a general processfor conceptual modelling so that the specific phases for a conceptual modelling methodology thataddresses the needs of the SCM domain can be proposed. The chapter concludes with an outlinedesign of the methodology to be further refined through application in chapter seven. 108
    • 6.1 Design issues for developing a ‘good’ methodologyThis section discusses the design issues for developing a ‘good’ conceptual modellingmethodology for SCM applications. Firstly, a small number of contributions that provide a guidefor conceptual modelling are reviewed to identify some general stages (section 6.1.1). Secondly,the different roles participants play in the context of conceptual modelling are identified (section6.1.2) followed by the points of entry (section 6.1.3).6.1.1 General guide for conceptual modellingThe process of conceptual modelling must address ‘how’ a modeller could create a conceptualmodel. The approaches discussed in chapter four do provide some useful guidance on the generalstages for conceptual modelling but have two distinct weaknesses. This includes that they do notnecessarily answer the question of ‘how’ a conceptual model should be created (Robinson, 2004a;2004b) and are not described in much detail.Robinson (2004a) offers the most detailed set of stages supplemented within an outline of someguiding principles for each stage. These general stages are listed in table 6.1 in contrast to thestages identified in earlier contributions by Shannon (1975), Robinson and Bhatia (1995) and Pace(1999; 2000a; 2000b). The table includes some comments on the purpose and applicability ofeach stage in the context of conceptual modelling for SCM applications. 109
    • Table 6.1 Proposed stages for conceptual modelling in general suggested in the literature Author Purpose and applicability in the context for Robinson and Pace (1999; Robinson SCM applications Shannon (1975) Bhatia (1995) 2000a; 2000b) (2004a) Collect Developing an authoritative Gain sufficient understanding of the real world Identifying the understanding of information on problem from the perspective of the client (e.g. problem the problem the problem supply chain improvement and supply setting) situation domain Specification of Determine the Set the Define the objectives to evaluate each supply the model modelling objectives chain improvement purpose objectives Robinson (2008a; 2008b) provides a definition of the experimental factors and reports which can be contextualised for the purpose of modelling SCM applications: The model must Define the Design the accept the experimental factors determined experimental conceptual from the modelling objectives and general factors and model: inputs project objectives (i.e. supply chainConceptual modelling stage reports and outputs improvement). Also, provide the responses that determine the achievement of, or reason for failure of the defined objectives (i.e. impact of the improvement on supply chain performance measures) (Robinson, 2008a; 2008b). Identify entities Specification of and processes the model that need to be component’s represented Specification of Determining the boundary of the model and the parameters Identify level of detail required to represent the actual Determine the Design the and variables simulation practices to be included in the model. The scope and conceptual associated with elements model components and interconnections can level of the model: the the components then be determined from a description of what model model content Specification of should be included at the required level of Identify the relationships detail. relationships between the between components, simulation parameters and elements variables Collect and analyse data Provide a Not within the scope of conceptual modelling project specificationTable 6.1 demonstrates that there are some differences between the stages described byRobinson (2004a) and the contributions published before it. Two possible reasons for this includethat the earlier work has a narrower focus upon what constitutes a conceptual model and areweighted towards the modeller’s perspective on the process. For instance, the earlier workconcentrates upon the design of the ‘model content’, while Robinson (2004a) also placesattention on defining the problem and designing the experimental factors and reports. Evencomparing Robinson and Bhatia (1995) and Robinson (2004a), later framework demonstrates howRobinson shifted from recognising the ‘problem definition’ stage in a simulation project to what isnow widely known as ‘conceptual modelling’.All the contributions demonstrate that the heart of conceptual modelling is determining thecontent of the model (i.e. scope and detail). Shannon (1975) and Pace (1999; 2000a; 2000b)considered this from the modeller’s perspective (i.e. description of the model components and 110
    • relationships between them). This assumes that the necessary scope and detail is determined inthe description but this does not address ‘how’. The significance of Robinson’s (2004a) work isupon the analysis that needs to be undertaken prior to describing the model components andtheir relationships, which has received insufficient discussion in the literature. There are anumber of principles that have been discussed by Robinson (2004b, pg. 84) when designing themodel content: The starting point when designing the model content is to recognise that the ‘model must be able to accept the experimental factors and to provide the required responses’ Scope of the model ‘must provide a sufficient link between the experimental factors and responses’. The meaning of significant being defined by the ‘level of model accuracy required’. Level of detail ‘represents the components defined within the scope and their interconnections with other components of the model with sufficient accuracy’ Both scope and detail are considered in light of the ‘impact upon the models responses’The principles identified have implications for the development of the procedure to be designedin this thesis. This includes how a problem should be identified; the improvement and objectivedefined and from these descriptions, identify sufficient and critical links between the modelcomponents and those in the supply setting. The latter places emphasis on the formulation of themodel boundary using a guide to follow some predetermined decision rules.6.1.2 Role of participants in the process of conceptual modellingOrmerod (2001) provides a description of the different groups and the role that each play in asimulation project. This includes the doers (the interveners such as policy makers), done forclients, with project team members, done to those interviewed about the real world system anddone without members of the organisation and society who are not involved in the project, butare affected by it (Ormerod, 2001).In the context of the conceptual modelling stage of a simulation project, three of Ormerod’s(2001) roles are of critical importance. Firstly, the ‘modeller’ is responsible for creating theconceptual model; secondly, the ‘client’ is the problem owner and recipient of the conceptualmodel; finally, the subject matter experts ‘SMEs’ are consulted to provide the domain knowledgenecessary to understand the supply system and to design the model content.A methodology for conceptual modelling would need to aid a modeller to create the conceptualmodel (for the benefit of the client) from the domain knowledge provided from subject matter 111
    • experts. Pritsker (1995) argues that the client and subject matter experts should be thoroughlyinvolved in a simulation project. This view is also is supported by Banks et al., (2005) who add thatsuch involvement increases the confidence and application of the model by its eventual user, inparticular, greater ‘confidence’ can be placed in the ‘credibility’ of the model representing the realworld problem to a desired level of model accuracy.Conceptual modelling requires a set of skills and experience to both create a conceptual modeland to facilitate interactions between the different participants involved in the process. Theseexperiences and skills are learnt generally through training and education but more importantlypractice and mentoring (Carson, 2004). A gap exists between the skills and experience held by themodeller and the domain knowledge held by SMEs. The methodology, to some extent, can bridgethis gap by providing a detailed procedure and identify ways to incorporate existing domainknowledge from published sources (explored in more detail in section 6.4).6.1.3 Points of entry in the methodologyThe methodology is entered when a client has a supply problem to be evaluated using asimulation approach. The description of the problem is extracted from the client, who may haveundergone a supply chain redesign initiative (e.g. Bolstroff and Rosenbaum, 2003), to identifyimprovements that could bring about a desired change in the supply system. Robinson (2004a)notes that the accuracy of this description may be dubious, so the first phase of the methodologywill need to acquire a sound understanding of the cause and effect relationships between theimprovement and objectives within the supply setting.Robinson (2004a) also suggests that if the client has a poor grasp of a problem a number of moreformal problem structuring methods might prove useful (e.g. soft systems methodology,Checkland, 1981; cognitive mapping, Eden and Ackermann, 2001 and casual loop diagrams,Sterman, 2000). There have also been some more specific approaches suggested that have usedthe methods listed in the context of simulation (e.g. Balci and Nance, 1985 and Lehaney and Paul,1996).The process of conceptual modelling is iterative (Robinson, 2004a). The reason for this is that theprocess itself is one of learning and refinement. Section 6.2.2 discusses incorporating validationchecks within the methodology to ensure that a phase has been completed successfully beforeproceeding. It also discusses the need for a draft description of the conceptual model to becreated so that it can be validated in full. This will enable the participants to re-enter a specific 112
    • phase in the methodology if any adjustments are required because of any new knowledgeidentified during the process.6.2 Design issues for creating an ‘effective’ conceptual modelThis section discusses the design issues for creating an ‘effective’ conceptual model. The first setof issues concern how the methodology can address the principle ‘keep the model as simple aspossible’ by incorporating the advice and methods on model simplification identified in chapterfour where possible (section 6.2.1). The other set of issues further develops the argument that amethodology should aid in the creation of a conceptual model that is both valid and credible(section 6.2.2).6.2.1 Keep the model as ‘simple’ as possibleA central aim of the methodology is the ‘keep the model as simple as possible’ in particular bychoosing the most appropriate complexity and level of detail. A host of advice and methods forsimplifying a model was identified in chapter four, which could be incorporated into themethodology. Each of the advice and methods on model simplifications is listed in table 6.2along with some ideas for how they can be incorporated into the methodology. 113
    • Table 6.2 Incorporating model simplification advice and methods into a methodology Advice on model simplification/method Ideas for incorporation into the methodology (From table 4.2) The process can be initiated with a description of the improvement, objectives and general supply setting. The interconnections between the components that representModelling process: start simple then add an improvement and provide the data sources for an objective are ‘core’ components.complexity and detail The model boundary can be determined by considering the interconnections within the supply setting based upon some decision rules. The core components that have been identified have interconnections between them. Interconnections can be simplified (i.e. treated as a fixed value) if the source componentMake variables into constants does not impact upon model behaviour. A fixed value indicates the model boundary as no further interconnections need to be considered. Aggregation provides a means for reducing the level of detail (Robinson, 2004a). ThisAggregate variables/group components can be considered when specifying how the model components represent each actualwith certain shared characteristics practice. Variables can be eliminated if they do not have an effect on model accuracy orRestrict/eliminate variables simplified with random variables. Assumptions and restrictions are incorporated into the description of the modelStrengthen assumptions and restrictions components. This is a key purpose when representing actual practices with the model components. Components can be omitted if they have little effect on the accuracy of the model; it is aExclude/drop unimportant components in form of scope reduction (Robinson, 2004a). This is considered when determining thethe model model boundary. Each variable needs to be considered for inclusion in the model boundary in terms of itsUse random variables to depict parts of effect on the accuracy of the model. If it can be fixed, or treated as a simple distributionthe model then there is no requirement to consider its interconnections with the supply setting.Quantify relationships (connections) See ‘restrict/eliminate variables’between variablesLimit the amount of uncertainty in the Simplify inputs to the model if they can be generated in a simplified form (e.g. simplemodel/Reduce randomness distributions). This is determined when formulating the model boundary.Assume a well-defined objective function Identify in the first stage the improvement and how it will impact upon the desiredor set of decision criteria/ include only objective within its supply setting. This forces the modeller to explicitly consider theoutput measures of interest (experimental experimental frame.frame)Split models (divide larger models into This is a way of making an individual model run faster and different parts of the modelsmaller components)/ use more than one can be developed by different modellers (Robinson, 2004a). This is outside the scope ofmodel conceptual modelling. Extracting the required domain knowledge is a critical consideration throughout theProper use of data process of conceptual modelling.It is clear that a methodology could incorporate, on the whole, the ideas noted in table 6.2. Theseideas can be grouped into four themes. Two focused upon describing the problem adequatelyand extracting the required domain knowledge (both ideas are considered in more detail insections 6.4 and 6.5). A third considers how actual practice is represented by the components ofthe model by incorporating assumptions and simplifications. The fourth theme sheds more lighton ideas to determine the model content.It is previously noted that Robinson (2004a) has offered some principles for determining themodel content but no formal methods. One aspect of this is to determine the model boundary,which in the context of SCM application would concern considering the interconnections between‘core’ components and those that are deemed ‘critical’ in the supply setting. To implement thisprinciple and respect the need to keep the model as simple as possible the following need to beconsidered: 114
    • 1. ‘Core’ components to be included in the model can be identified from the improvement and objective 2. Components that provide a source interconnection are effectively ‘candidates’ for possible inclusion 3. Candidate components can be ‘promoted’, thus included in the model if the input generated will affect model behaviour by significantly impacting upon the objectives of study (and excluded if they do not affect model behaviour) 4. Included components can be considered for simplification if they can be represented as either a fixed value or simple distribution 5. Simplified inputs represent the boundary of the model as no further inputs can be considered6.2.2 Creating a ‘valid’ and ‘credible’ conceptual modelIn chapter 4 (c.f. sections 4.3 and 4.4) existing practices were considered in relation todocumenting and validating a conceptual model. It was argued that validation is of particularimportance in simulation modelling but there is a lack of methods, or even advice, at theconceptual modelling stage. The methodology can also incorporate a means for documenting andvalidating a conceptual model, adding considerable value to the procedure.Table 6.3 lists the general stages for conceptual modelling as proposed by Robinson (2004a;2004b) and describes the requirements for documenting the output from each stage and how itcould be validated. The table lists Robinson’s (2004a; 2004b) framework because it is the mostdetailed and comprehensive and consults Shannon (1975) and Pace (1999; 2000a; 2000b) toidentify desired outcomes for each stage. The documentation and validation requirements areconsidered for delivering each outcome in the context of evaluating supply chain problems. Thisis useful as the general frameworks consulted do not explicitly state how to document theoutcomes from each stage and none have incorporated validation considerations. 115
    • 2Table 6.3 Documentation and validation requirements for the SCM General conceptual What are the requirements for modelling stage documenting the desired outcome Desired outcomes How can it be validated? (Robinson, 2004a; from each stage in the context of 2004b) SCM applications? Description of the objective of study Description of the Check the description of the supply problemDeveloping an Description of how the problem situation is correct for the purpose at hand from theunderstanding of the supply system is to be from the clients client’s perspective. The proceedingproblem situation improved perspective analysis is dependent upon this information. Description of the supply setting Description of how eachDetermine the process is to provide data Check the description of how the objectivemodelling objectives used to calculate each is to be measured and how an improvement(and how each Model objectives objective is to be represented for ‘correctness’ withimprovement is to be Description of how each SME’s.represented) process is to represent each improvementDesign the conceptual Experimental List of model processes and source Check with SME’s that the processes andmodel: inputs and factors; inputs that provide an source inputs are correct for the purpose atoutputs Responses interconnection hand. Check with SME’s that the actual practices to be modelled are sufficient and correct Description of the components (i.e. no critical omissions or unnecessary that represent each actual practice details). If these are not correct then it and how their relationships would not be possible to convert the actual practices into the model components and Model components; interconnections.Design the conceptual Relationship Check how each model component andmodel: the model Description of the actual practices between their interconnections provide a sufficientcontent to be modelled and their components and correct link between the objectives and relationships improvements. Check with SME’s that the assumption and Description of any assumptions or simplifications incorporated into the simplifications incorporated into conceptual model are correct, without the model components and significantly impacting on the probable interconnections model accuracy.The descriptions provided by each of the stages of conceptual modelling need to be validatedduring the process to improve the validity and credibility of the conceptual model. It isparticularly important to note that the process of conceptual modelling is sequential; certainoutputs are required to initiate and drive the analysis (e.g. model boundary is derived frommodelling a description of the improvement and objectives). Iteration is necessary when issuesarise regarding the ‘correctness’ of the outputs documented in each stage. Using the descriptionsprovided in table 6.3 six issues can be distinguished that could invalidate a conceptual model: 1. Incorrect description of the supply problem from the perspective of the client 2. Incorrect description of the improvement and the performance measures and how they are to be included and represented in the model 3. Incorrect information obtained from the client on the processes and interconnections to be modelled 4. Incorrect description of actual practices to be modelled (e.g. issues that concern the scope, such as any omissions, or unnecessary details) 116
    • 5. Incorrect description of how each actual practice is to be modelled by the components in the model and their interconnections (e.g. issues that concern the detail and assumptions and simplifications incorporated into the model) 6. Insufficient links between the critical interconnections between the improvements and the objectives of study6.3 Design issues for the domain specific needs for creating a CMA procedure for conceptual modelling would need to obtain information provided from sourcesthat are specific to a particular domain. Section 6.1.2 noted that domain knowledge is acquiredprincipally from consultation with SMEs. This section of the thesis firstly argues that a supplychain process reference model is a complimentary source that could provide an opportunity toenable a more efficient and focused SCM2 (section 6.3.1). Secondly, SCOR is identified as asuitable process reference model from the range of alternatives (section 6.3.2) before itsusefulness is described in more detail in section 6.4.6.3.1 Opportunities to use a process reference model for creating a CMA process reference model represents a class of domain (Becker et al, 2003) that can be used toaccelerate the development of a model (Fettke and Loos, 2003). Table 6.4, identifiesopportunities to use a process reference model to enable a more efficient and focused conceptualmodelling process against the requirements. The main advantage offered by a process referencemodel is that it provides standard language and content to describe a supply chain configuration.Three significant opportunities exist when formulating the problem precisely; gatheringinformation from client sources and to focus interaction between the stakeholders in aconceptual modelling project.Table 6.4 Role of domain knowledge in conceptual modelling Objective Requirement Opportunity to make the process more efficient and focused Build the most simplest Use domain knowledge to extract an accurate representation of the model for the purpose at real system for the purposes at hand1. Meet the requirements for an handeffective conceptual model Aid in formulating the problem precisely from the client’s Build a valid and credible perspective and focus consultations with SMEs to determine the model model content (Law, 2008) Steps and information requirements can be described using Procedure standard terms and language common to the SCM domain2. Meet the requirements of Procedure that suggests how the participants should be involvedgood methodologies Participation the process and what information is required from them. Point of entry N/A Supply chain3. Meet the requirements for Standard language and terms for describing a supply problem and improvementsconceptual modelling for SCM the details necessary to analyse the interconnections between Supply chain objectivesapplications ‘core’ components and those in its supply setting. Supply chain setting 117
    • 6.3.2 Identification of a suitable process reference model for creating a CMLambert et al (2005) evaluated process-orientated supply management frameworks, identifyingfive supply chain management frameworks, which recognise the need to implement businessprocesses in the literature. These include Bowersox, Closs, and Stank (1999); Cooper, Lambert,and Pagh (1997); Mentzer et al., (2001); Srivastava, Shervani, and Fahey (1999) and the Supply-Chain Council (2005, note this research uses SCOR v.9, 2008) which all hold distinctivecharacteristics and objectives. The Value Chain Group VCOR model could also be added to this listwhich is similar in style to SCOR but seeks to describe all the processes and activities within anorganisation.Lambert el al., (2005) note that four of the five frameworks they identified suggestimplementation of standard cross-functional business processes. However, only the GlobalSupply Chain Forum (GSFC) and Supply Chain Operations Reference model (SCOR) frameworksinclude business processes that could be used by management, to achieve cross-functionalintegration and are described in the literature with enough detail to draw meaningfulcomparisons. This also stands true for the VCOR model which, in its infancy, has little publishedliterature available other than on its website which is restricted to members who pay a fee.Table 6.5 draws some comparisons between the GSFC description of supply chain businessprocesses and the SCC SCOR model. Becker et al., (2003) six main qualities for an effective modelis used so that the opportunities and limitation of using a process reference model can beconsidered in light of conceptual modelling. The key finding is that only the SCOR model canprovide the domain knowledge necessary for conceptual modelling. This is supported by anumber of research publications that have discussed the applicability of SCOR for simulationpurposes (e.g. Barnet and Miller, 2000; Arns et al., 2002; Terzi and Cavalieri, 2002; Min and Zhou,2002; Hermann, Lin and Pundoor, 2003; Van der Zee et al., 2005; Albores et al., 2006; Persson andAraldi, 2009). Although argued extensively in the wider simulation literature, there is littleresearch that has concentrated on the opportunity to use SCOR for the purposes of conceptualmodelling.SCOR has notable strengths over the GSFC framework in all six of the criteria for an effectiveconceptual model. In particular the areas include: Extensive development and testing with academic, corporate and software partners who make up the membership of SCOR across the world and from different industrial contexts Regarded as a de facto standard model for SCM 118
    • Applicable for use in simulation analysis (both to develop tools and describing and evaluating supply problems) Standard description of processes and their relationships, performance measures and business practicesThe major limitations hold for both models. Although both claim to have been developed andevaluated with industrial and academic partners it is not clear on how systematic and rigorous thedesign process was. All that can be drawn is that the Supply Chain Council has advanced themodel and revised it on eleven occasions with collaboration with academics, technology providersand the government organisations that participate in the council’s activities. The other key lessonthat can be drawn from the analysis is that the ‘clarity’ and ‘economic efficiency’ of both models isdifficult to ascertain. It is clear that an advantage of a process reference model is to offerstandard descriptions but the question still open is ‘how these descriptions can be effectivelyused for the purposes of conceptual modelling’. This question is explored in more detail insection 6.4 using the SCC SCOR model, as it offers considerable strengths over the GSFC modeland has been shown to be applicable for simulation modelling purposes. 119
    • Table 6.5 Comparison of supply chain process reference models Global Supply Chain Forum process reference model Supply-Chain Council SCOR model v.9 (2008) (e.g. Cooper, Lambert and Pagh, 1997) Developed and tested originally with 69 voluntary member companies and now has over Developed and evaluated with members of the supply chain forum (includes fifteen major US 1,000 corporate and academic members established in each region across the world, Correctness corporations). No extensive testing has been claimed. from different industrial contexts (e.g. manufacturers, services, distributors, and retailers). It is now in its eleventh revision (SCC, 2008). The aim is to ‘help with implementation, instructors with material for structuring a SCM course It is often regarded as a de facto standard model in SCM (Stewart, 1997) and known as a and researchers with a detailed framework for future research in SCM’ (Croxton et al., 2001, pg. well established and well practiced model (Swafford, Ghosh and Murthy, 2006). SCOR has 14). There is no evidence of the model being used for simulation purposes other than for the Relevance been considered for its applicability for simulation purposes (e.g. Barnet and Miller, 2000; reasons noted in ‘economic efficiency’ (e.g. Arns et al., 2002; Min and Zhou, 2002 use the model Arns et al., 2002; Terzi and Cavalieri, 2002; Min and Zhou, 2002; Hermann et al., 2003; to define SCM and Terzi and Cavalieri, 2002 do not include it in their survey of simulation in the Van der Zee et al., 2005, Albores et al., 2006; Persson and Araldi, 2009). supply chain context). The model allows companies to (Stewart, 1997 and Huan, Sheoran and Wang, 2004 cite SCC, 1999): evaluate their own processes effectively; compare their performance with The work has been widely cited in research publications. Its main use has been for defining Economic other companies both within and outside their industry segment. Pursue specific SCM, teaching purposes and identifying areas for future research (e.g. Lambert et al., 1998; efficiency competitive advantages, use benchmarking and best practice information to prioritize Lambert and Cooper, 2000). their activities, quantify the benefits of implementing change, and identify software tools best suited to their specific process requirements. The SCC (2008) states: SCOR provides a standard description of management processes, a Eight key processes that make up the core of supply chain management are described at the framework of relationships among the standard processes, standard metrics to measure Clarity strategic and operational levels. The sub-processes describe typical activities. process performance, management practices that produce best in class performance and standard alignment to software features and functionality. The paper assumes that the ‘eight key business processes run the length of the supply chain and The standard is used widely for benchmarking activities (e.g. Gilmour, 1998). Huan et al., cut across firms and functional silos within each firm’ (Croxton et al., 2001, pg. 14). The work (2004, pg. 24) analysis suggest that it has a ‘complete’ set of supply chain performance Comparability notes that all firms should consider the eight processes but the importance of each process and metrics, industry best practices and enabling system which can be used to perform ‘very specific activities will vary. Its primary focus is for describing and defining SCM. thorough fact based analyses of all aspects of their current supply chain’. Advanced through collaboration with ‘technology suppliers and implementers, Systematic Not detailed. All that is claimed is that it has been developed and evaluated with industrial academicians and government organisations that participate in the council activities and design collaborators (see note in ‘correctness’). the development and maintenance of the model’ (SCC, 2008, pg. 1.1.1). 120
    • 6.4 Using SCOR for conceptual modellingThis section expands upon the argument that SCOR could be used as one source that can makethe process of conceptual modelling more efficient and focused. The previous section identifiedSCOR as a supply chain reference model that could provide the detail on the improvements(termed ‘best practices’), objectives (the descriptions of performance attributes and metrics) andthe supply setting (e.g. processes and relationships between them). The detail offered by SCORfor each of these requirements is summarised in table 6.6.Table 6.6 Domain knowledge offered by SCC SCOR model Detail offered by SCOR Glossary of supply chain best practices with associated definitions and processes that implementList of supply chain each practiceimprovements (‘best Major best practices are described in more detail (i.e. best practice needs and suitability indicators,practices’) impact on supply chain performance attributes/metrics, key best practice success factors and additional resources) Range of performance attributes (characteristics of the supply chain which permit it to be analysed and evaluated against other supply chains with competing strategies) Each performance attribute can be decomposed using the hierarchy of metrics into level 1 strategicList of objectives and metrics, level 2 and 3 lower level calculations (generally associated with a narrower subset ofmetrics (‘performance processes)attributes and metrics’) Level 1 and 2 metrics are described in detail (i.e. qualitative relationship description, quantitative relationships, calculation, data collection from SCOR processes, discussion and a graphic of the associated level 2 and 3 metrics in a hierarchy tree) Level 3 metrics are described and the data collection needs from SCOR processes are listed Graphics provide a visual representation of the process elements and their relationships to each other Inputs and outputs that are germane to each process elementSupply setting (‘Supply Following the graphics are text tables that identify: 1) the standard name for the process element,processes and their 2) the notation for the process element, 3) SCC’s ‘standard’ definition for the process element, 4)relationship’) performance attributes that are associated with the process element, 5) metrics that are associated with the performance attributes, 6) best practices that are associated with the process (candidates, not necessarily an exhaustive list) Model focuses on three environments: make-to-stock, make-to-order, and engineer-to-orderExtracted from: SCOR V.9 (2008)The following three sub-sections demonstrate how SCOR could be used to describe and analysean improvement (section 6.4.1), objectives (section 6.4.2) and its supply setting (section 6.4.3).This is achieved by considering two examples of typical supply chain problems from the literature,extracted from five research studies that have cases that have been evaluated using simulation.Table 6.7 lists the purpose of each study, objective and improvements. The two improvementsconsidered include vendor managed inventory (VMI) and collaborative, planning, forecasting andreplenishment (CPFR). These improvements have been studied for various objectives of study(e.g. inventory reduction, improve service level and improve total supply chain cost). 121
    • Table 6.7 Examples of two typical supply chain problems Disney and Towill Reiner and Trcka Southhard andContribution Sari (2008) Chang et al., (2007) (2003a; 2003b) (2004) Swenseth (2008) Supply chain design: The effect of vendor Problems and On the benefits of Evaluating VMI A study of an management inventory alternatives for aExample case in CPRFR and VMI: A in non- augmented CPFR (VMI) dynamics on the production companythe literature comparative traditional model for the 3C bullwhip effect in in the food industry: simulation study environments retail industry supply chains A simulation based analysis Bullwhip (peak order rate to step input and Minimise WIP Monthly inventory Reduction in total Inventory order rate variance) and inventory, fill rate turnover rate;Objective of supply chain cost, system costs, stock performance (service level) and capital turnover;study customer service delivery costs (system and production lead-times (cycle- out of stock rate; level (fill rate) and stock outs inventory, inventory times) service level availability) Distribution of Implement Implement orders between collaborative Implement VMI Implement VMI collaborative partners, decision planning, forecasting betweenImprovements between manufacturer planning, rules in the supply and replenishment, manufacturer and customer forecasting and chain, layout of the vendor managed and customer replenishment supply chain inventory 6.4.1 Using SCOR to describe supply chain improvements SCOR provides over 420 supply chain practices which could be selected based upon the type of improvements that may wish to be evaluated (Weaver et al., 2007). Sixteen best practices are described as major and are detailed with a description, impact on supply chain performance attributes/metrics and key success factors. An example of VMI and CPFR is provided in table 6.8, describing the SCOR process and the information that could help determine the potential impact on supply chain performance attributes/metrics. Table 6.8 Example of SCOR detail extracted for improvements SCOR Impact on supply chain performance attributes/metricsImprovement processes Reliability Costs P1, P2, P4, S1.1, S2.1, VMI helps to assure the availability of Inventory level decreases by up to 20% leading to S3.3, ES.7, D1, D1.5, items thereby helping to ensure lower inventory costs. The supplier gets a clear viewVMI D1.6, D2.5, D2.6, better on-time delivery performance of demand and flexibility (see above), so that they D3.5, D3.6 as well as greater fill rates can achieve lower variable manufacturing costs P1 (Plan Supply Better store in stock (2% - 8%)CPFR Lower logistics cost (3% - 4%) Chain) Better customer service (2% - 8%) Extracted from: SCOR V.9 (2008) Although only the major best practices are described in this level of detailed description, the business processes have further information that could be useful. This includes a description of the process, which performance attributes apply and the associated metrics. The description also lists other best practices that may be implemented as part of the process selected to represent each improvement (and performance measures). 6.4.2 Using SCOR to describe supply chain objectives The supply chain objectives can be represented using the SCOR metrics at three levels of decomposition. SCOR provides primary high level strategic measures that cross multiple SCOR 122
    • processes and lists a hierarchy of associated lower level metrics to calculate each of the higher level metrics. The level 1 metrics do not necessarily relate to a SCOR level 1 process type (e.g. plan, source). The metric hierarchy tress included in the SCOR model are useful to identify how a supply chain performance attribute can be measured by a range of associated metrics at three different levels. The examples provided in table 6.8 demonstrated a range of metrics being used to measure the performance of implementing VMI and CPFR improvements. Generally, these studies examined total supply chain cost, reduction of inventory and maintaining a desired customer service level (fill rate). Table 6.9 translates these into SCOR performance attributes and identifies the associated level 1, 2 and 3 performance metrics. For each performance metric, SCOR provides a definition of how the metric is to be calculated and indicates the data required to perform the calculation and the source process. Table 6.9 Example of extracting SCOR performance measures Performance Performance Performance metric Definition of metric calculation Data collection source attribute metric level CO.1.1 Total Supply TSCMC = Cost to Plan + Source + Make + Any related level 2 Level 1 metrics Chain Management Deliver + Return + Mitigate Supply ChainImprove supply chain process category Cost Riskcost by minimising CO.3.69 Cost to EM.4 Manage in-WIP inventory The sum of the costs associated with Level 2 metrics manage in-process process products managing in-process products (WIP) products (WIP) (WIP) Current on hand finished goods AG.3.39 Current On- inventories including safety stock EM.4 Manage in-Minimise finished Level 3 metric hand finished goods required to sustain current order process productsgoods inventory inventories fulfillment, assuming optimized (WIP) inventory practices P1.3 Balance supply chain resources with SC requirements P4.4 Establish delivery The percentage of ship-from-stock plansMaintain customer Level 3 metric RL.3.36 Fill rate orders shipped within 24 hours of order M1.3 Produce and testservice level (fill rate) receipt. D1.3 Reserve inventory and determine delivery date D1.9 Pick product Extracted from: SCOR V.9 (2008) Table 6.9 shows how to calculate total supply chain costs. This would include aggregating all the costs associated with plan, source, make, deliver, return and mitigating supply chain risk, selected if the process type has been selected. Decomposing this level one metric includes a hierarchy of costs associated with each type and at level three, specific calculations for activities such as managing WIP inventory etc. The example to maintain service level had many different choices for metrics that provided specific requirements but the fill rate metric were dominated in the 123
    • cases selected. In this example two planning, a ‘make’ and two ‘deliver’ decomposed processesprovide the data source to perform the metric calculation.6.4.3 Using SCOR to determine the interconnections with the supply settingThe SCOR model provides the inputs and outputs to each decomposed business processdistinguished by manufacturing environment (i.e. make-to-stock, make-to-order and engineer-to-order configurations). Figure 6.1 shows a sample of the detailed workflow for S1.2 (receiveproduct), a third level decomposed business process element that is associated with receivingproduct to contract requirements. The ‘S’ depicts that it is a source process element and at leveltwo, the ‘S1’ indicates it is concerned with source stocked product and is specific to receivingproduct, ‘S1.2’.Figure 6.1 Example of SCOR inputs and outputs to a decomposed business processSource: SCOR V.9 (2008)SCOR can be used to identify the interconnections between business processes using thedescriptions of inputs and their source process elements. Sections 6.4.1 and 6.4.2 showed thatSCOR can be used to identify the processes germane to a supply chain objective and improvementat a particular level of decomposition (i.e. level of detail is predefined). These can be classed asthe ‘core’ components and their source interconnections ‘candidates’ for possible inclusion. Thisprovides an opportunity to evaluate each interconnection based upon some decision rules with anaim of determining the boundary of the model. 124
    • 6.5 Presentation of outline designThe outline design presents a synthesis of the ideas considered for each design issue discussed inthis chapter. These ideas are presented as the key concepts for incorporation into themethodology. Each of these key concepts are identified and justified based upon the issuesidentified in this chapter in section 6.5.1. Secondly, each key concept is linked to one of thegeneral conceptual modelling process stages before identifying specific phases that should beincluded in the methodology (section 6.5.2).6.5.1 Key concepts to be incorporated into the methodologyTen key concepts have been synthesised from the discussion of the design issues for each of therequirements for developing the SCM2 (outlined and described in table 6.10). Each of the keyconcepts is outlined in table 6.10 with a brief description. The core idea developed in this chapteris that SCOR can be utilised to make the process of conceptual modelling more efficient andfocused. The main role of SCOR is to provide a critical source of domain knowledge to drive theprocess of conceptual modelling (key concepts 1 – 4), aid in decision-making process whenformulating the model boundary (key concepts 6 and 7) and aid in the description of the actualpractices to be represented in the model (key concept 8). The other core ideas include, how themodel boundary is formulated using some decision rules to include or exclude processes (keyconcept 6) and simplify model inputs (key concept 7), representing the model content (keyconcept 9) and for documenting and validating the conceptual model (key concept 10).Key concept one states how a supply problem should be described (i.e. objective, improvementand supply setting – see section 6.4). The objective describes the performance attributes (e.g.cost, responsiveness, agility) that an improvement in the supply chain must achieve. Animprovement describes the way in which the client wishes to alter the existing supply system tomeet the objective, while the supply setting describes the nature of the real world in which theimprovement interconnects with each objective. This is central to the design of the conceptualmodelling process as all further steps are derived from an understanding of the supply problemand the output of the process is validated against the objectives set. 125
    • 2Table 6.10 Key concepts to be included in the design of the SCM Key Concept Description Objectives describes the performance attributes (e.g. cost, responsiveness, agility) that an improvement in the supply chain must achieve1. Supply chain problem describe the Improvements describe the way in which the client wishes to alter the existing objective, improvement and supply supply system to meet the objective setting Supply setting describes the nature of the real world in which the improvement interconnects with each objective SCOR provides a hierarchy of performance metrics for each supply chain2. SCOR SCM performance metrics performance attribute with associated detail. The detail includes a description of can be used to identify how an each metric, calculation and the process elements that provide data to perform each objective is to be measured calculation SCOR provides a list of practices with associated detail. The detail includes a3. SCOR practices can be used to description of the practice, for major practices the impact that the practice should describe each improvement to be have on SCOR performance metrics and the process elements that are germane to evaluated each practice4. Identification of the core processes The process elements associated with each objective and improvement is identified that need to be modelled, their and classified as core process elements included in the model. Each process element inputs generated from a source has a list of inputs fed into the process from a source process element. process element5. Process elements that have yet to be included in the model can be The source process element that feeds an input to each process element included in classed as candidates for possible the model are considered for inclusion in the model inclusion6. Candidate process elements are Candidate process elements are included in the model if the input to be generated considered in turn for inclusion in will affect model behaviour by significantly impacting upon the objectives of study. the model, as they form a critical Those that do not can be excluded, if the client or modeller is unsure then they must interconnection between the core be tested by prototyping using sensitivity analysis. processes and the real world Process elements that feed an input that can be generated in a simplified form (e.g. fixed value, simple distribution) can be simplified. No inputs are fed into simplified7. Included process elements are process elements; therefore they indicate the boundary of the model. considered in turn to identify those Process elements that cannot be simplified, must be promoted (included in the that could be simplified model) and treated similarly to core process elements (thus the process repeats to identify candidates for inclusion in the model).8. The detail that needs to be included can be identified from the SCOR provides a list of typical practices for each process element. These can be actual practice for each process reviewed in light of the general practices described from the client’s perspective. element included and simplified in the model9. Modelling practice should The actual practice should be considered in turn to identify the activities that need represent the complexity and to be modelled to generate the desired output from the inputs identified in the detail of the actual practice to be simplest way without effecting model accuracy. The entities that need to be evaluated modelled are identified. The conceptual modelling report is documented from the objectives, improvement,10. The conceptual model is model content, assumptions and simplifications made about the model to be documented and validated developed. The conceptual model is validatedKey concept two states that SCOR can be used to describe each objective in terms of performancemetrics, calculations and data sources necessary to perform each category (shown in section6.4.2). Similarly for key concept three, SCOR provides a list of practices with associated detailwhich includes a description of the practice and the process element that are germane to eachpractice (shown in section 6.4.1). The major best practices (e.g. VMI, CPFR, cross-docking) usedby SCOR practitioners are listed in more detail. This is useful as this also suggests the impact thateach practice should have on related SCOR performance metrics. This can be used to verify thateach improvement will have an impact on desired performance so to focus the study on thoseimprovements that could address the objective. 126
    • The importance of key concepts two and three is that the process elements, which are germaneto each improvement and objective, have been identified. These process elements can beclassified as ‘core’ process elements that are to be included in the model (key concept four). TheSCOR process elements have a set of inputs that are fed from a source process element. Theseinputs can be considered for inclusion in the model; they are, effectively, candidates that need tobe included or excluded based upon some rules (key concept five).Each candidate process element can be considered in turn for inclusion in the model as they forma critical interconnection between the core processes and the real world (key concept six).Candidate process elements are included in the model if the input to be generated will affectmodel behaviour by significantly impacting upon the objectives of study (discussed in section6.2.1). Those that do not can be excluded. An alternative option is added to the criteria toinclude those process elements in which the modeller is ‘unsure’. This provides a list of those todiscuss and confirm in greater detail with the client. Ultimately, the process elements that couldnot be determined should be ‘tested’ by using a prototyping method.The boundary of the model can be determined by identifying those ‘included’ process elementsthat generate an input to be fed into the model which can be simplified (key concept seven). Therule, which determines whether the input can be simplified, is determined by asking: can theelement be generated in a simplified form (e.g. a fixed value, simple distribution)? This indicatesthe boundary of the model because no further inputs are needed to generate the input. Theprocess elements that cannot be simplified must be ‘promoted’ (included in the model) andtreated similarly to ‘core’ process elements. The process is repeated until all candidate processelements have been identified and considered for inclusion. The point when no furthercandidates can be considered indicates that the model boundary has been reached and allprocess elements that should be included have been (both core and promoted process elements).The detail of the model can be identified from the actual practice for each process elementincluded and simplified in the model (key concept eight). The SCOR model practices can be usedas examples of typical practices that have been used for each process in a supply chain context.This enables the modeller to have an indication of the types of practices that could be modelledprior to obtaining, from the client, the actual practice from the client’s perspective.The conceptual model needs to represent the complexity and detail of the actual practice in termsof how it will be modelled (key concept nine). The modeller needs to consider each actual 127
    • practice to identify the activities and events that would need to be modelled, to generate the desired output from the inputs identified. Although the inputs have previously been identified the process has also identified the outputs as they feed into a ‘core’ or ‘promoted’ process element. The modeller is concerned with identifying the simplest way to model the activities and actions without affecting model accuracy. To do this the entities that need to be modelled are identified. The final key concept concerns documenting and validating the model to be developed (discussed in section 6.2.2). The output of the process is a description of the computer model to be developed (e.g. objectives, improvement, model content, assumptions and simplifications). Validation checks should be incorporated into the methodology phases where appropriate and provides a final hypothesis validation test. 6.5.2 Linking key concepts to phases in the SCM2 Seven phases are required to incorporate the ten key concepts into a procedure for the SCM2. These phases have been identified by linking each key concept to the general stages in a conceptual modelling process that were identified in section 6.1.1 (this is shown in table 6.11). Each of these phases is justified in this section. 2 Table 6.11 Linking key concepts, conceptual modelling process with phases in the SCM Conceptual modelling Key concept Phase in SCM2 process1. Supply chain problem describe the objective, Problem structuring and Phase 1: Describe the supply chain problem improvement and supply setting setting objectives from the clients perspective2. SCOR SCM performance metrics can be used to Phase 2: Determine how each objective is to Defining the identify how an objective is to be measured be measured experimental factors and3. SCOR practices can be used to describe each Phase 3: Determine how each improvement is reports improvement to be evaluated to be represented4. Identification of the core processes that need to be modelled, their inputs generated from a source Phase 4: Determine the model inputs and process element source process elements that interconnect5. Process elements that have yet to be included in the within the model and the immediate supply model can be classed as candidates for possible setting inclusion6. Candidate process elements are considered in turn for inclusion in the model, as they form a critical Determining the model interconnection between the core processes and the content (i.e. complexity Phase 5: Formulate the model boundary real world7. Included process elements are considered in turn to and detail) identify those that could be simplified8. The detail that needs to be included can be identified from the actual practice for each process element included and simplified in the model Phase 6: Design the level of detail necessary9. Modelling practice should represent the essential to implement the model complexity and detail of the actual practice to be evaluated Provide a project Phase 7: Document and validate the10. The conceptual model is document and validated specification conceptual model 128
    • The process of conceptual modelling is initiated by identifying the supply problem from theclient’s perspective (key concept one and phase one). The experimental factors correspond tohow the improvement is to bring about a change in the supply system (phase two) and thereports correspond to how each objective is to be measured (phase three). These phases areseparated as they require different checks to ensure that the descriptions provided are ‘correct’.This is important because the descriptions provided drive the analysis of the model content somust comprehensively represent the objective and improvement so that the interconnectionswith the supply setting can be determined.Phase one – six correspond to determining the model content (i.e. complexity and detail). This isbecause they first identify interconnections between core components and the supply setting andoffer some rules to decide upon whether they should be included or not. Firstly, SCOR is used toextract the domain knowledge necessary to identify the inputs to each ‘core’ process element andhow it interconnects within the model and the immediate supply setting (phase four). Thepurpose of this stage is to identify ‘candidate’ process elements that have yet to be considered forinclusion. Phase five builds upon the descriptions obtained in phase four so to formulate themodel boundary. Each of the inputs is fed from a candidate process element that could beincluded as they may form a critical interconnection between the processes included in the modeland with the real world. Here, candidate improvement options are simplified, promoted, tested,or excluded, based on the two rules defined in section 6.2.1.Phase six designs the level of detail necessary to implement the processes and simplified inputsidentified in the model boundary. The level of detail is determined by identifying the actualpractice for each process included and simplified in the model. This is a form of aggregation asthe actual practice may occur in more than one process. From this analysis, the modeller decideson the modelling practice to represent the essential complexity and detail of the actual practice tobe evaluated.Phase seven documents and validates the conceptual model. This phase cannot be seen as theend phase but a phase that refines the model throughout each of the phases. The output ofphase seven is a validated description of the computer model to be developed agreed by both theclient and the modeller. 129
    • 6.6 Chapter summary The chapter has considered the design issues for each of the requirements for the SCM2. The discussion has led to a range of ideas being synthesised into a set of ten key concepts for inclusion in the procedure. Each of the key concepts have been aligned to a general process of conceptual modelling, so to deduce a set of phases that are specific for conceptual modelling in the domain of SCM. An outline of phases in the methodology is shown in table 6.12 along the inputs that require information and outputs that provide information from the methodology. Table 6.12 Outline of the methodology: Phases, inputs and outputs Inputs to the SCM2 Phases in the (SCM)2 Outputs from the (SCM)2From the client: Supply chain objective(s) Phase 1: Describe the supply chain Statement of supply problem Supply chain improvement(s) problem from the clients perspective Supply settingUse SCOR to identify the following fromthe description of the supply chainobjective(s): Statement of how each process is to Supply chain strategic and diagnostic Phase 2: Determine how each objective is provide the data used to calculate each metrics to be measured objective Supply chain metric calculation Data collection requirements from each process elementUse SCOR to identify the following fromthe description of the supply chainimprovement(s): Statement of how each process is to Phase 3: Determine how each Level of process detail for each represent each improvement (i.e. list of improvement is to be represented improvement (includes process processes germane to each improvement) element that represents each improvement)Use SCOR to identify the following fromthe process elements identified in phasetwo and three: Phase 4: Determine the model inputs and Inputs fed to each process element source process elements that interconnect List of model inputs and candidate process included in the model within the model and the immediate elements Source process elements can be supply setting discriminated based upon whether they exist in the model or not Statement of the model boundaryThe modeller with input from the client (includes a list of process elementsconsiders each ‘candidate’ process Phase 5: Formulate the model boundary promoted, simplified, excluded and thoseelement in turn against each rule that require testing)The modeller uses SCOR and client sources Phase 6: Design the level of detail Statement of actual practice and how itto identify and describe actual practice necessary to implement the model will be represented in the modeland represent this into modelling practice Phase 7: Document and validate the A valid description of the computer modelInputs from phases one - six conceptual model to be developed Table 6.12 represents the procedure as a logical and sequential set of phases to be completed. However, iteration will be necessary in particular when a validation check has not been satisfied and when determining the model boundary (between phases five and four). A benefit offered by the methodology, is that SCOR has been incorporated into the design to enable a more efficient and focused process (e.g. provide information necessary to drive the analysis, useful aid when consulting SMEs). Additionally, validation checks will be necessary when 130
    • describing the supply problem, improvement, objective, model boundary, and description of howthe actual practices are to be represented in the computer model. This will be the result oflearning during the process of conceptual modelling, leading to adjustment when necessary.The following chapter (chapter seven) details and refines the outline methodology defined in thischapter so that it is aligned to meet the specification of requirements. The development of themethodology is founded upon a strong conceptual base and an evaluation of how eachrequirement can be considered in the design of the methodology. 131
    • Chapter 7 Detailed design for SCM2 (Stage IV)This chapter implements stage IV of the research methodological programme by refining anddetailing the methodology with two supply chain applications. It is the final stage to address thesecond objective of the research to ‘develop a methodology for simulation conceptual modellingin the context of SCM applications’. The detailed methodology is aligned with the requiredspecification, presented in chapter five, to demonstrate that it meets the requirements. This iscompleted before the SCM2 is preliminarily validated in chapter eight.The chapter is structured to: Overview of the SCM2 (described in section 7.1 and represented in figure 7.1) - Present an overview of the phases and steps to follow as laid down in the SCM2 Presentation of the development cases (described in section 7.2) - Present the supply chain application cases to detail and refine the SCM2 with rationale for their selection Application of the development cases to refine and detail the SCM2 (each phase is justified in section 7.3) - Discuss how each phase in the SCM2 was detailed and refined with illustration of the decisions made in the design using the two supply chain applications Implementing the SCM2 using a spreadsheet application (discussed in section 7.4) - Discuss how the SCM2 can be implemented using a spreadsheet application to provide templates and automate a number of the steps Alignment of detailed design of SCM2 against specification (discussed in section 7.5) - Demonstrate that the detailed and refined design of the SCM2 methodology meets the specification of requirements identified in chapter five7.1 Overview of the SCM2The aim of the methodology is to provide a prescribed procedure to aid the users to create asimulation conceptual model for SCM applications. The output from the methodology is adocumented and validated description of the computer model to be developed (the conceptualmodel). The methodology includes seven phases, associated detailed steps, who participates ineach step and the information needs that incorporate each of the key concepts described inchapter six. It has been developed to meet the specification of requirements identified in chapterfive; the outline design presented in chapter six and has been refined and detailed in this chapterusing two supply chain applications. The methodology is summarised in graphical form in figure7.1. 132
    • Phase 2: Phase 1: Determine how each Point of entry Describe the supply objective is to be problem measured A formal problem formulation and Output: Description of the structuring methodology or unstructured Output: Description of the core improvement(s) to be evaluated, problem from client processes that provide data for a given objective(s) within its used to calculate each supply setting objective Phase 6: Phase 3: Phase 7: Design the level of detail Determine how each necessary to implement improvement is to be Document and validate the the model represented conceptual model Output: Description of the model Output: Description of the core Output: A valid description of the components and processes that represent each computer model to be developed interconnections that represent improvement the actual practices included in the model Phase 4: Determine how the Build a prototype and inputs and their Phase 5: use sensitivity analysis sources interconnect to extend the model within the model and Formulate the model boundary and level of with its immediate boundary detail supply setting Output: List of processes and Output: List of inputs and Output: Refinement of the inputs included in the model candidate processes for model boundary and level of possible inclusion in the model detail boundary Iterate for each PROMOTED process decided in phase five 2Figure 7.1 Overview of the SCMThe purpose of each phase can be described along with the information that each phase providesto aid a user in the creation of a conceptual model for SCM applications (in italics below). Thisincludes an overview of the steps which need to be followed and ‘checks’ that need to becompleted before proceeding to the next step. Iteration is required between steps if a check hasnot been satisfied (requires the phase to be repeated), or to initiate a return to a previous step asnew information is generated (in the case of formulating the model boundary).Point of entry: Enter the methodology when a client has a supply problem to be evaluatedusing a simulation approachPhase one: Describe the supply problem - The supply problem is described from the perspective ofthe client This includes a description of the supply chain improvement(s) to be evaluated (step 1.1) for a given objective(s) (step 1.2) within the supply setting (step 1.3). Two checks are made to state how each improvement could achieve each objective (step 1.4) and that the descriptions provided are ‘correct’ (step 1.5). Simulation would be unsuitable if the 133
    • improvement could not achieve each objective and thus the improvement is excluded from the study. The phase cannot be exited until the description of the supply problem is ‘correct’; the phase is repeated otherwise.Phase two: Determine how each objective is to be measured – The objective is described in termsof how it will be measured This includes a description of the decomposed supply chain metrics (step 2.1), how each performance metric will be calculated from the data sources that have been generated from the decomposed business processes (treated as ‘core’ processes at the required level of detail), (step 2.2), and the actors associated with each data source and measurement span (step 2.3). The description of how each objective is to be measured is checked for ‘correctness’ (step 2.4) and, therefore, the phase cannot be exited until this check is complete; the phase is repeated otherwise.Phase three: Determine how each improvement is to be represented – The improvement isdescribed in terms of how it is to be represented This includes a description of the decomposed business processes (treated as ‘core’ processes at the required level of detail) (step 3.1), for each actor (step 3.2). The description of how each process is to be represented is checked for ‘correctness’ (step 3.3). The phase cannot be exited until this check is complete and, therefore, the phase is repeated until it is.Phase four: Determine how the inputs and their sources interconnect within the model andwith its immediate supply setting – Provide a list of model inputs and candidate process elements(NB supplies information only to formulate the model boundary) The inputs and their source interconnections for each actor are described for each process element included in the model (step 4.1) and discriminated against to identify ‘candidate’ process elements (step 4.2). The process is initiated from the included process elements that are provided from the ‘core’ processes (identified in phase two and three), followed by the ‘promoted’ process elements (identified in phase five). The phase cannot be exited until all process elements have been discriminated against (in step 4.2) 134
    • and, therefore, it is re-entered in subsequent rounds when ‘promoted’ process elements are identified (from step 5.3).Phase five: Formulate the model boundary – Provide a list of processes and inputs included in themodel Each of the inputs from the candidate process elements (listed in step 5.1) are analysed by two rules for inclusion in the model boundary and the rationale for each decision is documented (step 5.2). The first includes reaching a decision on: whether the input to be generated from the candidate process element will affect model behaviour by significantly impacting on the objectives of study? If the answer is ‘yes’, then ‘include’, if ‘no’, then ‘exclude’, if unsure, ‘test’ (using a prototyping method). The second refers only to those indicated as ‘include’: can the included input be generated in a simplified form (i.e. random distribution or fixed value) so that there are no further inputs to the process? If the answer is ‘yes’, then ‘simplify’, if ‘no’, then ‘promote’. When no further inputs have to be considered, the ‘promoted’ process elements are returned to phase four for discrimination. The cycle is completed in rounds until no further inputs and their sources need to be considered. The list of processes and relationships between inputs to be included in the model are checked for ‘completeness’ and ‘correctness’. The phase cannot be exited until this check is complete, phase four and five is repeated otherwise.Phase six: Design the level of detail to implement each process and input included inthe model boundary – Provide a description of how the actual practices are representedby the model components and relationships between them Each process included in the model is described in terms of how it is actually implemented in practice in sufficient detail of how the inputs (and sources) are converted to produce an output (to a destination), specified by each actor (step 6.1). This also includes identifying ‘phantom’ process elements by determining the process elements that contain only a workflow input and no practices can be identified, that would influence the ch aracteristics of the workflow. These are not modelled in any detail. The following steps describe how each actual practice, specified by each actor, will be represented by the components (i.e. processes; activities and events; entities) in the computer m odel, in the simplest way (by incorporating any assumptions or simplifications into the descriptions) 135
    • (step 6.2). There are no checks at this stage as the following phase is a full and complete validation of the conceptual model (phase seven). The phase cannot be exited until all actual practices have been represented by the components in the computer model.Phase seven: Document and validate the conceptual model – The draft descriptionsprovided from the phases and steps in the methodology are documented and validated The draft conceptual model is documented by describing the supply problem from the client’s perspective, how each objective is to be measured, how each improvement is to be represented, how each actual practice is to be represented by the components in the model and their interconnections, assumptions and simplifications incorporated into the model component descriptions (step 7.1). Each of the descriptions is validated for ‘correctness’ from the perspective of the modeller, (hypothesis validity), and client, (credibility). Any issues that arise, results in the necessary revisions and adjustments by returning to the necessary phase in the methodology (step 7.2). The final step documents a non-software specific description of the simulation model to be developed (step 7.3).7.2 Presentation of the development casesThe detailed design has been developed and refined using two development cases which arerepresentative and typical of complex supply chain problems. Firstly, the BeerCo developmentcase is used as a typical and simplified application that has been used for many years to teach(e.g. Kaminsky and Simchi-Levi, 1998; Holweg and Bicheno, 2002; Jacobs, 2000; Sparling, 2002)and to study supply chains (e.g. Ackere, Larsen and Morecroft, 1993; Lee, Padmanabhan andWhang, 1997; Disney and Towill, 2003a; 2003b). Secondly, the CarCo development case is adetailed and extremely complex case that has been developed in an industrial context: anautomotive seat supply chain. This case has been used to compare supply chain simulationfunctionality (e.g. Albores et al., 2006), identify methods for evaluating a supply chain problem (inWeaver et al., 2007) and to select supply chain architecture (Benton, 2009).7.2.1 Development case 1: BeerCoThe BeerCo development case (Beer distribution game) is considered a good illustration of a real-life supply chain (Sterman, 1989; Disney and Towill, 2003a; 2003b) and an environment that isrich, containing four actors in a chain, many feedback loops and time delays (Paik and Bagchi,2007). In particular, Sterman (1989; 2000) points out that the decision rules describe the actualdecision process very well as it includes the essential features of a stock management procedure. 136
    • These features include replacement of expected orders, correction of differences between thedesired stock and the actual one, and an evaluation of the supply chain inventory (Paik andBagchi, 2007).Figure 7.2 Structure and flows in the BeerCo development caseSource: Paik and Bagchi (2007)The structure and flows in the game are shown in figure 7.2. The game consists of four actorsdirectly linked in a supply chain; a factory, distributor, wholesaler and retailer supplying beer.Beer cannot skip the adjacent position (e.g. the wholesaler orders beer from the distributor andships beer to the retailer). The objective of the game is to minimize the total cost for everyone inthe supply chain by maintaining low stocks but managing to deliver all orders. An importantconsideration in making decisions is the delay in the movement of beer through the supply chain.There are two costs involved: inventory holding costs and back order costs. To minimize the sumof these costs, the cost of having stock (stock holding cost) with the cost of being out of stock,when a customer orders beer (back order costs) must be balanced. This development case isdescribed in more detail as the methodology is applied in section 7.3.7.2.2 Development case 2: CarCoThe CarCo development case is a detailed, complex and contextually rich supply chain problemthat has been extracted from an industrial context (justification for the case was provided insection 3.3.3). The supply chain under study deals with the supply of seats to CarCo. There arefour echelons involved in the supply chain: the car company (a luxury automobile manufacturer:LA), the seat supplier (SS), a third party warehouse (operated by a third party logistic provider:3PL), and two suppliers of seat components (central head-rests: CHR and tracks: T). Figure 7.3shows the configuration of the supply chain. 137
    • Figure 7.3 A simplified diagram of CarCo’s supply chainSource: Albores, Weaver, Love and Benton (2006)The issue that the development case considers in detail is one of planning the supply chain. Thegoal is to improve the visibility of demand in the supply chain to impact upon deliveryperformance and to reduce supply chain costs. At present there is multiple and contradictoryinformation being generated by different planning systems at each of the actors locations (e.g.planning is difficult because the seat supplier receives three pieces of contradictory planning datafrom the luxury automobile manufacturer). Due to the inaccuracy of the information provided,informal planning data is relied upon to achieve the delivery performance targets. There is a needto improve the coherence of the information flows in the supply system and minimise the impactof the contradictions in the planning data due to the multiple signals of demand. Thisdevelopment case is described in more detail as the methodology is applied in section 7.3.7.3 Application of the development cases to refine and detail the SCM2The aim of both development cases is to apply them in detail and refine the outline design for theSCM2 developed in chapter six. Also, align the design to meet the required specificationpresented in chapter five (identified from the literature). This includes detailing how each of thekey concepts identified in the outline design is implemented in the methodology and ajustification for the choices made in the design. A list of principles considered from existingpractice and observations made during the refinements, using the two development applications,are provided in tables for each phase in appendix A. The design choices, considered for each ofthe principles/observations, cover a range of aspects such as detailing the: Procedure for each phase with specific steps that should be followed Role of the participants in each step Appropriate means for documenting and representing the description and content of the conceptual model Points of entry and exit to and from the phases and steps Way in which information is used and provided from the methodology 138
    • In relation to how information is used and provided, the outline design argued that using domain-knowledge (particularly from the SCOR model) would provide an opportunity to develop morefocused and efficient guidelines for simulation conceptual modelling. The information that can beprovided from SCOR to undertake a step is considered along with ways to use the standardterminology to provide information (i.e. for use by other steps in the methodology or to describethe conceptual model).The two development cases are only applied in detail up to the point that the actual practices aredescribed (a domain-orientated simulation conceptual model). This is the point at which themethodology is novel. This means that the description of the model components is notconsidered in detail in this chapter, nor is the final documentation of the validated conceptualmodel. The steps that correspond to each of these needs incorporate existing practice that iswidely used to describe the model components and their interconnections (theseinterconnections were identified in the model boundary formulation). It can be argued, that thedocumentation and validation procedure is also novel but is dependent upon how the modelcomponents are described and is a considerable area of study in its own right. To demonstratehow the methodology can be used in these steps some illustrative examples are discussed fromthe BeerCo development case. As previously noted a modeller worldview (e.g. those that arefamiliar with DES), experience or skills have a bearing on how the model components aredescribed (C.f. section 2.4.3).7.3.1 Phase 1: Describe the supply problem from the client’s perspectivePhase one implements the first key concept identified: a supply chain problem describes theobjective, improvement and supply setting. It initiates the whole conceptual modelling procedureby describing the supply problem and performs two checks to verify that simulation is suitable forevaluating an improvement and that the descriptions provided are ‘correct’. There were anumber of observations that were made when implementing key concept one into phase one ofthe SCM2. These observations have been highlighted previously by Robinson’s (2004) discussionof developing an understanding of the problem situation and determining the modellingobjectives. These are considered in light of applying the two development cases and how theseimpacts upon the design of the phase in table A.1 in appendix A.These considerations were included in the formulation of the detail of the phase. In particular,the applications to the cases demonstrated it was of critical importance to structure and describethe problem correctly. This is the case because the core components to be included in the modelare derived from the supply problem and the boundary of the model is formulated by considering 139
    • the relationships between core components and the supply setting. The detail of phase one is shown in table 7.1. 2 Table 7.1 Detailed steps for phase one of the SCMPhase one: Describe the supply problem from the clients perspective Process steps Information required Information providedPoint of entry:From client source(s): A structured (from a problem formulation and structuring methodology) or unstructured problem, from theclient;From 7.2.1: An invalid description of the supply problem Obtain from the client the desired impact (e.g. minimise, maximise, Describe the objectives of study, in terms of the impact on maintain) on supply chain performance Description of the1.1 supply chain performance attributes, that the improved attributes. objective(s) of study supply system should achieve Use SCOR descriptions of supply chain performance attributes (SCOR V.9, section 3) as a guide Obtain from the client situational Description of the Describe the alternative [set of] supply chain information. alternative1.2 improvement(s) in terms of how and which actors are Use SCOR descriptions of practices improvements to be affected by the change (SCOR V.9, section 4) to select and made to the existing describe each improvement system Describe the nature of the setting of the supply problem (e.g. actors in the supply system that have a cause and Obtain from the client information on Description of the supply1.3 effect relationship between the improvement and the general supply problem setting problem setting objective, and any important situational information) that may influence the scope of the problem Use the information provided from 1.1 – 1.3 to describe the cause and effect relationship between the objective and improvement within the stated supply setting; State the means by which each improvement is to achieve Use SCOR descriptions of impact of Description of how each each objective, by bringing about change in the supply1.4 each practice on performance improvement could system (in terms of desired impact on performance attributes, if the improvement is achieve each objective attributes) described as a major best practice (SCOR V.9, section 4). Consult the client on how each improvement will bring about change in the supply system Check that the description provided from steps 1.1 – 4 are Consult with the client on each Description of the supply1.5 ‘correct’ from the perspective of the client making any description (Information provided from problem from the client’s alterations as deemed necessary before proceeding 1.1 – 4) perspectivePoint of exit:To phase 2: Proceed only when the description of the supply problem has been agreed as ‘correct’To step 7.1: As above and when all other phases have been completed (final validation)Output: Description of the supply problem from the client’s perspective 7.3.1.1 Step 1.1: Describe the objective(s) of study The objectives of study can be described in terms of the impact on supply chain performance attributes that the improved supply system should achieve. In both cases the objectives can be easily translated into the required supply chain performance attributes using the SCOR descriptions of typical attributes. The impact is indicated in general terms such as whether the performance attribute is to be ‘minimised’ or ‘maximised’ in line with most simulation studies (Beamon, 1998). This can be seen in table 7.2. 140
    • Table 7.2 Description of the objective(s) of study BeerCO development case CarCO development caseDescription of the From 1. Minimise total supply chain costs (-) 1. Maximise supply chain reliability (+)objective(s) of study step 1.1 2. Maintain customer service (-) In the BeerCO development case, the retailer is experiencing high total supply chain costs and poor levels of customer service due to a lack of planning and control across the supply chain and between actors. The aim is, therefore, to ‘minimise’ total supply chain costs by reducing inventory, while ‘maintaining’ customer service. Using SCOR performance attributes, it is obvious that there are two objectives: to minimise total supply chain costs and to maintain customer service. In the CarCO development case, the luxury automobile manufacturer (OEM) needs to ‘minimise’ the impact of contradiction in planning data due to multiple signals of demand which is impacting upon the reliability of the supply chain. The aim is to improve the coherence of the information flow to the partners in the seat supply chain. To achieve this, the luxury automobile manufacturer needs to be supplied seats on time as line stoppages are unacceptable. Using SCOR performance attributes, it is apparent that there is one objective: to ‘maximise’ supply chain reliability. 7.3.1.2 Step 1.2: Describe the supply chain improvement(s) The supply chain improvement can be described in terms of how and which actors are affected by the change to the supply system. This information is specific to the organisation implementing the improvement but the extraction of this information can be aided by the descriptions of the practices provided by SCOR (it was previously noted that over 420 practices exist). There was also some difficulty ensuring that the correct practices were identified due to different variations in certain practices and the need to combine more than one for the purposes of describing the improvement. Although, using the SCOR best practice guide it is worth noting that the participants (or at least the facilitator) involved in this step needs to be proficient with SCOR and its terminology. It was important to be clear how the improvement is to be implemented in practice and by which actor; therefore this was emphasised in the step. For instance, the SCOR model was clear in how to describe the improvement but not how it will bring about an effect in the real system. An illustration of the information provided from step 1.2 is shown in table 7.3. 141
    • Table 7.3 Illustration of the description of the improvements selected BeerCO development case CarCO development case Enable real-time visibility using an EDI digital Providing key participants in the supply chain full link between the wholesaler and distributor to visibility of the daily call in (DCI) formulated by theDescription of the From share information on complete finished goods, luxury automobile manufacturer. The informationalternative step order status, expected shipments and backlog. is used by the tier three manufacturers (seatimprovements to the 1.2 The information is used by the distributor to headrests and tracks) so that they can adjust theirexisting system plan deliveries and how orders are placed with kanban set-up to reflect daily call in (DCI) the factory. requirements. For illustrative purposes, two practices could be identified from the SCOR descriptions that adequately described the improvements for each development case: Enable real-time visibility using an EDI digital link between the wholesaler and distributor to share information on complete finished goods, order status, expected shipments and backlog (BeerCo case) – This was selected to improve one of the underlying effects of the bullwhip effect by improving information sharing between actors in the supply chain (e.g. Lee et al., 1997a, 1997b; Mason-Jones and Towill, 1997; Chatfield, Kim, Harrison and Hayya, 2004; Steckel, Gupta and Banerji, 2004; Croson and Donohue, 2005; Ouyang, 2007). There were many practices for information sharing. The one to be selected had to reflect who implements the practice and what information is visible and how it is used (i.e. distributor uses the information to plan deliveries and how orders are placed with the factory). Provide key participants in the supply chain to have full visibility of customer demand - This was selected to improve the coherence and sharing of actual demand further upstream. The description was improved by describing the means of providing demand (i.e. the daily call in), from a particular actor (i.e. LA) and how the information is used (i.e. by SS and T so that they can adjust their kanban set-up to reflect the daily call in requirements). 7.3.1.3 Step 1.3: Describe the supply setting The supply setting is specific to the actual supply system being examined. Therefore, information can only be obtained from the client (or more specifically SMEs). The aim here is to identify the client’s perspective on the scope of the problem and, in particular, the actors to be included in the analysis. The actors to be included in the model should have a cause and effect relationship. The cause is the improvement that is implemented to bring about a change in the supply system and the effect relates to how the impact of the change is measured. A later stage (formulation of the model boundary) determines the necessary interconnections between the improvement, objective and the supply setting. An illustration of the information provided from step 1.3 is shown in table 7.4. 142
    • Table 7.4 Illustration of the description of the supply problem setting BeerCO case CarCO case The scope of the investigation The scope of the investigation is determined by:- is determined by:- Actors in the supply system: luxury automobile manufacturer Actors in supply system: (LA), seat set manufacturer (SS), third-party LogisticsDescription of the From retailer, wholesaler, Company (3PL), central headrest manufacturer (CHR) andsupply problem step distributor and factory) track manufacturer (T).setting 1.3 Product: units of beer Product: seat set consisting of front and rear seats delivered supplied downstream in as a module to the luxury automobile manufacturer, in the a chain fabric specified by the end customer Both development cases were described in terms of the actors in the supply system and the product that flows through the system. In the BeerCo development case, four actors are included so that the impact of the improvement can be measured in terms of the ‘total supply chain cost’. The boundary would be drawn differently if only the ‘customer service’ measure was used. This is because the boundary would be drawn between the retailer (places orders to be satisfied); wholesaler (measure how customer service is maintained, implements improvement) and the distributor would also be included because it is affected by the improvement. Similarly, the CarCo development case consists of five organisations. These are included because the improvement includes the LA sharing information with the two tier three suppliers so that they can adjust their kanban set-ups. The supply chain would need to include all critical links between the actors involved in the supply of a seat set to the LA to demonstrate the impact of the change upon delivery performance. 7.3.1.4 Step 1.4: Description of how each improvement will achieve each objective This step was included as a useful means of justifying the inclusion of the improvement as part of a simulation study, aid in the determination of the model boundary and for validating the model in later phases. This was a more difficult step to complete as it forces the participants to be clear on how the change is to be brought about in the system. This is considered by stating the means by which the improvement is to achieve each objective. An illustration of the information provided from step 1.4 is shown in table 7.5. Table 7.5 Illustration of how each improvement could achieve each objective BeerCO case CarCO case The distributor decides how much to order from the factory Adjusting the CHR and T masterDescription of how and deliver to the wholesaler based upon visibility of stock, From production schedule based uponeach improvement orders, shipments and backlogs. This will impact upon the step the DCI (information flow) tocould achieve each amount of inventory required (and the associated cost of 1.4 effect the reliability of deliveredobjective inventory) to satisfy customer orders “in-full” as stock-outs are materials to the LA unacceptable. In the BeerCo development case the improvement to share information between the wholesaler and distributor is not useful unless it states what the information is and how it will be used. Likewise in the CarCo development case sharing actual demand further upstream is meaningless 143
    • unless it states who receives the information and how it will change the way suppliers plan theiroperations. SCOR can provide information on the typical impact that a practice can have on therange of performance attributes described in SCOR. In these development cases the modeller canuse this information to describe how the improvement could achieve the objective and besatisfied that it should be included in the simulation study. The problem is that this has only beencompleted for a select number of key business practices so it is likely that the information issourced directly from the client. It is clear that forcing the modeller to express the improvementin terms of how it will impact upon an objective, provides a useful check that the improvementshould be included and can be used for validation purposes in later phases.The modeller in the BeerCo development case must be clear that the information aids ‘thedistributor to decide how much to order from the factory and deliver to the wholesaler, to reducesupply chain cost while maintaining reliability’. Improving visibility between the wholesaler andthe distributor in the BeerCo case should enable the distributor to receive demand, stock andshipping information, without an order delay. This information can be used to keep finishedgoods inventory stock low so as to satisfy future demand without stock-outs.For the CarCo development case, the improvement enables the ‘tier three suppliers’ (centralheadrests and tracks) to adjust their master production schedule based upon the DCI (informationflow) to affect the reliability of delivered materials to the luxury automobile manufacturer’. TheCarCO case improvement will improve reliability to the luxury automobile location becausecommunicating the DCI will enable the tier three suppliers to plan production and deliveryrequirements based upon LA demand (reduced demand contradictions and real demand fromsource).7.3.1.5 Step 1.5: Draft and check the description of the supply problemThis step was added in order to draft a description of the supply problem and check with theclient that the problem is understood and has been agreed between the participants. During thedesign of this phase it was clear when applying the two development cases that if the problemwas not described correctly then the proceeding analysis would be flawed. Likewise, thedescription of how each improvement is to be represented and how each objective is to bemeasured could not be derived if the information was not provided in the structured way.7.3.2 Phase 2: Determine how each objective is to be measuredPhase two implements the second key concept identified in the outline design: ‘SCOR SCMperformance metrics can be used to identify how an objective is to be measured’. The phase is 144
    • entered only when the supply problem has been described and been agreed as ‘correct’. This isimportant as the information required by the phase is gathered from the description of theobjective of study. The observations made when refining and detailing this phase are presentedin table A.2, in appendix A. with an overview of how they influenced the design.The development case applications showed that it was relatively straight-forward to describe howthe improvement is to be represented using SCOR. An hierarchy of metrics is presented in SCORfor each supply chain performance attributes which are described in significant detail (example ofthe reliability metric is shown in figure 7.2). The modeller needs to select the metrics and extractthe relevant information from the descriptions of each metric. Critically, the data sources areprovided that will drive each calculation. These are used to derive the core processes(components) that are to be included in the model. There was some situational informationrequired which included describing where the metric are to be measured. The rationale for this isthat it would not be possible to identify the interconnections between actors in the supplyproblem if they are not described. The procedure to follow had to aid in the extraction of theSCOR information by selecting the metrics relevant for the performance attribute and extract thecalculations, data sources and how the metric is measured and where. These considerations wereincluded in the formulation of the detail of the phase, shown in table 7.6. 145
    • 2 Table 7.6 Detailed steps for phase two of the SCMPhase two: Determine how each objective will be measured Process steps Information required Information providedPoint of entry:From phase 1: Description of the objective(s) of study (information provided from step 1.1) Use SCOR level 1 strategic metrics (SCOR v.9, section 2) to decompose each supply 2.1.1 Specify the high level chain performance attribute. strategic level 1 metrics Objective(s) of study (from associated with each 1.1). performance attribute How each objective is to achieve each improvement (from 1.4) Specify the supply chain Use SCOR level two diagnostic performance measures for each 2.1.2 Specify (if any) the level metrics (SCOR v.9, section 2) Hierarchy of supply2.1 objective, in the context of how two metric(s) that provide the to decompose each level 1 chain metrics to each objective is to achieve each lower level calculations for each metric if necessary. measure each improvement higher level strategic level one How each objective is to objective metric achieve each improvement (from 1.4) Use SCOR level three diagnostic metrics (SCOR v.9, 2.1.3 Specify (if any) the even section 2) to decompose each lower level three diagnostic level two metric if necessary. metric(s) for each higher level How each objective is to metric achieve each improvement (from 1.4) Use SCOR metric calculation 2.2.1 Define the calculation for Define how each performance descriptions for each metric List of calculations each metric metric will be calculated from the (SCOR v.9, section 2) and data source2.2 data sources that are used to 2.2.2 Specify the processes that Use SCOR data collection requirements for each drive the calculation generates each data source for descriptions for each metric metric each metric (SCOR v.9, section 2) 2.3.1 Specify the actors associated with each data source Derive from the supply Definition of the Define the nature of the process problem statement (from nature of2.3 measurement for each supply 2.3.1 Specify the measurement phase 1) and class of measurement for chain performance metric span for each performance measures (from 2.1) each metric metric Check the outputs from 2.1 – Check that the outputs provided from steps 2.1 – 2.3 correctly Description of how 2.3 with the objective2.4 interpret the objective(s); make any alterations as necessary before each objective will be described in 1.1, if necessary exiting phase two measured confirm with the clientPoint of exit:To phase 3: Proceed only when the description of how each objective that will be measured is agreed as ‘correct’Output: Description of how each process is to provide data used to calculate each objective These considerations were included in the formulation of the detail of the phase. In particular the applications to the cases demonstrated it was of critical importance to structure and describe the problem correctly. This is the case because the ‘core’ components to be included in the model are derived from the supply problem and the boundary of the model is formulated by considering the relationships between ‘core’ components and the supply setting. 7.3.2.1 Step 2.2: Specify the class of SC performance measure for each objective The objective that is described in terms of the impact upon a supply chain performance attribute can be decomposed into specific performance metrics at the desired level of detail. SCOR provides a hierarchy of metrics for each performance attribute. An example of the hierarchy of metrics for the reliability performance attribute is shown in figure 7.4. The modeller will need to 146
    • decide upon the metric at the different levels of decomposition that are most suitable to measure how an objective will have an impact upon an objective. It was evident from the applications that the correct metric needs to be defined otherwise the data sources provided will be incorrect. This resulted in the need for a check at the end of the phase. Figure 7.4 ‘Reliability’ metric structure with an example of a level 3 metric Source: SCOR Supply Chain Operations Reference Model Version 9.0 (2008, section 2.1.2 and 2.1.13) The BeerCo objective can be broken down into one high level strategic metric and two diagnostic metrics. At level 1 ‘CO.1.1: total supply chain management costs’ are calculated from a level two metric, ‘AM.2.8 Inventory: capital employed in finished goods stock’. The description of how an improvement is to achieve an objective specifies that orders must be delivered in full as stock- outs are unacceptable. There are many reliability metrics but the one associated with the “in-full” aspect is ‘RL.2.1 % of orders delivered in full’. The CarCo objective refers to the luxury automobile manufacturer receiving seat sets on time to the specific daily call in requirements. Therefore, a level 3 metric ‘RL.3.20 % orders received on-time to demand requirement’ can be identified from the SCOR description of metrics (see figure 7.4 above). A summary description of the supply metrics for both cases can be seen in table 7.7. Table 7.7 Description of the supply chain metrics BeerCo development case CarCo development case 1. R.L.2.1 % of orders delivered in full 2. CO.1.1 Total supply chainHierarchy of supply chain metrics to From R.L.3.20 % orders received on-time to management costmeasure each objective step 2.1 demand requirement 3. AM.2.8 Inventory: Capital employed in finished goods stock 147
    • 7.3.2.2 Step 2.3: Define how each performance metric will be calculated from the datasourcesThis step is included as it provides information on how a performance metric is to be calculatedand the data sources that provide the necessary information to drive the calculation. This isimportant because the processes and the information it provides are critical components thatneed to be included in the model boundary. For each of the metrics described in the previoussteps, it was easy to extract the corresponding information from the SCOR descriptions for eachperformance metric (figure 7.5 provides an extract from the SCOR metric descriptions to describehow to calculate the metric, what data is required and from which components). It must benoted that this can only be completed successfully if the correct metrics have been selected. Asummary description of how each performance metric will be calculated from the data sourcesfor both cases can be seen in table 7.8.Figure 7.5 Calculation and data collection needs for RL.2.1 % of orders delivered in fullSource: SCOR Supply Chain Operations Reference Model Version 9.0 (2008, section 2.1.3)The RL.2.1 % of orders delivered in full is driven by data primarily associated with the originalorder processing step of ‘Reserve inventory and determine delivery date’ (D1.3), inventoryavailability (M1.1) including location accuracy (ED.4), and the satisfaction of that commitmentthrough shipment and customer receiving processes (D1.12, D1.13). In the BeerCo developmentcase the wholesaler does not manufacture the product, so this can be ignored, but stores stockedproduct which is satisfied by the D1.3 process. SCOR provides a definition of the calculation toinclude [total number of orders delivered on the original commitment date]/ [total number oforders delivered] x 100%. The data collection needs, for the total supply chain cost is calculatedfrom the sum of A.M.2.8 inventory at each actors ‘receive product from source’ (D1.8) process,expressed in dollars. The level 2 metric used to calculate ‘orders received on time to demandrequirement’ in the CarCo problem is collected from ‘receive product from supplier’ (S1.2). SCORdefines the calculation for this as the number of orders that are received on-time to the demandrequirements divided by the total orders for the demand requirements in the measurementperiod. 148
    • Table 7.8 Description of calculation and data source requirements for each metric BeerCo development case CarCo development case 1. [Total number of orders delivered on the original commitment date]/[total number of orders delivered] X 100% The number of orders that are received on-time toList of calculations and From 2. Sum of finished goods inventory the demand requirements divided by the totaldata source requirements step stock cost at each actor location orders for the demand requirements in thefor each metric 2.2 3. The amount of finished goods measurement period inventory (stock) on hand to support customer service, expressed in dollars 7.3.2.3 Step 2.4: Define the nature of measurement for each performance metric The nature of the measurement for each performance metric is included to ensure that the modeller pinpoints the actor who calculates the metric across a particular measurement span (e.g. activity, process, organisation, and supply chain). Failure to do so will mean that it will not be possible to formulate the model boundary from the core processes and data requirements identified. The BeerCO objective is to understand the impact of the improvement on the delivery reliability from the wholesaler to the retailer. Inventory cost is calculated from the factory, distributor, wholesaler and retailer. Therefore, only the wholesaler includes the process element (ED.4 – manage finished goods inventories, D1.3 - reserve inventory and determine delivery date, D1.12 - ship product and D1.13 - receive and verify product by customer) associated with the R.L.2.1 % of orders delivered In full metric; and all actors include D1.8 - receive product from source or make to calculate CO.1.1 - total supply chain management cost and AM.2.8 - inventory metrics. The CarCo objective (RL.3.20 - % orders received on-time to demand requirement) is concerned with the reliability of deliveries at the luxury automotive location (S1.2 – receive product); no metrics are calculated at any other actor location. Table 7.9 Description of the nature of measurement for each metric in both development cases BeerCo case CarCo case 1. D1.3, D1.12, D1.13 (wholesaler process)Description of the nature of From step S1.2 (Luxury automobile 2. D1.8 (all actors – supply chain)measurement for each metric 2.3 manufacturer – activity) 3. D1.8 (Factory, distributor, wholesaler, retailer – process) 7.3.2.4 Step 2.5: Check the descriptions in phase two for ‘correctness’ A check is required at the end of phase two to ensure that the metrics selected in step 2.1 correctly interpret the objective(s) described in step 1.1 and that the associated detail has been described accurately. Of particular importance is defining the actors that provide each metric as this is specific to the supply setting. SCOR only provides data sources from within one organisation. This is important because the formulation of the model boundary is founded on identifying the interconnections between processes and with actors in the supply setting. The 149
    • applications showed that an incorrect description of how each objective is measured with the appropriate details will not allow the model boundary to be determined successfully. 7.3.3 Phase 3: Determine how each improvement is to be represented Phase 3 implements the third key concept identified in the outline design: SCOR practices can be used to describe each improvement to be evaluated. The phase is entered after successfully completing phase two although it is not dependent upon this phase. It can only be entered when the supply problem has been agreed as ‘correct’ in phase one. This is important as the information required by the phase is gathered from the description of the improvement(s) (step 1.2). There were seven observations made when implementing key concept three into phase three of the SCM2, shown in table A.3 in appendix A. These principles/observations are included in the design of phase three, shown in table 7.10. Table 7.10 Detailed steps for phase three of business methodologyPhase three: Determine how each improvement is to be represented Process steps Information required Information providedPoint of entry:From phase 2: Enter phase three when the description of each objective that will be measured is agreed as ‘correct’. The informationrequired from a previous step includes the description of the improvement(s) provided in step 1.2.Participants note: Phase three is not dependent upon the output from phase two, it could be completed simultaneously but phase fourcannot be initiated until both phase two and three is complete 3.1.1 Specify the level 1 top level (process types) Use SCOR v9.0 best practice guide described in that define the scope and section 4 to identify process types associated with content each improvement identified in phase one. If no 3.1.2 Specify the level 2 practices can be identified for each improvement configuration (process List of processes at Define the level of then the processes must be selected that are categories) to implement three levels of process process detail for critical to represent each improvement (verified3.1 an organisation detail that represent each supply chain with client sources). operations strategy each supply chain improvement option Consult the client on how each improvement will 3.1.3 Specify the level 3 improvement option affect the supply system – the effect of each process elements improvement (on processes in the supply system) (decomposed processes) also needs to be included in the description of the that “fine tunes” an improvement organisations operations strategy List of actors Specify the actor(s) associated with implementing Derive from phase one statement of the supply3.2 associated with each each business process problem, verified with the client if necessary business process Check that the outputs provided from steps 3.1 – Check the outputs from 3.1 – 3.2 with the Description of how3.3 3.2 correctly interpret the improvement(s); make improvement(s) described in 1.2; if necessary each objective will be any alterations if necessary before proceeding confirm with the client measuredPoint of exit:To phase 4 Proceed only when the description of how the processes represent each improvement is ‘correct’Output: Description of how the processes represent each improvement Similar to phase two the cases show that the steps are relatively easy to follow with information provided by SCOR practices. It is important to note that this phase can only be completed if the improvement(s) have been described in enough detail and verified with the client. The phase not only provides the processes and actors that need to be included in the model but also provides an 150
    • indication of the level of decomposition necessary to evaluate each improvement. The processesidentified are treated as ‘core’ processes that are used as a starting point to formulate the modelboundary.7.3.3.1 Step 3.1: Define the level of process detail for each improvementThe level of process detail for each improvement can be obtained from the SCOR practicedescriptions. This is the case if the described improvements are aligned to one or more practicedescribed in SCOR. If this is not the case the process descriptions need to be used to identifywhich processes would need to be represented for each improvement. Likewise, for thedescription of objectives, SCOR only describe the processes. The user must decide who isresponsible for implementing each process. These requirements have been incorporated into thesteps.Figure 7.6 Extract of the SCOR descriptions of best practicesSource: SCOR Supply Chain Operations Reference Model Version 9.0 (2008, section 4.1.50)The BeerCo development case covers two practices in SCOR in relation to the visibility ofinformation and how the information will be used by the distributor. The process that representsthe visibility of information from the wholesaler includes D1.2 (receive, enter and validate order) 151
    • and D1.3 (reserve inventory & determine delivery date). This information is used by the distributor to plan deliveries (P4: plan deliveries) and decide how orders are to be placed with the factory (P2: plan source). On the other hand, the information to be shared (daily call in) in the CarCo case is generated by the P2 (plan source) and sent via a replenishment signal that continuously broadcasts the target launch sequence (S1.1: schedule product deliveries). This is received by the tier 3 manufacturers (central head-rests and tracks) so that they can adjust their kanban set-up to reflect daily call in requirements (P4: plan deliver). Table 7.11 List of processes at three levels of process detail that represent each SCIO BeerCo case CarCo case 1. Level 2 (process 1. Level 3 (process element): D1.2 (Receive, categories): P2 (planList of processes at three levels of process enter and validate orders); D1.3 (Reserve source); P4 (Plan deliver)detail that represent each supply chain inventory & determine delivery date) 2. Level 3 (processimprovement option 2. Level 2 (process category): P2: (plan source); elements): S1.1 (Schedule P4 (plan deliver) product deliveries 7.3.3.2 Specify the actor(s) associated with each process The specification of the actors associated with each process can be completed at the same time as step 3.1. The descriptions provided in the previous section illustrate how the actors are to be affected by each improvement. The step needs to be included as the actor associated with each process is specific to the situation. An observation can be made about SCOR when describing business practices; it would be useful if SCOR could indicate how practices are implemented by processes across different actors. In some cases, such as VMI (c.f. section 5.1) the improvement makes it explicit that some process elements are implemented in the supplier (or even the customer) as well as the organisation. Therefore, this step is made explicit to ensure that the actors are correctly identified as this has a bearing on the analysis that forms the model boundary. Table 7.12 List of actors associated with each business process BeerCo case CarCo case 1. Implemented at the 1. P2 implemented at the Luxury automotive manufacturer; P4List of actors associated with wholesaler implemented at both the central-headrest and tracks tier 3each business process 2. Implemented at the suppliers distributor 2. Implemented at the luxury automotive manufacturer 7.3.3.3 Check the outputs in phase three with the improvement(s) A check is needed to ensure that each improvement has been correctly interpreted into SCOR business processes. In the two cases this was relatively straight-forward as the improvements were aligned with existing SCOR practices. Ensuring the correct actors is listed for each business process should be checked and, if necessary, verified with the client. If an improvement is not described by SCOR, or even, more than one practice is used, then it is necessary to justify why 152
    • each process is said to be critical as this has a direct bearing on the formulation of the modelboundary.7.3.4 Phase 4: Determine how the inputs and their sources interconnectPhase four implements two key concepts, four and five identified in the outline design: Identification of the core processes that need to be modelled, their inputs generated from a source process element Process elements that have yet to be included in the model can be classed as candidates for possible inclusionThe phase is entered in the first instance from both phase two (processes that provide a datasource) and phase three (processes that represent each improvement) which provide informationon the process that need to be included. These are classed as the ‘core’ process elements to beincluded in the model. The processes are critical and the interconnections between theseprocesses in the supply setting need to be determined to formulate the model boundary. SCORdescribes the inputs to each process therefore, the relationships (interconnections) betweenprocesses can be determined. The importance of the step is to identify interconnections thathave not been included so that they can be considered for possible inclusion based upon rulesthat aid the formulation in the model boundary (in phase 5). There is also a second point of entryin phase four, from phase five, when a process has been ‘promoted’ for inclusion in the model.These ‘promoted’ process elements must be treated in the same fashion as ‘core’ processelements. During the process of promoting process elements it is expected that these may havealready been included so they no longer need to be treated as ‘candidates’ for possible inclusion.There were eleven observations made when implementing key concepts four and five into phasefour of the SCM2, shown in table A.4 in appendix A. These principles/observations are includedinto the design of phase four, shown in table 7.13.The development cases demonstrate that the phase involves the most time to extract and toidentify the candidate processes. However, the processes and relationships described by SCORcan be used as a way of identifying the critical links between the improvements and the objectivesand those in the supply setting. This satisfies one of the most difficult aspects of conceptualmodelling; determining the components and interconnections that need to be included in amodel. In this phase it is the case of discriminating between processes and inputs that areincluded in the model or not. This requires no interaction with the client or great effort other thandiscriminating inputs to identify candidate processes. Additionally for both the development 153
    • cases it provided a starting point to begin the analysis aided in the determination of the model boundary. 2 Table 7.13 Detailed steps for Phase 4 of the SCMPhase 4: Determine the model inputs and source process elements within the model and with its immediate supply setting Process steps Input to step Output from stepPoint of entry:From phase 3: Enter phase four when the description of how the processes that represent each improvement have been confirmed as‘correct’From phase 5: The phase is re-entered to consider each ‘promoted’ process element identified in phase five From phase two and three for 4.1.1 List all the process elements core process elements. included in the model Phase five for promoted process elements. Use SCOR (SCOR, v.9 section 3): 4.1.2 Add the inputs to be fed to each Processes to identify the input process element included in the model descriptions for each process List of inputs to each List the inputs fed element process element included in to each process 4.1.3 Add the source process element4.1 the model, their source element included that generates each input to be fed to process elements, specified in the model a process element included in the As above by actor model, consolidating the list where possible As above, but specify the actor 4.1.4 Specify the actor that generates using the description of the supply the input from the source process problem as a guide (from step 1.4 element - verify with the client if necessary) List of model inputs and Discriminate the source process elements that generate each Verify using output from step 2.4 candidate process elements4.2 input to be fed from a process element included in the and 3.3 for possible inclusion in the model modelPoint of exit:To phase 5: Proceed when all source process elements have been discriminated. The phase is complete after each round and when nofurther processes are ‘promoted’ in phase 5.Output: List of model inputs and candidate process elements 7.3.4.1 Step 4.1: List the inputs fed to each process element included in the model It was relatively straightforward to list the inputs and source process elements for each included process element. This was because SCOR describes the relationships between processes and the task involves assembling the information in a meaningful way. An example of S1.1 which is a core process element in the CarCo development case and promoted in the BeerCo development case is shown in figure 7.7. Figure 7.7 Example of the inputs of a source process element described in SCOR Source: SCOR Supply Chain Operations Reference Model Version 9.0 (2008, section 3.2.5) 154
    • SCOR provided all the configurations (for MTO, MTS and ETO environments) which is useful butmultiplied the analysis. Although both the BeerCo and CarCo development cases had MTSconfigurations it could be envisioned that multiple environments could be used. To eliminate thenumber of entries it was noted in the procedure that different environments can impose theinterconnections to be selected. For both the BeerCo and CarCo development cases the MTO andETO interconnections were ignored.It was found that it was not clear how each process element could connect with a processelement, with another actor. For instance, S1.1 (schedule product delivers) has an output thatgenerates the procurement signal to the supplier but does not state how this signal is received bythe supplier (see figure 7.7). It is the D1.2 (receive, configure, enter & validate order) process atthe supplier location which receives the customer replenishment signal, deliver contract termsand customer order. Due to the generic nature of SCOR this link is not made explicit. Therefore,there was a need to walkthrough each of the SCOR interconnections and produce a set ofdocuments for each of the production environments and clear up any loosely definedinterconnections. These data files were formatted to support the analysis in phase four whichspeeded up the process considerably. An extract of S1 source stocked product is shown in figure7.8.Figure 7.8 Process elements, inputs, source process element and suggested source actor 155
    • The presentation of the information was also important. The SCOR detail had to besupplemented with the actors associated with the source and destination process element. Forinstance, in both cases information is being supplied from a source external to the organisation.This requires the modeller to verify each input source and destination process elements.Additionally, the presentation of the information needed to ensure that there was no unnecessaryduplication. The BeerCo development case generated a list of 371 entries, while the CarCodevelopment case included 402 entries without any duplication. This was considerably higherwhen all production environments were included in the analysis. An extract of theinterconnections considered for the S1.1 process element in the CarCo development case isshown to demonstrate the standard layout adopted, in figure 7.9. A solution to this problem isdiscussed in the section 7.4 (by automating the process).Figure 7.9 Extract of the list of inputs considered for S1.1 in the CarCo development case7.3.4.2 Step 4.2: Discriminate the source process elementsIt was straightforward to discriminate the source process elements to identify if they had beenincluded in the model. This was addressed by asking whether the source process element (thatgenerates each input to be fed to an included process element) currently exists in the model. Thispresented no problem as long as the data was presented in a standard format (shown in figure 7.9and figure 7.10). 156
    • Figure 7.10 Extract of how phase four was completed for the CarCo development caseEach input had to be treated separately due to most being an input to more than one process andthe complexity of different actors. It was found that it was the combination of source processelement, actor and input that was being discriminated against. For instance, figure 7.8 shows S1.1(schedule product deliveries) as an input to the ‘replenishment signal’ which can be sourced fromboth M1.2 (issue material) and D1.3 (reserve inventory and determine delivery date). Anotherexample includes S1.1 as an input ‘logistic selection’ that is considered in both the LA and the 3PL.7.3.5 Phase 5: Formulation of the model boundaryPhase five implements two key concepts, six and seven identified in the outline design: Candidate process elements are considered in turn for inclusion in the model, as they form a critical interconnection between the processes and the real world Included process elements are considered in turn to identify those that could be simplifiedThe boundary is formulated by deciding whether an input fed from a candidate process should beincluded or excluded from the model and whether it could be simplified (e.g. fixed value or simpledistribution). The phase is entered from phase four where candidate process elements and theirinputs, that interconnect with processes that have yet to be included, are identified. It is exitedfor two reasons: firstly, when a candidate process element has been ‘promoted’ it triggers theneed for iteration (to repeat phase four based upon additional information) and secondly, whenno more process elements have been identified (to phase six). There were sixteen observationsmade when refining and implementing the two key concepts into phase five, shown in table A.5 inappendix A. These observations were incorporated into the phase shown in table 7.14. 157
    • The development case demonstrates that the phase structures the decision process, uses domainknowledge to identify interconnections between processes and provides a means to documentany justification for the decisions made (i.e. simplified, promoted, tested or excluded). Thedecision rules that were considered for each input and candidate process were subjective but itcould be observed that participants could benefit greatly from the information provided fromSCOR. It provides a focus for participants to identify any misunderstandings and need forclarification which would involve consultation with the client (particularly SMEs). On the wholethe boundary could be identified relatively easily and efficiently using domain knowledge.Particularly, the interconnections have been defined by SCOR, which can be used to form adetailed understanding of what needs to be included in the model (included processes need to bemodelled in detail, simplified inputs can be represented as a fixed input or distribution). Theprocess elements that needed to be included but could be simplified indicated the boundary ofthe model. 158
    • 2 Table 7.14 Detailed steps for phase 5 of the SCMPhase 5: Formulate the model boundary Process steps Input to step Output to stepPoint of entry:From phase 4: Proceed in successive rounds between phase four and five when the candidate process elements have been identified. Ifthere are no candidates to be considered then no further iteration is necessary. Use the list of candidate List of inputs fed process elements from 4.2.1 List all the inputs generated and fed by each candidate process from each candidate5.1 and 4.2.2, list of inputs from element, specified by each actor (if necessary) process element (for 4.1.1, Derive actors from 2.2 each actor) and 3.2 5.2.1 SIMPLIFY candidate process element that WILL generate inputs that will effect model behaviour by significantly impacting on performance measures AND CAN be simplified in a simplified form (either a fixed value or distribution) 5.2.2 PROMOTE candidate process element that generate inputs that will effect model behaviour by significantly impacting on the performance measures Determine whether to List of SIMPLIFIED, AND CANNOT be simplified in a simplified simplify, promote, test Use the SCOR input PROMOTED, form (either a fixed value or distribution) or exclude each descriptions (SCOR v.9, section TO TEST or5.2 5.2.3 TEST candidate process elements if a candidate process 3) and verify with the client if EXCLUDED process decision cannot be made about whether element by applying rule necessary elements/inputs the input to be generated will have an 1 and 2 with justification effect on model behaviour by significantly impacting on performance measures 5.2.4 EXCLUDE candidate process elements if the inputs to be generated WILL NOT affect model behaviour by significantly impacting on performance measures 5.2.5 For each decision provide some rationale and justification for the choice made If any process elements were PROMOTED then go to If any process elements were PROMOTED in step 5.2.2 then repeat phase four and steps 5.1 – 3;5.3 phase four and five until no process elements can be included in the When no process elements can no longer be model and inputs to be fed can be SIMPLIFIED. PROMOTED then proceed to step 5.4. Use Information from step 1.5 5.5.1 In a table list each actor and note and 2.4 (core process which CORE and PROMOTED process elements); step 5.2 (promoted elements and SIMPLIFIED input are process elements and included in the model simplified inputs) 5.5.2 For each improvement that could achieve each objective. Trace the inputs Use information provided from processes that provide data sources Check that there are from step 1.4 (description of to drive the calculation for each objective sufficient and correct how each improvement could to the point that a sufficient and correct linkages between achieve each objective); 5.2 List of processes and link is made with the processes that processes and inputs (interconnections between inputs included in5.4 represent an improvement. Use the table included in the model processes and inputs) the model specified to tick off each process that provides a and that there are no by actor link. isolated process 5.5.3 Any remaining ‘isolated’ processes elements in the model and simplified inputs should be checked using the justifications provided to verify step 5.3 (justification for each that they are necessary as they effect decision) model behaviour by having a significant impact upon the objectives of study 5.5.4 If any isolated process elements or inputs exist that cannot be justified then Use information from 5.5.3 return to step 5.2.Points of exit:To phase 4: Iterate between phase 5 and 4 for each ‘promoted’ process identified in phase five. The iteration is no longer necessarywhen there are no more inputs to be considered.To phase 6: Proceed when there is sufficient linkages between the processes in the model and the supply settingOutput: List of processes and inputs included in the model 159
    • 7.3.5.1 Step 5.1: List all the inputs generated and fed by each candidate process elementThe list of inputs generated and fed by each process element could be obtained from theinformation provided from phase four. The key decision made in the design of this step was howto present the information so that it is compatible with the preceding analysis. In earlier versionsthe table was formatted to present the candidate process element, input it feeds to a process thatis already included in the model. This involved changing the way in which the data was displayed.It was decided to use an identical format used in phase four so that the material could be easilyextracted. An example of the information that was extracted when considering the inputs fed toboth D1.2 and D1.3 for the warehouse in the BeerCo development case is shown in figure 7.11. Itcan be seen that two interconnections already exist but twelve need be considered for inclusionin the model boundary. Only the information for candidates was transferred to the table in phasefive.Figure 7.11 Extract of the output from phase four that is transferred (in step 5.1)The step was considerably time consuming as it involved manually selecting entries that wereindicated as ‘no’ and transferring this to the table in phase five. It was speeded up by consideringthe format of the layout and completing the stages in rounds (iterating between phase four andfive, when candidate processes are promoted). The process was necessary and benefited greatlyfrom using the relationships described between processes in the SCOR model but it was observedthat this step could involve no human interaction. It could be effectively automated meaning noeffort from participants (discussed in section 7.4). 160
    • 7.3.5.2 Determine whether to simplify, promote, test or exclude each candidate processelementAt the heart of the methodology is the decision of how to determine the model boundary. Theonly guidance that is offered is in Robinson (2004, pg. 84) which included in the observation list inappendix A. This is achieved by applying two rules to each input from a candidate processelement. The issue was to determine which interconnections were critical, to ensure a sufficientlink between the processes included for each improvement and the metric it has an impact upon.In addition, to identify process elements that needed to be tested because a judgment could notbe reached, and to exclude any unnecessary processes.The key issue in this step was to determine how to treat each interconnection between thecomponents included in the model and the supply setting. The decision is subjective but canbenefit significantly by having the necessary information at hand. Two sources exist for thisinformation from SMEs and utilisation of the descriptions in SCOR. The aim was to identify a wayin which to provide a sufficient link between the ‘core’ processes that represent an improvementand how this impacts upon the processes that provide the data required to calculate a metric (C.f.section 4.1.1).In the case of BeerCo the improvement was being made by the wholesaler, but influencing theway in which the distributor planned its deliveries, and how it is ordered. Hence all criticalprocesses between the wholesaler inventory reservation, shipping and receiving processes thatprovide information to the distributors plan, source, and deliver processes need to be made. TheBeerCo development case measures the impact of the improvement in relation to the total cost ofinventory at each of the actors and the wholesaler delivery performance. The links therefore hadto include the previous processes and the ‘receive product from source’, at each actor (thisincluded an output that determined how much inventory was available), the wholesaler inventoryreservation (D1.3), product shipping (D1.12), and receiving at the retailer (D1.13), to fulfill the “in-full” aspect of the metric. There were a host of processes that needed to be considered todemonstrate this link. The model boundary steps were able to take the participants through theprocess, by considering each connection in turn. This was satisfactorily achieved by asking thequestion: Will the input, to be generated from the candidate process element, effect modelbehaviour by significantly impacting on the objectives of study?In both development cases there were a considerable number of interconnections to consider(288 in the BeerCo development case and 354 in the CarCo development case). The main reason 161
    • for this is that SCOR has specific entry points (e.g. sending an order to a supplier) and exit points(e.g. installing product to a customer) between actors. This led to all the links between thesepoints being considered in light of the problem even when the value added by a particular processis not significant and adds unnecessary details. This was seen most notably in the CarCodevelopment case, when the improvement is between the luxury automobile manufacturer andthe tier 3 suppliers, with no core process elements in the third party logistic supplier or the seatsupplier. This resulted in the need to introduce an additional concept, by effectively treating alink, which performs activities that will not have an effect on model accuracy, as a ‘phantom’. Inessence the link is critical because it provides a workflow input (e.g. product flow) but there areno activities that ‘add value’ to the input so the process does not need to be modelled in anydetail. For example, in the BeerCo case, picking and packing will not have an impact upon modelbehaviour because the product is not transformed in any way between actors but the productflows through these activities. Although this decision concerns what to include, it is necessary toinclude the interconnection but not to model any practices in any detail as they are not significant(considered in next phase when the practices to be modelled are detailed).The rules were useful when deciding what to include, or exclude, and the step also recognised theimportance of documenting the rationale for the decision made. It was useful when a link wasdeemed to have a sufficient impact on model behaviour that it was considered in light of whetherit could be simplified. This was important as it was observed that the inputs that could besimplified made up the boundary of the model, which has not been discussed in the literaturebefore. A simplified input is one that can be fixed (e.g. lead times in both cases) or can begenerated by a simple distribution (e.g. end customer demand in both cases); there are no furtherinputs to consider. Additionally, another key problem that was addressed was the process offormulating when the model boundary ends. This was achieved once all inputs had beenconsidered in the model boundary (inputs had been simplified, excluded or needed to be tested)and there were no more ‘promoted’ process elements to trigger the need to return to phase four.The test feature (for decisions that were ‘unsure’) was not used in either of the cases but it couldbe observed that this decision is beneficial when formulating the model boundary at theconceptual modelling stage. At the conceptual modelling stage it can be used to pinpoint thecomponents of the model that should be analysed using a prototyping method, by building asimple computer model and providing insights into the key variables and interconnections inorder to design the conceptual model (Robinson, 2004a; 2004b). It, therefore, provides someuseful insights so that a decision can be reached at the stage of formulating the model boundary. 162
    • The ‘unsure’ decision was made in earlier refinements but it was concluded that this was down toearlier attempts at designing the decision process. All the ‘unsure’ decisions could be discountedat the end of the process. At this point there is a need to gain information from SMEs, thequestion a modeller would need to address is: what information is required? It could be observedthat the domain knowledge provided from SCOR coupled with the process could facilitate thedecision-process and provide information that will aid the modeller to identify the informationrequired. Although in most cases the information provided by SCOR was sufficient to be able tomake a decision (e.g. descriptions were provided about each input). When performing a check ofthe linkages in step 5.4 it was apparent that the candidate process elements were needed. In thisrespect, the process of learning, through considering and reviewing the linkages, increased theunderstanding of the model requirements, or it indicated a part of the model that should be apriority for communicating with the client. Figure 7.12 shows how the information was displayedand decisions reached with justification.Figure 7.12 Extract of phase five from the BeerCo development case for the Wholesaler7.3.5.3 Step 5.3: Repeat phase four and five for each PROMOTED process elementsEach promoted process element was added to the list of model inputs and candidate processelements. This initiated step 5.1 – 3 until no process elements could be considered. During theprocess, it was observed that towards the end of the step, the modeller would notice that themajority of candidates have already been included in the model. Alternatively, they have beensimplified so no inputs need to be considered, thus indicating the process had been completed. 163
    • 7.3.5.4 Step 5.4: Check the linkages between processes and inputs in the modelA step was added to check that the linkages between the processes and inputs included in themodel are ‘sufficient’ and ‘correct’ before proceeding. For example, the links between coreprocess elements included in the model and the processes and inputs that have been includedfrom the supply setting. It is important to note that the model up until this point has beenformulated by considering each interconnection (addressing the principle ‘start small and add’)and whether it should be included based upon two rules.The problem identified in the previous step was that the decisions reached on eachinterconnection were subjective. To improve decision-making, information was gathered fromSCOR descriptions and SMEs when necessary. It does not satisfy the requirement that sufficientand correct links exist between each improvement that could achieve each objective and those inthe supply setting.To facilitate this problem a matrix was drawn up that listed each of the actors in the supplysystem and the processes included for each actor (illustrated in figure 7.13). This was deemeduseful as it visually represented all the core, promoted and simplified inputs. It was used to checkthe two different types of interconnections in the following two ways: 1. For each improvement that could achieve each objective. Trace the inputs from processes that provide data sources to drive the calculation for each objective to the point that a sufficient and correct link is made with the processes that represent an improvement. Use the matrix to tick off each process that provides a link. 2. Any remaining ‘isolated’ processes and simplified inputs should be checked using the justifications provided to verify that they are necessary as they affect model behaviour by having a significant impact upon the objectives of study. 164
    • Figure 7.13 Template used to check the linkages between processes in the CarCo development caseThis procedure was used successfully in both development cases. The matrix was used as areference point and a way to highlight each process as the check was being completed. For bothdevelopment cases there were sufficient links between the improvements and objectives.In the CarCo development case the improvement is implemented in P2 (plan source) in the LA andin P4 in the tier three suppliers (T and CHR). The change brought about by this improvementimpacts upon the behaviour of the supply system and is ultimately measured at S1.2 (receiveproduct) in the LA. Each of the included ‘promoted’ processes and ‘simplified’ inputs can bechecked along with the justifications stated for each decision to check that it should be included.Using an Excel spreadsheet (shown in figure 7.14) this could be completed using the filteringfunctions, selecting the included processes and each interconnection and ticking the matrix whenrequired. In this way all the different routes could be checked so that the justifications could bechecked. This also included some ‘dead-ends’, which were necessary such as planning orders,deliveries and production. Additionally the manufacturing processes were included in both T andCHR as they produced tracks and central head-rests which are both components of the seat set. 165
    • Start at S1.2 LA D1.13 SS to S1.2 LA D1.12 SS to D1.13 SS D1.11 SS to D1.10 SS D1.10 SS to D1.9 SSFigure 7.14 Tracing back the inputs of included processes from a data source7.3.6 Phase 6: Design of the detail of the modelPhase six implements two key concepts identified in the outline design: The detail that needs to be included can be identified from the actual practices for each process element included and simplified in the model Modelling practice should represent the complexity and detail of the actual practice to be evaluatedThe aim is to firstly provide a detailed representation of the supply problem to be modelled (thedomain-orientated model). From this describe the requirements of the model in terms of how thesupply problem will be represented by the components in the computer model (the design-orientated model). The phase is entered when the list of processes and inputs to be included inthe model have been checked. Information provided to the phase firstly is extracted from theSCOR model to identify typical practices for each process element included in the model boundaryand the actual practice adopted for each process is identified from consultations with SMEs. 166
    • 2Table 7.15 Detailed steps for phase 6 of the SCMPhase 6: Determine and design the level of detail for each process element and input included in the model Process steps Information required Information providedPoint of entry:From phase 5: Enter phase six when the list of processes and inputs to be included in the model specified by actor have been checked 6.1.1 List the process elements List of process elements Use the list presented in within the boundary of the specified by status for phase five model, by status and actor each actor 6.1.2 Identify ‘phantom’ process elements by determining the Use the list from phase five process elements that contain to identify the inputs to List of process elements Describe how each process only a workflow input and no each process element; that are to be treated as element is actually practices can be identified that Verify if necessary with the ‘phantoms’ implemented in practice, in would influence the client sufficient detail of how the characteristics of the workflow6.1 inputs (and sources) are Use SCOR v.9 process converted to produce an element descriptions (SCOR 6.1.3 Describe actual practice for List of descriptions of output (to a destination), v.9, section 3) to identify the existing ‘AS-IS’ and ‘TO-BE’ actual practice for each specified by each actor practices from the list of supply system for ‘core’ and included ‘AS-IS’ and ‘TO- candidates, as a guide to aid ‘promoted’ process elements BE’ process element in the collection of information from the client 6.1.4 Consolidate the list of Consolidated list of actual practices, removing any Use the list from 6.1.3 actual practices duplications 6.2.1 Describe what processes, Description of the activities or events need to be Use descriptions processes, activities or included in the computer model consolidated in 6.1.4. events to be included in to represent each actual practice the computer model 6.2.2 Describe what entities Description of the Describe how each actual need to be included to represent Use the descriptions from entities to be included in practice, specified by each the activities or events in the 6.2.1 the computer model6.2 actor, will be represented by computer model? the components in the 6.2.3 Describe any assumptions Description of any computer model or simplifications incorporated assumptions or into the description of the simplifications Refinement of 6.2.1 and model to be developed (e.g. incorporated into the 6.2.2. aggregation of model description of the components, reduce the rule components in the set) computer modelPoint of exit:To phase 7: Proceed when the components in the computer model represent all actual practicesOutput: Description of how the components in the computer model and their relationships will represent the actual practices to bemodelled7.3.6.1 Step 6.1: Describe how each process element is actually implemented in practiceThe aim of step 6.1 is to describe how each process element included in the model boundary isactually implemented in practice (components that describe the domain). The previous analysishad used the SCOR model to identify the processes, inputs to each process and the relationshipsbetween the processes included in the model. The information that is missing includes the actualpractices that are implemented within each process.In sub-step 6.1.1 the previous analysis provides the background information. This could be listedwith no problem for both cases. The table that lists each actor and the status of how a process istreated was a useful means of extracting the information easily. Alternatively, it could be listeddirectly from the output from phase five. 167
    • Sub-step 6.1.2 identifies the ‘phantoms’ which are, effectively, processes that have been includedbecause they provide a link that have a significant impact upon the behaviour of the model. Itwas observed that this creates unnecessary detail that could be excluded. Effectively it is theinput that is required but no practices exist, or, a practice exists that will not have a significanteffect on the model accuracy. A process can be viewed using the previous model boundary list toidentify processes that have only workflow inputs. The justification should also provide someguidance. In an Excel spreadsheet the filters can be used to select each process specified by eachactor and see which inputs have been included. A list of ‘phantoms’ is shown for the CarCodevelopment case, with an example of identifying the necessary information for D1.10 SS in figure7.15.Figure 7.15 ‘Phantoms’ in the CarCo development case (inputs shown for D1.10 SS)Sub-step 6.1.3 is the main step in step 6.1 as it describes the actual practice for the ‘AS-IS’ and‘TO-BE’ model. The ‘core’ process elements could be considered first as it is likely that they maycontain the greatest level of detail followed by those that were promoted and then the simplifiedinputs can be described in terms of either being a fixed input or simple distribution. A columnwas added so that the ‘AS-IS’ and ‘TO-BE’ descriptions can be described. The ‘TO-BE’ changesshould be within the ‘core’ process elements that describe the improvement as they are thecomponents that are implementing the change that is being evaluated. It was important toinclude in the descriptions the actor that implements the practice and the relationships betweenthem. This makes it easier to describe the model components in the next step. In the case of theBeerCo case, an extract is shown in figure 7.16. The SCOR descriptions of processes could be usedto identify practices and if no specific practice could be identified the description was useful when 168
    • describing the actual practice. For instance, three examples are highlighted where SCORdescriptions were helpful: 1. S1.1 (schedule product deliveries) at the end customer – The model needs to be able to generate a simplified input of the customer placing an actual order 2. S1.4 (transfer product) at the distributor – The model needs to be able to represent beer being received from the factory and being stocked at the distributor goods receiving location 3. M1.3 (produce and test) at the manufacturer – The model needs to be able to represent the factory manufacturing beerFigure 7.16 Extract of actual practice descriptions in the BeerCo development caseSub-step 6.1.4 recognises that an actual business practice can be implemented in one or moreprocesses. The model components are described from the actual practices and not from theSCOR descriptions as these were only used as a means to identify the structure, content andrelationships in the model. This ensures that the descriptions reflect the real world nature of theproblem. If an actual practice is implemented in more than one process then these can beconsolidated to produce a simpler list of the actual practices to be represented in the computermodel. This must not be confused with grouping of entities as this is a way of representing agroup item in a simulation model. An illustration is shown in figure 7.17 of the CHR CarCoprocesses M1.5 (stage product), M1.6 (release product to deliver), D1.3 (reserve inventory anddetermine delivery date), D1.4 (consolidate orders) and D1.5 (build loads) that are implementedby a kanban practice which controls the movement of central head-rests. 169
    • Figure 7.17 Extract of how actual practices can be ‘consolidated’ for the CarCo development case7.3.6.2 Step 6.2: Describe how the actual practices will be represented by thecomponents in the modelStep 6.2 uses the reduced list of actual practices to describe how each actual practice, specified bythe corresponding actor, will be represented by the components in the computer model. This keyconcept is well documented in the literature in particular simulation texts (e.g. Pidd, 2004a;Robinson, 2004a; 2004b) and, therefore, it is not implemented any differently than in existingmodelling practice. The only difference is that the actual practices have been derived by applyinga structured methodology that utilises the domain knowledge from SCOR.Within different worldviews or even simulation approaches the terminology used to describe thecomponents in a model may vary (C.f. section 2.4.3). The objective in this step is to be generic sothat different terminology can be applied. Pidd (2004a) discussed the standard terminology forDES using some labels that could be used generically: objects that constitute a system to besimulated and the operations in which these objects engage over time. It was observed that itwas straight-forward to be able to describe each actual practice in terms of the activities or eventsthat are needed to be implemented in the model to represent them. This is asked first followedby a description of how the activities/events can be represented in the model. Table 7.16illustrates how these questions have been addressed in the BeerCo development case along witha standard definition for the model component and examples of them. 170
    • Table 7.16 Model components, definitions and examples (in the BeerCo development case) Model Questions asked in step 6.2 Definition BeerCo case examples Components Reserve inventory and A sequence of events in the chronological order determine delivery date, Process in which they occur (Pidd, 2004a; 2004b) plan order, plan deliveries, schedule deliveriesWhat activities or events need An operation over a duration of time of finite Receive product, shipto be implemented in the Activity length that causes change in the system state productmodel to represent the actual (Tanir and Sevinc, 1994).practice? An occurrence of an operation at an isolated Product arrives at point in time; it may change the system state warehouse, customer place Event (Tanir and Sevinc, 1994). It initiates the order, initiate sourcing plan beginning and ending of an activity or delay cycle (Banks, 1999). Individual elements of the system that are being Warehouse, orders, vehicle, Entity simulated and whose behaviour is being explicitly delivery schedule tracked (Pidd, 2004a; 2004b)How will the activities/events Individual elements of the system that are not Number of units of beerbe represented in the model? modelled individually, instead, they are tracked available in warehouse, Resources as countable items whose individual behaviour is number of units of beer on not tracked by the simulation (Pidd, 2004a; vehicle 2004b)The final step is to incorporate any assumptions or simplifications into the description of themodel components. There are many standard discussions on how this can be achieved (c.f. insection 4.1.2). Interestingly the methodology has used a number of simplification methods orprinciples throughout the procedure (e.g. in the model boundary setting: excluding componentsand details in the formulation of the model boundary, replacing components with randomvariables or fixed inputs). A key method of simplification that has not been used but is useful andis specific to simplifying model components includes removing components and interconnectionsthat have little effect on model accuracy (e.g. aggregating model components using represent asection of an operation as a time delay and/or grouping entities). Another simplification that canbe used to minimise the detail in a model includes reducing the rule set (e.g. routes, schedules,allocation of resources) which is described in detail in Robinson (2004b, pg. 90).An example of five actual practices that were described in the BeerCo development case and howthey have been converted into the model components is shown in appendix D. These aredescribed using DES terminology, identifying the entities or resources that constitute the systemand how they engage in time (process, activities or actions). The entities are described by actorand object (actor_object) and described in terms of the attributes that are required by theprocess, activity or action (actor_object.attribute) e.g. ‘Retailer_order: An order from the retailer for units of beer (Retailer_order.qty) for a given date (Retailer_order.date)’ ‘Wholesaler_Warehouse: The units of beer available in Wholesaler warehouse (Wholesaler_Warehouse.AVAILinv)’ 171
    • The process, activities and events are described in sufficient detail so that they can be developedinto a computer model e.g. ‘Retailer player makes a decision on how much to order from the wholesaler in the next period using available planning data. A purchase order is created (Retailer_order) with the order qty (Retailer_Order.qty) and date (Retailer_Order.date) set on the retailer purchase order’ ‘Check to see if any backorders remain (Wholesaler_backorder>0) and create Wholesaler_onorder. It is calculated: Wholesaler_onorder = Retailer_order.qty + Wholesaler_backorder’Simplifications and assumptions are included in the model and documented with the descriptionof the model components e.g.: ‘Include order acceptance rule based on order max size in place order’ ‘All orders received are accepted; Orders are received in each one week period; The order is delayed by 1 week’7.3.7 Phase 7: Validate and document the conceptual modelPhase seven is the final phase of the methodology implementing one key concept: The conceptual model is documented and validatedThe aim of the final phase is to document and validate a conceptual model from the informationprovided from the key outputs in the methodology. The phase recognises that the conceptualmodelling process is iterative (Robinson, 2004b) due to new insights or learning that occurredduring the process. For these reasons it is necessary to ensure that each of the descriptionsprovided from phases or steps in the methodology are ‘correct’. This may result in any necessaryalterations when the validation check has not been satisfied. To improve the validity andcredibility of the conceptual modelling being developed ‘checks’ have been implemented atcertain points within the methodology. This phase implements a final set of checks on the draftconceptual model.The phase is entered after phase six has been completed but each of the steps involves gatheringdescriptions from specific phases in the methodology. The phase has many points of exit whenissues have been identified warranting a need to return to a previous phase or step. When thereare no further revisions or alterations necessary to the draft description of the simulation model 172
    • to be developed the conceptual modelling process is complete. The descriptions contribute todrafting the conceptual model to be developed in terms of Robinson’s (2004b) definition, withinin the context of SCM applications. These include: Purpose of the model - Draft a description of the supply problem from the client’s perspective (from phase 1) Inputs - Draft a description of how each process is to provide data used to calculate each objective (from phase 2) Outputs - Draft a description of how the processes represent each improvement (from phase 3) Content - Draft a description of how the components and their relationships in the computer model will represent each actual practice to be modelled (from phase 6) Assumptions and simplifications - Draft a description of the assumptions and simplifications that have been incorporated into the model components and relationships (from phase 6) 173
    • 2Table 7.17 Detailed steps for phase 7 of the SCMPhase 7: Validate and document the conceptual model Information Information Process steps required providedPoint of entry:From phase 6: Enter phase seven when the components in the computer model and their relationships will represent the actual practicesto be modelled have been described 7.1.1 Draft a description of the supply problem from the client’s From phase perspective 1 7.1.2 Draft a description of how each process is to provide data From phase used to calculate each objective 2 A draft non- Draft the conceptual 7.1.3 Draft a description of how the processes represent each From phase software specific model by describing improvement 3 description of7.1 the computer model to 7.1.4 Draft a description of how the components and their the simulation From phase be developed relationships in the computer model will represent each actual model to be 6 practice to be modelled developed 7.1.5 Draft a description of the assumptions and simplifications From phase that have been incorporated into the model components and 6 relationships 7.2.1 Check the description provided from step 7.1.1 by repeating From 7.1.1 step 1.5; If not correct return to the beginning of phase 1. 7.2.2 Check the description provided from step 7.1.2 by repeating From 7.1.2 step 2.4; if not correct return to the beginning of phase 2 7.2.3 Check the description provided in 7.1.3 by repeating step 2.4; From 7.1.3 if not correct return to the beginning of phase 3 From 7.1.4; Description 7.2.4 Check the correctness of the descriptions provided in step of how each 7.1.4 by asking SMEs the following question (e.g. circulate the improvement draft description for feedback and/or provide a structured is to achieve walkthrough of the actual practices and relationships in the each Validate the model): Does the description of the actual practices and objective ‘correctness’ of the relationships in the model provide all the necessary details to be described in draft descriptions of List of issues that sufficiently accurate to evaluate the means by which each 1.4; the simulation model need to be improvement is to achieve each objective. if not correct return to Consult to be developed. If any resolved by the beginning of step 6.1 SMEs on7.2 issues arise then returning to a each revision and previous phase description adjustments are or step (only if 7.2.5a Check the correctness of the descriptions provided in step required by returning necessary) 7.1.4 by examining each of the connections between model to a previous phase or components, to determine whether the conceptual model From 7.1.4 step. reproduces the actual practices and relationships? If not correct then return to step 6.2 7.2.5b If necessary, to support the analysis in 7.2.4: Represent the components and relationships using a graphical means of See above representation (e.g. process flow diagram, logic flow diagram, activity cycle diagram). 7.2.6 Check the draft description of each of the assumptions and simplification that have been incorporated into the model From 7.1.4; components and interconnections described in 7.1.4 with the Consult client for the level of confidence that can be placed in them and SMEs their likely impact on the accuracy of the model; If not correct then return to step 6.2.3 A final non- software specific From 7.1 and description of7.3 Document the final non-specific description of the simulation model to be developed 7.2 the simulation model to be developedPoint of exit:To Phase 1, phase 2, phase 3, step 6.1, step 6.2: Re-enter a previous phase or step if any issues arise that need to be resolvedTerminate conceptual modelling stage of a simulation project: Exit when the final description of the simulation model to be developedhas been fully validated.Output: A non-software specific description of the simulation model to be developedThere were fourteen observations made when refining and implementing the key concept intophase 7, which are shown in table A.7 in appendix A. These observations were incorporated intothe phase shown in detail in table 7.17. As previously noted this step has incorporated existing 174
    • simulation practice aligned to the outputs from the necessary phases and steps in themethodology. The main observations were that a draft description could be obtained from thevarious different phases and steps in the methodology but these needs to be validated in full, incase any new insights or learning’s have changed the nature of the conceptual model to becreated. The checks incorporated were to repeat some checks relating to defining the supplyproblem, objectives and improvement. If the description is no longer correct then this means theparticipants must return to the beginning of the associate phase and proceed through themethodology. In addition three outstanding descriptions have yet to be validated: Actual practices and their relationships between them provide all the necessary details to be sufficiently accurate to evaluate the means by which each improvement is to achieve each objective (addressed in step 7.2.4) Model components and their connections between them reproduce the actual practices and their relationships (addressed in step 7.2.5a and 7.2.5b) The client and the modeller has a level of confidence in the assumptions and simplifications that have been incorporated into the model components and interconnections and their likely impact on the accuracy of the model (addressed in step 7.2.6)The final validation procedure makes a useful distinction between validating the actual practicesand their relationships (a domain-orientated model) and how they are represented by the modelcomponents and connections (a design-orientated model). The domain-orientated model hadbeen previously checked when the model boundary was formulated to ensure that there weresufficient and correct links between business processes in the model. However, in the design ofthe conceptual model the processes and inputs identified were described by the actual practicesto be implemented at the required level of detail. These descriptions had not been checked toensure that they were significantly accurate from the perspective of the client (credibilityrequirement). Therefore, a step is added to check that the descriptions of the actual practiceswith SMEs (e.g. circulate the description for feedback or provide a structured walkthrough of themodel) contain all the necessary details to evaluate the means by which each improvement is toachieve each objective (information provided from step 1.4).Two checks are included in the methodology to check the validity of the model components andconnections (design-orientated model) and the assumptions and simplifications that have beenincorporated into them. A previous discussion considered the need for a ‘hypothesis test’, todetermine whether the connections between the model components reproduce the actual 175
    • practices and relationships (C.f. section 4.4). This check was broken down into two sub-stepsbecause it was observed that it is difficult to validate the descriptions presented in a componentlist. It would be easier to show the model components and interconnections if they were shownusing a graphical means of representation (e.g. process flow diagram, logic flow diagram, activitycycle diagram). This is helpful because they graphically show the connections between theprocesses and activities and the rest of the model and entities are shown to flow throughprocesses and activities. A further check is included for the assumptions and simplifications using(Robinson, 2004b, pg. 215) guidance, the ‘assumptions and simplifications can be checkedbetween the modeller and the client for the level of confidence that can be placed in them andtheir likely impact on the accuracy of the model’.Appendix D shows the five actual practices for the BeerCo case, the way in which they wererepresented by the components in the model including how assumptions and simplifications havebeen incorporated into the model. These are further represented in a process flow diagrampresented in appendix E. Taking the practice that the retailer ‘places an order’ it can be seen thatthis is implemented by five activities that ensure that the order size is not exceeded. Therefore,the necessary details are that an order is placed per decision period and checked that an order iswithin the max order size, (i.e. If no, the order is rejected, if yes, the order is accepted). Acceptedorders are sent to the wholesaler ‘receive order’ process. The graphical representation inappendix E is useful as it shows that if an order is rejected then the human player is informed tore-enter the order, and, if accepted, it needs to be sent to the wholesaler ‘receive order’ process.The simplifications and assumptions detail that the orders are placed in each weekly decisionperiod and that orders must be within the maximum order size.When all the validation checks are complete, there are no further issues to resolve by returning toprevious phases or steps indicating that the model is valid and credible. It is valid because theaccuracy of the model has been checked from the perspective of the modeller, most importantlythat the model components and connections reproduce the actual practices and relationships. Itis credible because the actual practices and relationships have been checked to ensure all thenecessary details are included to address how each improvement is to meet each objective.There is also a greater level of confidence (from both the client and modeller perspective) in thesimplifications and assumptions incorporated into the model in relation to their likely impactupon model accuracy. At this point, the final description of the simulation model to be developedcan be documented. 176
    • 7.4 Implementing the SCM2 using a spreadsheet applicationThe methodology can be implemented using a spreadsheet application (e.g. Microsoft excelworkbooks), which provide a number of advantages. Each of the outputs from the phases can bereported in workbooks, within one spreadsheet. Both the detailed design cases wereimplemented using Microsoft Excel workbooks along with any associated documentation (e.g. listof SCOR process elements, inputs and sources).There were three key advantages for using a spreadsheet application: its functionality, the way inwhich data can be presented and the centrality of keeping all outputs in one document. Initialdesigns of the output from each phase were presented in word-processing tables (e.g. MicrosoftWord). This proved cumbersome for the reason that the level of detail and number of entriesmade using the word-processing tables were inappropriate. It was also difficult to re-use datathat was required for a future phase.The functionality of a spreadsheet application enabled filters to be used, to sort data based upona set criteria (e.g. all promoted process elements at the factory, all process elements to betested). These features were used, to filter the necessary data, which would be inputted to asubsequent stage. In particular during the designing of actual practice, filters could be used toshow common practices which could be aggregated. This was seen most notably in the CarCoproblem, where a kanban control system was used, for both the production and deliveriesbetween the tier three suppliers, third-party logistics provider and the seat supplier. Conditionalformatting was used to indicate the status of a particular data entry to distinguish it from another.This was useful as it increased the readability of the document by displaying data clearly usingdifferent colours for filled cells (e.g. promoted process elements indicated as green, red forexcluded). The functionality in a spreadsheet application also presents an opportunity toautomate some steps and reduce the time-consuming activities (this is considered in more detailin section 8.6).7.5 Alignment of detailed design of the SCM2 against specificationThe detail design can be aligned against the specification to ensure it meets the requirements.The requirements were identified in chapter five, when the specification for the methodology wasformed. These included the requirements for an effective conceptual model, a goodmethodology and for conceptual modelling of supply chain problems. Each of these requirementsis considered in turn, reviewing the content of the methodology to demonstrate that thespecification has been met. 177
    • 7.5.1 Meet the requirements for an ‘effective’ conceptual modelThe SCM2 meets the requirements for an effective conceptual model. It has been developed on afundamental principle that the model should be kept as simple as possible and that it shouldcreate a valid and credible conceptual model. Table 7.18 shows how the requirements for aneffective conceptual model have been realised in the SCM2. 2Table 7.18 Aligning the SCM to meet the requirements for an ‘effective’ model Requirement Realised in the SCM2 The ‘core’ processes that represent the improvements and provide data values for objectives (in phase 2 and 3) are identified. The model boundary is initiated from a list of ‘core’ processes (in phase 4) The model boundary identifies the critical interconnections between the ‘core’ processes and those in the supply setting (those processes that have been included in the model by being ‘promoted’) (in phase 5) The boundary of the model included the inputs that have been simplified; they have no further inputs that could be considered.Keep the model as The level of detail required in the description of the model components is determined from the actual practice described (in phase 6). The actual practices have been described with guidance using SCORsimple as possible which suggests a level of process decomposition. Methods of simplification are included in the process and not just as a final stage (e.g. excluding components and details, in step 5.2.4; replacing components with random variables or fixed inputs, in step 5.2.1) Assumptions and simplifications are incorporated into the description of model components and their connections (in step 6.2) Assumptions and simplifications are validated in the final phase (step 7.2) with the client and modeller to ensure that there is a level of confidence and that they do significantly impact upon model accuracy The problem is formulated precisely in phase 1 and the objective and improvement is detailed (in phase 2 and 3). Two checks are incorporated into phase 1 to ensure that the description of the supply problem is ‘correct’ The participants in the process of conceptual modelling are included to ensure that there is interaction on a regular basis (in steps 1.1 – 1.5; 2.4; 3.3; 5.2; 6.1; 7.2) SMEs are consulted (in steps 5.2; 6.1; 7.2)Build valid and A step in the final validation asks SMEs whether the descriptions of the actual practices and relationshipscredible models in the model provide all the necessary details to be sufficiently accurate to evaluate the means by which each improvement is to achieve each objective (in step 7.2.4) A step in the final validation examines the connections between model components, to determine whether the conceptual model reproduces the actual practices and relationships (in step 7.2.5) A step in the final validation checks the assumptions and simplifications that have been incorporated into the model components and interconnections with the client for the level of confidence that can be placed in them and their likely impact on the accuracy of the model (in step 7.2.6)Phases two and three describe the improvement and objectives so that a set of ‘core’ processesthat need to be included in the model are identified. This is in effect the foundations, the buildingblocks to identify the simplest model. Each of the interconnections to ‘core’ processes isconsidered in turn to identify candidates for inclusion. At the heart of the methodology is thedecision of how to treat candidates by either including, excluding or testing them (using aprototyping method). This aids in identifying the model boundary using two rules, one thatprovides guidance on how to include a process and a second that considers whether it could besimplified (i.e. fixed input or sample distribution). Additionally the methodology embeds methodsof simplifications at the various points in which they can be implemented in a procedure forconceptual modelling (e.g. excluding components and details in step 5.2.4) and not just at theend. 178
    • The methodology addresses Law’s (2007) issues relating to building a valid and credible model atthe early stages of a simulation project. This entails formulating the problem precisely,interacting with the decision-makers on a regular basis throughout the process of conceptualmodelling and to consult appropriate SMEs. The supply problem is described in detail using aprescribed format and implements two checks before detailing the improvement and objective inmore detail. The client (or decision-makers) and SMEs are involved throughout the wholeconceptual modelling process but the methodology identifies the specific points in which theyneed to be consulted. A significant advantage of the methodology is that it uses the SCOR modelto provide domain-knowledge to enable a more focused and efficient process, in particular wheninformation is required from the client and SMEs.The methodology also includes checks for the key components that make up the description ofthe conceptual model: supply problem, objectives and improvements. A method is also designedto check that the processes and inputs included in the model boundary have ‘correct’ and‘sufficient’ linkages between ‘core’ processes and those processes and inputs included from thesupply setting. A final validation procedure re-checks that the supply problem, objectives andimprovements are ‘correct’. The procedure recognises that the purpose of the model may changedue to a greater understanding of the problem being studied. In addition, check the descriptionsof the actual practice and relationships in the model provide all the necessary details to besufficiently accurate with the client and SMEs (credibility); examination of the connectionsbetween model components to determine that they replicate actual practice and theirrelationships (hypothesis validity); and that both the modeller and the client have confidence inthe assumptions and simplifications incorporated into model component descriptions for acertain level of model accuracy (validity and credibility).7.5.2 Meet the requirements of ‘good’ methodologiesThe SCM2 meets the requirements of the desirable characteristics of a good methodology in thecontext of conceptual modelling for SCM applications. This includes a procedure (or guide),participation and points of entry. Table 7.19 shows how each of these requirements have beenrealised in the SCM2. 179
    • Table 7.19 Meet the requirements of ‘good’ methodologies Requirement Realised in the SCM2 Well defined stages to gather and analyse information from the client (in steps 1.1 – 1.5; 2.4; 3.3; 5.2; 6.1; 7.2), SMEs (in steps 5.2; 6.1; 7.2) and the SCOR model when necessary (in steps 1.1; 1.2; 1.4; 2.1 – 2.3; 3.1; 4.1; 5.2; 6.1) Suggests a format and terminology for describing the supply problem (in phase 1); describing the objective (in phase 2); describing the improvement (in phase 3); describing the actual practices and relationships (in step 6.1); describing the components in the model and their connections (in step 6.2) Represent the conceptual model in a component list (in step 6.1; 6.2 and 7.2); Provide a graphically represent the conceptual model using a graphical means (in step 7.2) Provide a method to identify the connections between processes and extract procedure information from SCOR (in phase 4) Provide a method to formulate the model boundary (in phase 5)Meet the Use an hypothesis validity test to examine the model components and connections (in phase 7.2)requirements of good Suggest consulting the client and SMEs in the validation process (e.g. circulate themethodologies description of the conceptual model, structured walkthrough) in step 7.2 Provide a written record to justify any decisions made when formulating the model boundary (in step 5.2) and simplifications and assumptions incorporated into the model components (in step 6.2) Participation in Participation between the modeller, the client and SMEs (see building a valid and each step credible conceptual model) Point of entry to the conceptual modelling process clearly defined in phase 1 Points of entry embedded at the beginning of each phase, with the requirements Embed points that should have been met from a preceding phase/step Iteration between steps is noted, particularly when formulating the model of entry boundary in phase 5 and when completing each of the validation checks in phase 7. Checks state that if the rule has not been met the point of return to a previous step (in steps 1.5; 2.4; 3.3; 5.4; 7.2).The procedure laid down in each of the phases has well defined stages for gathering and analysinginformation from four key sources: the client, SMEs, SCOR guide and from a previous step in themethodology. SCOR is used not to replace interactions and consultations with the client andSMEs but to improve the process. The methodology includes a number of established principles,methods of simplification and means to represent the content of a conceptual model. Moreoverit offers specific guidance for tackling some of the most demanding and difficult aspects ofconceptual modelling such as formulating the problem correctly, gathering information fromclients, SMEs and SCOR, identifying the connections between the components in the model,determining the model boundary and validating a conceptual model. These procedures are noveland address specifically the needs of evaluating supply chain problems.The involvement of participants is included in the description of the procedure, specifically howinformation is obtained from them. The modeller is responsible for following the steps laid downin the methodology and interacting with the participants as suggested in the description of eachstep. The methodology does not consider (as suggested in Platts, 1994) how individuals in thegroup achieve enthusiasm but some of the steps have been designed to provide clarity andcommitment between the modeller and the client (e.g. check the description of the supplyproblem). There are also decisions that need to be reached, some present no difficulty at all (e.g. 180
    • identifying candidate processes) if using SCOR while one of the most challenging tasks ofconceptual modelling, identifying the model boundary is facilitated by decision rules, using SCOR,interacting with SMEs when necessary and documenting the justification for the decision made.The point of entry to the methodology is clearly defined in phase one and for each of the phases.There are also places in the methodology that may require iteration to a previous step. Inparticular when processes are promoted in the model boundary formulation this initiates theneed to return to phase four. Similarly the checks are in place to ensure the ‘correctness’ of thedescriptions used to create a conceptual model. If the requirements in the checks are notsatisfied then the points of entry in a previous step are stated.7.5.3 Meet the requirements for conceptual modelling of supply chain problemsThe SCM2 meets the requirements for conceptual modelling of supply chain problems. Thisincludes addressing the range of supply chain improvements, for a given objective within thesetting of the supply problem. Table 7.20 shows how the requirements for conceptual modellingof supply chain problems have been realised in the SCM2.Table 7.20 Meet the requirements for CM of supply chain problems Requirement Realised in the SCM2 Alternative supply chain improvements are described in terms of how and which actors are affected by change (in step 1.2). Information is obtained from the clientHandle supply chain and/or using SCOR best practice guide.improvements The detail of how each improvement is to be represented is described in phase 3. Information is obtained from SCOR to guide the description, particularly to obtain the processes that implement a particular business practice. The objectives of study are described in terms of the impact on supply chain performance attributes that the improved supply system should achieve. InformationAddress a range of supply chain is obtained from the client using SCOR performance attributes. The detail of how each objective will be measured is determined in phase 2. Theobjectives objectives are described using the SCOR hierarchy of supply chain performance measures at three different levels and its associated detail (e.g. calculation, data source) The nature of the supply setting is described (e.g. actors in the supply system that have a cause and effect relationship between the improvement and objective, and any important situational information (that may influence the scope of the problem)Identify interconnections within in step 1.3. The interconnections between the inputs of included processes and their sources, arethe supply setting determined in phase 4, those that have yet to be considered are treated as ‘candidates’ for possible inclusion. ‘Candidate’ process elements are considered for inclusion in phase 5 based upon two decision rules.The methodology uses information provided from SCOR which is ‘designed as a tool to describe,measure and evaluate any supply-chain configuration’ (Lockamy and McCormack, 2004, pg. 1194)and it can be used in simulation applications (Persson and Araldi, 2009). The benefits of usingSCOR have already been described (C.f. section 6.4), but can be summarised as: 181
    • SCOR describes plan, source, make, deliver and return processes at three levels of process decomposition using standardised terminology (Meyr et al., 2002) and process orientated language SCOR includes an extensive database of supply chain practices and metrics (Persson and Araldi, 2009) and describes the inputs, outputs and the basic flow of process elements at the third level of decompositionThe first phase describes a supply problem using SCOR terminology and details the improvementand objective in phases two and three. The steps clearly recognise that SCOR should only be usedas a guide. Situational information is also required from the client. The benefit of using SCOR inthis context is that it suggests the processes that need to be modelled for a given practice andmetric. The inputs and source processes are considered to formulate the model boundary usingthe relationships described in SCOR between business processes.7.6 Chapter summaryThe chapter has presented an overview of the phases and steps to follow as laid down in theSCM2. Two application cases were presented to detail and refine the SCM2 with rationale for theirselection. Each of the phases was considered in turn so that the key concepts described inchapter six could be implemented. A range of principles and observations were considered andincorporated into the design. Each of the steps described in the methodology were illustratedusing the development cases, justifying any of the decisions made.The final part of the chapter demonstrated that the detailed and refined design of the SCM 2meets the required specification presented in chapter five. This includes: Requirements for an effective conceptual model – The methodology aims to keep the model as simple as possible, initiating the process with the ‘core’ processes and formulating the model boundary by considering the interconnections between ‘core’ processes and the supply setting. The methodology also incorporates checks within the phases to establish the ‘correctness’ of the descriptions provided and a final validation procedure to redo key checks in the process and at the end review the accuracy of the conceptual model from both the client’s (to show that the conceptual model is credible) and modeller’s (to show that the conceptual model is valid) perspectives. Requirements for a good methodology – The methodology provides detailed steps for gathering and analysing information from the client, SMEs and the domain- 182
    • knowledge extracted from SCOR. Methods are suggested for structuring a supply problem, identifying the interconnections between processes, formulating the model boundary, describing how the components in a model represent each actual practice and for validating the conceptual model. Points of entry are embedded into the phases of methodology, to ensure steps are completed successfully and to facilitate iteration. Requirements for conceptual modelling of supply chain problems – A supply chain problem is described in terms of its objectives and improvements selected within a supply setting. SCOR is used as an aid to describe improvements and metrics and identify the interconnections between processes that should be included in the model.The following chapter applies the detailed and refined SCM2 to a preliminary validation case todemonstrate that the methodology is initially feasible and has utility. 183
    • Chapter 8 Preliminary validation of the SCM2 (Stage V)The chapter implements stage V of the research methodological programme and addresses thethird and final research objective. The primary aim is to preliminarily validate the methodology byapplying it to a validation case to demonstrate that it is both initially feasible and has utility. Thisbuilds upon the previous chapter that has refined the methodology and aligned it so that it meetsthe specification of requirements. The chapter is structured to fulfill the following five aims: 1. Presentation of the validation case (discussed in section 8.1) - The CoffeePotCo case is described and justified 2. Apply the methodology to the validation case (discussed in section 8.2) - Walkthrough each of the steps as prescribed in the methodology in full up to the point that the actual practices to be represented in the computer model are described 3. Purpose of the evaluation the SCM2 (discussed in section 8.3) – The purpose and the sub-criteria for the feasibility and utility is described 4. Evaluation of the initial feasibility of the SCM2 (discussed in section 8.4) - The methodology is evaluated to demonstrate that it is initially feasible using the sub-criteria identified in section 8.3 5. Evaluation of the initial utility of the SCM2 (discussed in section 8.5) – as above but in relation to the utility sub-criteria 6. Discuss areas for further testing (discussed in section 8.6) – Issues for further refinement and testing are identified. The discussion centres upon the need for further applications with different facilitators and participants 7. Identify opportunities to improve the methodology (discussed in section 8.7) – Discusses three key opportunities to automate parts of the process, strengthen the use of domain knowledge in the process and develop a web-based tool.8.1 Presentation of validation case: CoffeePotCOThe validation case is of a multi-national company (CoffeePotCo) that is addressing a difficultdecision, regarding where and how to cost effectively manufacture products in a global andcomplex supply setting. The validation case is described in more detail in terms of itsimprovement, objectives and supply setting, as the methodology is applied in section 8.2.The advantage of using this case is that the computer model has been described and the findingsfrom the study published in Taylor et al., (2008). The researcher was involved in the study over atwo year period with three co-authors. It makes use of a scenario-based simulation approach thatutilises actual product data and information from the published literature. A discrete-event 184
    • simulation programme named ‘SIMNET II’ (See Taha, 1992 for details of the programme) was usedto evaluate the supply problem.The validation case is representative and typical of a supply chain problem that has beenevaluated using a simulation approach. Taylor et al., (2008) highlight the significance andimplications to the practicing manager of the case by stating that the operational and strategicimplications of global sourcing have not been well researched (Hong and Holweg, 2005) except inLowson (2002). In the case of Lowson (2002) a quantitative approach was proposed to determinesourcing costs; the study did not focus upon inventory needs for a desired service level. A reviewby Goetschalckx, Vidal and Dogan, (2002) supports this finding when outlining the characteristicsof published strategic logistics models and a review of global supply chain design by Meixell andGargeya (2005), shows that none of the previous literature in the area considers the stochasticcustomer service level.The product used in the study was a coffee maker, which is ‘functional’ in nature. There has beena great deal of product and process related information published in support of this productselection in Ulrich and Pearson (1998). For the purpose of this evaluation two scenarios wereselected that examined an efficient manufacturing scenario in a low-cost area with eithershipments made in (1) small (by air), or (2) large quantities (by road and ship). The experimentalsituation is shown in figure 8.1.Figure 8.1 Graphical illustration of CoffeePotCo supply problem8.2 Application of SCM2 to preliminary validation caseThe SCM2 is applied in this section by following the prescribed procedure as laid down in themethodology (presented and detailed in chapter seven). This was facilitated by the researcher toensure consistency in its application. The aim is to show that the methodology can be followed 185
    • up to the point of describing the actual practices to be represented in the computer model andprovide the necessary output. It was previously argued in the research programme chapter that itis at this point that the methodology is novel and could be compared against a validatedcomputer model.The output from each phase is documented in each section corresponding to applying each phaseof the methodology. The client role has been played by one of the expert modellers involved inthe design and the building of the computer model. This was particularly valuable whencompleting the validation checks in the methodology.8.2.1 Phase one: Describe the supply problemThe supply problem was described by following the four steps in phase one and the final stepchecked the description for ‘correctness’. The objective of study, description of improvements tochange the existing supply system, description of the problem setting and statement of how eachimprovement could achieve the desired impact of the objective(s) for the CoffeePotCo validationcase is summarised in table 8.1.Table 8.1 Statement of the supply problem (CoffeePotCo) Statement of supply chain problem A multi-national manufacturing company (MNC) is deciding how to cost effectively manufacture Output Statement of products in a global setting. The aim is to determine how much finished goods stock (reduce from objective(s) of study supply chain assets) to have on hand to support sales at a 95% desired service level (increase 1.1 supply chain reliability). A MNC has an efficient manufacturing facility in a low-income/low-cost location (i.e. Asia or Description of Africa) and a warehouse in a high-cost area where the product is primarily distributed and sold. Output improvements to Shipments are currently made in large quantities, with a cost effective and slow shipping from change the existing method (road and sea). The MNC wants to consider the impact of a change in the shipping 1.2 system method, by shipping in small quantities, with an expensive and fast shipping method (all air) on the defined objective. The MNC has an efficient manufacturing facility in a low-income/low-cost location (i.e. Asia or Africa) and a warehouse in a high-income/high-cost location where the product is primarily distributed and sold (i.e. North America or Western Europe). The capacity for an efficient Output Description of the production facility is defined as one which has a low cost of capital and shipping in economic from problem setting quantities. The method for shipments from a low-cost manufacturing location to a warehouse 1.3 in a high-cost area can be larger, cost effective and slow, or small, expensive and fast. It is assumed that the product selected is a coffee maker (Mr Coffee Expert Model) which is representative of a functional product type. An off-shore location offers advantages to reduce cost (e.g. Alguire, Frear and Metcalf, 1994; Fagan, 1991; Monczka and Trent, 1991). The study seeks to examine how much finished goods inventory at hand is needed in the warehouse to satisfy a defined customer service requirement Statement of how at lowest cost. The shipment size, speed and cost will affect how much finished stock is each improvement Output available at hand. If there is insufficient inventory stock-out will occur leading to the service could achieve the from level not being met, while too much inventory will satisfy the requirement but will incur a cost. desired impact on the 1.4 objective(s) Simulation is a good method to evaluate this problem as it will allow a user to adjust the re- order point to obtain a desired service level and review the implications on finished goods inventory costs.The objective of study can be described using the supply chain performance attributes (step 1.1).The aim of the study is to determine how much finished goods stock to have on hand to support 186
    • sales at a 95% desired service level. This can be translated into SCOR performance attributes as a‘reduction’ in ‘supply chain assets’ and ‘increase’ in ‘supply chain reliability’.The improvement can be selected and described to achieve the objective of study (step 1.2). Thisincludes evaluating the most cost effective way to ship a product from a low-income/low-costlocation (i.e. Asia or Africa) to a warehouse in a high-income/high-cost location (i.e. WesternEurope or USA). The existing approach is to ship in large quantities, with a cost effective and slowshipping method (road and sea). The alternative is to consider shipping in small quantities, withan expensive and fast shipping method (all air).The setting of the supply problem can be described in terms of the actors in the supply system tobe evaluated and product information (step 1.3). The scope of the evaluation is confined to oneproduct (Mr Coffee Expert Model as detailed in Ulrich and Pearson, 1998) which is representativeof a functional product type. The actors include the MNC that has an efficient manufacturingfacility in a low-income/low-cost location (i.e. Asia or Africa) and a warehouse in a high-income/high-cost location where the product is primarily distributed and sold (i.e. North Americaor Western Europe).The means by which the improvement is to achieve the objective by bringing about change in thedesired supply system can be described (step 1.4), using published sources and confirmation withthe SME. It is clear from the literature that an off-shore location offers advantages to reduce cost(e.g. Alguire et al., 1994; Fagan, 1991; Monczka and Trent, 1991). However, this question forcesthe participants to consider how the shipment method (size, speed and cost) will have an impacton any potential stock-outs or over-supply of finished goods. For instance, if there is insufficientinventory stock-out will occur leading to the service level not being met. Alternatively, too muchinventory will satisfy the requirements but will incur higher costs. Simulation would be a goodmethod as it would allow a user to adjust the re-order point to obtain a desired service level andrecord the implications on finished goods inventory costs. The output from phases steps 1.1 – 1.4was deemed correct after consultation with the client (step 1.5).8.2.2 Phase two: Determine how each objective is to be measuredThe objective is described in terms of how it will be measured by following three steps and a finalvalidation check. A description of the supply chain performance measures for each objective,how each performance metric will be calculated from the data sources that are used to drive thecalculation and nature of the measurement is summarised in table 8.2. 187
    • Table 8.2 Statement of each objective to be measured (CoffeePotCo) Statement of how each objective will be measured Perf. Process M’mentPerf. Att. metric Perf. metric Definition of metric calculation Actor elements span level Output from 2.1 Output from 2.2 Output from 2.3 Level 3 AM3.16: Inventory S1.4 WH ProcessAssets (Value of finished goods inventory/(COGS/365)) metric days of supply D1.8 WH Process The percentage of orders that are fulfilled on the D1.3 WH Process RL.2.2: Delivery customers originally scheduled or committed date D1.12 WH ProcessDelivery Level 2 performance to = [Total number of orders delivered on the originalreliability metric customer commit commitment date] / [Total number of orders D1.13 WH Process date delivered] X 100%The supply chain performance metric is described using the hierarchy of metrics presented bySCOR at three different levels. No level 1 (strategic) metrics were deemed appropriate forevaluating the objective leading to a review of level 2 and 3 metrics for each performanceattribute (steps 2.1.2 – 2.1.3). The metric that best describes reducing supply chain assets byminimising finished goods inventory is the ‘inventory days of supply’ (AM3.16) metric. Likewisethe ‘delivery performance to customer commit date’ metric was selected to improve the deliveryreliability performance attribute. In the case of the latter, some alternatives were available (e.g.from the customer or suppliers perspective, documentation accuracy); the customer commit datewas selected because it is the warehouse that wants to track the delivery performance to thecustomer to a specified date.Once the metrics had been identified it was relatively straight-forward to extract from the SCORdescriptions of performance metrics, how each metric is to be calculated from the data sourcesthat are used to drive the calculation. There was a difficulty obtaining the data sources fromSCOR to drive the ‘inventory days of supply’ metric. The SCOR documentation (SCC v.9, section2.5.2) adequately defined how the metric should be calculated but suggested the data sources arenot specified as they can be obtained by importing data from an existing business system (e.g.ledger account). This may suggest that the system may need to be included. However, by lookingat the definition the information can be obtained by calculating the value of finished goods stockfrom inventory availability, multiplied by a holding cost. This information can be obtained fromS1.4 and D1.8 where ‘source’ and ‘deliver’ inventory availability information is calculated. SCORprovides good information on the data sources for the ‘delivery performance to customer commitdate’ metric.The third step of phase two (step 2.3) uses information provided by 2.1 to specify the actors (step2.3.1) and the measurement span for each performance metric (step 2.3.2). This was deemedimportant in the refinement of the methodology otherwise it would be difficult to perform the 188
    • proceeding analysis. In light of the supply problem, the delivery performance and inventory daysof supply is measured at the warehouse location. The descriptions provided from phase two werechecked for ‘correctness’ with the client before proceeding to phase three.8.2.3 Phase three: Determine how each improvement is to be representedThe improvement is described in terms of how the processes will represent it by following twosteps and a final step to check the description for ‘correctness’. A description of the level ofprocess detail for each improvement specified by actor is shown in table 8.3.Table 8.3 Statement of how each process represents each improvement (CoffeePotCo) Statement of how each process is to represent each improvement Level of Business Improvement option process Actor process detail Output Output from step 1.1.2 Output from step 3.1 from 3.2 Level 3 D2.10 Fact.A MNC has an efficient manufacturing facility in a low-income/low-cost location (i.e. Asia orAfrica) and a warehouse in a high-cost area where the product is primarily distributed andsold. Shipments are currently made in large quantities, with a cost effective and slow shipping Level 3 D2.11 Fact.method (road and sea). The MNC wants to consider the impact of a change in shippingmethod by shipping in small quantities, with an expensive and fast shipping method (all air) onthe defined objective. Level 3 D2.12 Fact. Level 3 D2.13 Fact.Unlike the development cases there were no specific practices suggested by SCOR to represent achange in shipping method (e.g. small or large). This highlights a weakness in SCOR, however thestep was described in enough detail to use SCOR as a guide to define which processes would beneeded to represent the improvement. Firstly, the improvement is related to the ‘deliver’ process(step 3.1.1) and secondly, to a ‘make-to-order’ environment (step 3.1.2). This means that thefocus of the evaluation is in the ‘D2: Deliver make-to-order product’ business process.There are many transportation related practices in D2; one of these is ‘cross-docking’ that couldbe reviewed for information. This is a warehousing strategy that involves the movement ofmaterial directly from the receiving dock to the shipping down with the minimum in-storage time(Apte and Viswanathan, 2000). Effectively the factory ships packaged finished goods via either aseaport, or an airport. This docking area could be treated as a trans-shipment (order is receivedalready packaged for delivery to the customer, SCOR V.9, 2008). Using the information providedby SCOR for this practice and verifying any decisions with the client led to the D2.10 – D2.13process elements being identified (step 3.1.3). The rationale for this is that delivery times andcosts which include packing (D2.10), loading (D2.11), shipping to warehouse (D2.12) and receivingthe product at the warehouse (D2.13) will be influenced by shipment size (large or small) and 189
    • method (sea and road or by air). The practices are implemented at the factory location (via adocking location) (step 3.2).The descriptions provided from phase three were checked for ‘correctness’ with the SME beforeproceeding to phase two. The methodology forced both the facilitator and the SME to clearly andconcisely consider the impact of the improvement on different processes before reachingagreement between both parties. There were a number of changes to the original description; inparticular there was a debate about how the processes identified would be influenced by theimprovement.8.2.4 Phase four: Determine the model inputs and source process elementsThe model inputs and candidate process elements are determined by following two steps usingthe domain knowledge provided by SCOR. Figure 8.2 shows that each core process elementdefined in phase two and three were reviewed in turn by extracting, from the SCORdocumentation the input fed by the process element included in the model (step 4.1.2) and itssource (step 4.1.3). The step was detailed enough to emphasise the need to make explicit theactor that implements each included process element and the actor which generates the inputfrom a source process element.Figure 8.2 Interconnection identified for each process element in the modelThe source process elements were discriminated against those process elements that currentlyexist in the model. For example, the core processes D2.11, D2.12, D2.13 have workflows that arealready included (i.e. D2.10 is core as well as D2.12 and D2.13 having workflow connections) so 190
    • they no longer need to be considered for inclusion. Each of the process elements that have notbeen considered are effectively ‘candidates’ for possible inclusion in phase five. Phase four isrepeated for each promoted improvement that had been determined in phase five until nopromoted process elements are left to consider. In this case 207 entries were made in theanalysis resulting in a host of candidate process elements being considered for the factory,warehouse and customer.8.2.5 Phase five: Formulate the model boundaryThe model boundary is formulated by following three steps and a final check to be satisfied thatthere are sufficient and correct links between processes and inputs included in the model. Figure8.3 shows an illustration of the decisions made to include (simplify or promote), test or exclude aninput from a candidate process element by applying the two rules. The figure also shows that theanalysis and documentation (in particular a justification for each decision) was aided by using aspreadsheet application.Figure 8.3 Formulation of the model boundary (CoffeePotCo)Applying the steps outlined in phase five takes seven rounds of iterations between phase five andfour, until no process elements and their source inputs are left to consider. The steps wereinitiated by considering the source inputs from the core process elements that have not alreadybeen included in the model. At each decision point the two rules were applied and justificationwas recorded.The decisions made about how to treat each candidate process element and input resulted inmany being simplified, promoted and excluded but none were deemed necessary to test. Table 191
    • 8.4 shows those process elements that were included within the boundary of the model. At thispoint it must be noted that some of the process elements and associated inputs were treated as‘phantoms’ in phase six, as they are only included because they provide a necessary workflow, butthe practice itself adds no significant value (i.e. the link is critical but the activity is not).Table 8.4 Promoted process elements and simplified inputs (CoffeePotCo) Inputs and source process element that were promoted Simplified inputs (Customer) Customer ReplenishCustomer N/A Signal; (Customer) Customer Order Receipt Verification (S1.2, S1.3); Scheduled Receipts (S1.1); Delivery Plan (P4); Product Inventory Location, Sourcing Plans (P2); Validated Order (D1.2); Scheduled Receipts (S1.1); Planning Finished Goods Inventory Data (EP.3); Order Backlog (D1.11); Load Information (D1.5); Finish GoodsWarehouse Location; Service Levels; Product Inventory Target Levels (ED.4); Product On Order (S1.1); Packed product (D1.10); Inventory Target Levels; Order Daily Shipment Volume (D1.4); Picked product (D1.9); Shipment Routes (D1.6, Rules D1.7) Shipping Parameters and Picked product (D2.9); Production Schedule (M2.1); Finished Product Release Documentation (ED.6); Inventory Factory (M2.6); Information Feedback (M2.1 – 6); Order Signal (D2.3); Delivery Plans (P4); Availability (M2.2); DC/Vendor Load Information (D2.5); Booked Order (D2.2); Consolidated Orders (D2.4 Transit Time (N/A)There were a few process elements for the factory, warehouse and customer that could besimplified (step 5.2.1). At the customer location this included the demand which is generated bya simple distribution. The warehouse includes the inventory locations, target levels, service levelsand order rules which are fixed. In the case of the factory simplified inputs include the shippingparameters; inventory is assumed to be infinite to satisfy production and the shipping time isfixed.The customer had no promoted process elements but many were added for the warehouse andfactory. The warehouse included demand and order information, planning of how much productto deliver the customer and order from the factory, managing inventory, shipment quantities androutes and necessary product flows. The factory included order information, planning andmaterial flows similar to the warehouse but also included processes for manufacturing. Theprocess elements that generate these sources were promoted as the inputs they generate willaffect model behaviour by significantly impacting on the objectives of study and cannot besimplified. Table 8.5 demonstrates the number of iterations between phase five and six when acandidate process element had been promoted in step 5.2.2. 192
    • Table 8.5 Candidate process elements promoted in each round (CoffeePotCo)Number of iterations between phase Candidates process elements promoted in step 5.2.2 five and six Factory Warehouse CustomerInitiated with core process elements S1.4, D1.8, D1.3, D1.12, D2.10, D2.11, D2.12, D2.13 N/A D1.13 Iterative round 1 S1.2, S1.3, S1.1, P4, P2, D2.9 D1.2, D1.11 None Iterative round 2 M2.1, M2.6, D2.7, D2.8 D1.5, ED.4 identified Iterative round 3 P3, M2.3, M2.4, M2.5, D2.6 D1.4, D1.10 Iterative round 4 D2.3, P4 D1.9 (S1.1 – Iterative round 5 D2.3, D2.5, D2.4 D1.7 simplified) Iterative round 6 D2.2 D1.6A vast amount of entries were excluded from the model as they were deemed to not have animpact on the model behaviour (step 5.2.4). For instance, the warehouse does not manufacturerany products and no returns are assumed. Similarly, it is not necessary to include the sourcingprocess at the factory as the improvement specifically looks at the impact of the shipping methodon the warehouse delivery performance to the customer. Another example includes some of theplanning processes, in particular there is no supply chain planning taking place between thecustomer, warehouse and factory.The output from the phase is presented in a simple table (shown in figure 8.4 – note thephantoms are considered in phase six) listing SCOR process elements highlighted by theirboundary status (core, promoted or simplified) for the factory, warehouse and customer (step5.4). This table was used to verify that all core process elements that represent the improvementwill have an impact upon the metric. It was useful to walk through the process starting from eachmetric and working back through the process elements with consultation with the client. Therewere no issues of concern; all the critical links were included. 193
    • Figure 8.4 Promoted, core and simplified process elements (CoffeePotCo)8.2.6 Phase six: Designing the model detailThe level of detail for each process element and input to be included in the model is determinedand designed by following two steps. The preliminary validation only applies to the first step thatlists the actual practices to be represented in the computer model (step 6.1). The description ofthe actual practices are listed, and described in appendix G, which are used to compare with themodel components in the actual computer model.The process elements within the boundary of the model, by status and actor are listed frominformation provided from phase five (step 6.1.1). Each of the process elements are considered inturn to determine if any can be treated as a phantom (processes which contain only a workflowinput and no practice can be identified that would influence the characteristics of the workflow).Figure 8.4 in the previous section shows fifteen process elements that can be treated as aphantom in the CoffeePotCo validation case. These are effectively dormant processes thatprovide a necessary interconnection. The SCOR documentation is a useful aid as it suggests arange of example practices for each process element which help identify relevant practices andfocus discussions with the client.Step 6.1.3 aims to describe the actual practice for the existing ‘AS-IS’ and ‘TO-BE’ supply systemfor the process elements included in the model. The inputs and processes identified by the 194
    • analysis were then considered to describe how the inputs (and sources) are converted to producean output (to a destination) specified by each actor. Although there is no check after this stage,step 7.2.2 was implemented to ensure that the descriptions were ‘correct’ from the perspectiveof the client and that there are no unnecessary details, or omissions for the objectives of thestudy.8.3 Purpose of the evaluation of the methodologyThe purpose of the evaluation of the methodology is to demonstrate that it is both initiallyfeasibility and has utility. This conforms to two of the evaluation criteria described by Platts(1993) and later expanded upon by Tan and Platts (2002) and Blackwell (2003). Section 3.1justified that the usability criteria is outside the scope of the preliminary validation but issues fortesting can be identified (see section 8.4). The following sections expand upon the feasibility andutility criteria in the context of evaluating the methodology presented in this thesis.8.3.1 Criteria for evaluating the feasibility of the SCM2The criterion for feasibility has been further broken down by Tan and Platts (2002) to include: theavailability of information, timing and participation. There is no evidence of studies including anevaluation of feasibility using the sub-criteria of timing and participation except in Tan and Platts(2002). Blackwell (2003) considers the availability of information and offers a definition which hasalso been used by Benton (2009). This includes evaluating whether sufficient details wereprovided by each step in the methodology in order for each to be successfully completed(Blackwell, 2003).Benton (2009) notes that some significant issues could arise when discussing ‘participation’ butlike this study and Platts (1993) earlier work, more participants are required to discuss this sub-criterion in more detail. The ‘timing’ sub-criterion can also be viewed in the same way although itis important to note that the methodology does not claim to address project managementoutcomes. Using Platts (1993) original definition of ‘feasibility’ and Blackwell’s (2003) definitionof Tan and Platts (2002) ‘availability of information’ sub-criterion the question used to evaluatethe initial feasibility of the SCM2 is shown in table 8.6.Table 8.6 Summary of the feasibility criteria to be examined Sub-criteria Feasibility Question addressed in this thesis (Tan and Platts, 2002)Can the methodology be followed to Availability of information – whether sufficient Was there sufficient informationdescribe the actual practices to be details were provided at each step in order to required and provided by the steps toincluded in the model? complete them successfully (Blackwell, 2003) complete them successfully? 195
    • 8.3.2 Criteria for evaluating the utility of the SCM2The research programme identified that the preliminary test should also demonstrate that themethodology has initial utility. In Platts (1993) terms, utility concerns testing that the processprescribed by the SCM2 did provide a useful step in the creation of a conceptual model for SCMapplications. In the context of Platts (1993) original work this would mean that at a practicallevel, it is possible to create a conceptual model that can be developed into a computer model. Ata subjective level, this would involve asking actual users to establish their reactions when usingthe methodology. At this stage of the research the preliminary test focuses upon the practicallevel because further applications are required with actual participants.Tan and Platts (2002) provide further, a breakdown of the sub-criteria for testing the ‘utility’.These include relevance, usefulness and facilitation. Tan and Platts (2002) do not expand on whatis meant by ‘relevance’ but Blackwell (2003) offers a definition for usefulness and facilitation.Table 8.7 applies these definitions in the context of this thesis.Table 8.7 Summary of the utility criteria to be examined Sub-criteria Utility Question addressed in this thesis (Tan and Platts, 2002) Is the outcome from the methodology (description of the Relevance – No definition available actual practices to be represented in the computer model) applicable for the purpose at hand? Are there any significant omissions or differences Usefulness – Blackwell (2003) between the outcome from the methodology (descriptionDoes the methodology aid a described this as whether the of the actual practices to be represented in the computeruser to describe a useful and worksheets and tools included in the model) and the computer model (how the actual practicesrelevant set of actual practices methodology helped the would-be were represented by the model components in theto be included in the computer user to progress through it. computer model)?model? Facilitation – Blackwell (2003) described this as the problems that Did any problems arise when using the methodology? may arise when using it in practice and any additional comments or areas of Any areas that need to be addressed in further work? concern that need to be addressed8.4 Evaluation of the initial feasibility of the SCM2The validation case demonstrates the methodology can be followed to arrive at the actualpractices to be included in a computer model. The initial feasibility is assessed by following theprescribed steps in the methodology applied to the test case by the researcher. The criteriadiscussed in section 8.3.1 are used to evaluate the initial feasibility of the methodology. Section8.5.1 identifies issues that should be the focus of further refinement and testing particularlyaddressing the limitations of the preliminary validation discussed in chapter three. 196
    • 8.4.1 Evaluation of the availability of information required by the SCM2The methodology requires information for each of the steps prescribed from three distinguishablesources: 1. From information provided by a previous step in the methodology 2. Utilising the SCOR methodology for domain knowledge 3. Consultation with client sources (individuals knowledgeable of the supply system and/or decision-makers)On the whole the process could be followed with ease because each phase noted clearly thepoints of entry and exit. Each step also included what information is required from any previousstep. The methodology also allows for iteration between steps when alterations have beenidentified, when validating parts of the draft conceptual model. It was also clear when the clientshould be involved to provide information, or mainly to clarify, or to verify information that hadbeen extracted from SCOR.A key benefit and novelty of the methodology developed is that it utilises domain knowledgeprovided by the Supply Chain Council SCOR model. It was previously argued that this couldprovide an opportunity to aid a user to create a conceptual model in the context of SCMapplications. The SCOR process reference model was used to provide information in each of thephases of the methodology. The aim was not to replace the need for interaction between thestakeholders in a simulation project but to make any consultations more efficient and focused. Inthe CoffeePotCo development case it was observed that using domain knowledge from anexisting process reference model could provide an avenue to aid a user to create a conceptualmodel. It can aid in the identification of information to define a supply problem so that it can becommunicated using established terminology, provide information to drive the analysis and help auser to justify any decisions that need to be documented.The information required by each phase in the SCM2 and any comments on how the informationwas used by following the steps prescribed for the CoffeePotCo validation case are shown inappendix H. Comments are made relating to, using the domain knowledge, how information froma previous step was used and how interaction between stakeholders involved in the conceptualmodelling stage was improved. The analysis identifies some key observations when using SCORfor this purpose is shown in table 8.8. 197
    • 2Table 8.8 Key observations from an analysis of the information requirements for the SCMInformation required by Key observations the methodologyClarity of terms and SCOR model offered clarity and a means of communicating the supply chain problem using the predefinedmeans of descriptions of supply chain performance attributes, improvement and supply settingcommunication The objective of study defined in terms of SCOR performance attributes was relatively straight-forward to define a metric for delivery performance and inventory cost. SCOR provided information on how the metrics were calculated and the processes that provided the data sources. The actors were defined using the methodology to ensure that the proceeding analysis could be undertaken with the correct measurement span. An issue arose when describing the ‘Inventory days of supply’ metric which is only oneDescribing the of the metrics that SCOR does not define data sources. The supply chain council suggests this can beobjectives extracted from an existing system (e.g. accounts system). For the purposes of building a model this may mean that this activity needs to be represented in the model, or the SCOR documentation needs to be improved to identify which processes provide the necessary data to calculate the metric. In this case, the value of inventory is calculated by multiplying current finished stock level with the cost per unit – an input could be identified that satisfied this need. SCOR could be used to describe the decomposed processes for each supply chain improvement. Unlike the refinement cases, the improvement in the CoffeePotCo case was not explicitly described in theDescribing the practice guide. This did not present a problem because the descriptions could be used to identify whichimprovements processes would be affected by the change. The steps in the methodology were sufficient to cope with this scenario, with interaction with the client to verify. Once the supply problem was described in detail it was relatively straight forward to use the information provided by the SCOR model (e.g. inputs and their sources) to undertake the boundary setting analysis. The documentation (SCOR reference library for simulation modelling) developed was useful in this activityDescribing the inputs and it can be observed that embedding the information into a tool could provide an opportunity toand interconnections in automate the steps necessary to provide the information necessary when deciding the model boundary.the model This stage can be tedious and time consuming, automation would eliminate the need for steps to be completed manually (see section 8.6 for a further discussion) and hold accurate information which is specific to the needs of simulation modelling. The information describing each process and input was useful when deciding on how to treat each processDeciding what to element in the model boundary. The process was greatly improved by documenting the decisions andinclude in the model providing rationale. The SCOR practice guide was useful when identifying actual practices for each process element andIdentifying and describing them. It is intended that this will help focus and structure any interactions between thedescribing actual modeller and client (in this case the information was extracted from a discussion of the computer modelpractice and one of the authors of the Taylor et al., 2008 paper).It was clear from the evaluation that SCOR offered considerable benefits to focus and provide astructure when creating a conceptual model due to the information that can be extracted to driveeach step. There were however some notable weaknesses, not for the SCM2 itself but in order tomaximise the potential of using a supply chain process reference model (in this case SCOR) in theconceptual modelling stage of a simulation project. This included the need for a reference libraryto provide information on the SCOR process elements and the necessary interconnectionsbetween them so that it can be used for simulation modelling purposes. The key limitation is thatSCOR focuses upon describing a single organisation that sits within a supply chain. SCOR needs tobe further developed on how the practices, processes, interconnections (i.e. clarify the inputs andoutputs to and from suppliers and customers) and metrics (i.e. ensure that data sources aredescribed for all metrics) are described within and between suppliers and customers in a supplysystem.8.4.2 Evaluation of the availability of information provided by the SCM2The methodology provides information from each of the steps prescribed, that are used in twoways: 1. To provide information for a proceeding step in the methodology 198
    • 2. To provide information necessary to draft, and later document, the finalised version of the conceptual modelThe process laid down in the methodology could easily be followed to provide the necessaryinformation. The methodology aim is to draft and then finalise the conceptual model bydescribing the computer model to be developed. This was gathered from each of the relevantoutputs from the steps within the methodology. In the case of the CoffeePotCo validation casethe end result was a list of actual practices which was comprehensive and delivered in a morestructured and focused way. In order to arrive at the list of actual practices the followinginformation needed to be provided shown in table 8.9. 2Table 8.9 Key observations from an analysis of the information provided from the SCM Information provided from the Key observations methodology A structure was provided in order to present the objective, improvement and setting. It was also checked to ensure that the improvement in the Coffeepot Co case would bring aboutDescription of the supply chain change to achieve the set objectives. This is an important phase that needs checking with theproblem client to ensure that the subsequent analysis is completed effectively (the details provided in the supply problem drive the following analysis).Supply chain performance metrics to Using a predetermined format this could be described using SCOR terminology and processmeasure each objective, how they are descriptions, which would aid in communicating the problem between the stakeholders incalculated and necessary data the simulation projectsources for each metric and actorDecomposed business processesnecessary to represent each As aboveimprovement for each actorList of process elements and inputs Improves decision-making and documentation of decisions madeand decision on their boundary statusList of process elements that have A number of process elements could effectively be treated as a ‘phantom’; this was easilybeen treated as ‘phantoms’ displayed in the table provided.The methodology utilises the strengths also held by the SCOR model (discussed in chapter six).For instance, SCOR is a process framework developed with partners from a range of differentindustries. It can be used to communicate supply chain processes, inputs and outputs to eachprocess, metrics and how they are calculated, practices and where they are implemented in auniversal way. This is important because a critical problem in simulation modelling is defining theproblem sufficiently and accurately so that the content of the model can be determined. TheSCM2 aids a modeller through this process in a structured and focused way and improvescommunication between project stakeholders by documenting any rationale for the decisionsmade.8.5 Evaluation of the initial utility of the SCM2The validation case demonstrates the methodology which aids a user to describe a useful andrelevant set of actual practices to be included in the computer model. The initial utility isexamined by comparing the model components implemented in the computer model, against the 199
    • actual practices that the methodology indicated should be included. The criteria discussed insection 8.3.2 are used to evaluate the initial utility of the methodology. Section 8.5.2 identifiesissues for testing the utility criteria and addresses any limitations of the preliminary validation asdescribed in chapter three.8.5.1 Relevance of output derived from the SCM2The outcome from the point at which the methodology is applied is relevant for the purpose athand. A set of actual practices to be modelled could be described from the information providedin the previous steps and using the SCOR model (presented in appendix G). It can be suggestedthat following a structured approach can improve the rigour in the process of conceptualmodeling. In particular, the accuracy of the descriptions provided should be improved byfollowing the necessary checks within each phase and the final validation procedure (notconsidered in the validation case). This addresses the need that conceptual modelling usuallyrequires iteration between steps in the methodology to address any changes/alterations deemednecessary.Both the modeler (in this case the researcher) and client (in this case one of the paper authors)are involved in the description which has been checked within the phases of the methodology.These checks were in place to improve both the validity and credibility of the draft conceptualmodel. This is strengthened by embedding predefined domain knowledge that was originallydesigned and tested in 70 world-class manufacturers from diverse industry segments (Stewart,1997). More recently, SCOR has often been regarded as an industry standard for evaluating andimproving enterprise-wide supply chain performance and management (Wong and Wong, 2008).Using a reference model that has been widely developed, tested and applied in practice, presentnew opportunities to develop methods and tools for conceptual modelling that can be used forthe practicing manager.8.5.2 Usefulness of the output derived from the SCM2The methodology can be used to provide useful output that can aid in the creation of aconceptual model for SCM applications. This can be shown by demonstrating that no significantomissions or differences occur between the outcome derived from the methodology (descriptionof the actual practices to be represented in the computer model) and the computer model (howthe actual practices were represented by the model components in the computer model).Appendix G provides a table that compares the actual practices to be modelled, how eachpractice is represented in the computer model (for the CoffeePotCo as presented in Taylor et al.,2008) and makes some comments on any omissions or differences. 200
    • The comparison showed that all the actual practices had been represented in the computermodel but the differences resided on the whole in how they were implemented. This wasexpected as it was previously noted that the conversion from the descriptions of actual practice tomodel components is influenced by a number of factors. However, an observation can be madethat the description is detailed enough to aid the user to perform the conversion with someguidelines. In particular the description of the actual practices had been described by identifyingthe boundary of the model at the required level of detail based upon knowledge of the domainand with structured consultation with the client.The list of actual practices was comprehensive because the methodology could be followed toidentify what to include, or not, and checks were in place to ensure that they were correct fromthe modellers and clients perspective. In the case of the computer model developed in Taylor etal., (2008) no structured analysis was undertaken (i.e. analysis relied upon the experience of themodeller) and the practices to be modelled were not explicitly documented. The design choicesand evaluation of the content of the model were not presented in the Taylor et al., (2008) paperbut decisions were taken as a group of modellers. For instance, the modellers had incorporated anumber of simplifications and assumptions into the model.There were four practices that were considered as ‘core’ process elements and described in detailduring the description of the actual practices. These can be summarised as activities that makeup the ‘deliver’ process in the factory including: The factory loads the product onto the distribution vehicle The factory consolidates orders to suit container size The factory collects orders from production for each container load The factory packs the container, the time taken is dependent on container sizeInterestingly the modellers for the CoffeePotCo validation case had simplified the four activities asa single ‘shipping delay’ for both a full container load of ‘air freight’ and ‘water (sea) and truck’.The methodology placed a greater weight on these activities than the modellers. This was evidentin the application as it was highlighted that significant debate was needed to adequately describethe processes that would be influenced by the improvement. The inputs to the processes thatrepresent these actual practices would have been considered resulting in candidates beingincluded and the rationale defined. It has highlighted that the methodology has been useful inproviding a structured and comprehensive approach that has ensured that the participants in theconceptual modelling process have considered and justified choices in the design. 201
    • The ‘shipping delay’ includes a transportation lead-time which was taken from Zeng (2003) whoprovides an estimate and considers what the activities include. Zeng (2003, pg. 372) states thatthe activities in the ‘deliver’ process ‘typically begins with consolidation, involves the transfer ofconsolidated goods to the airport/sea port by rail or truck, storage in warehouses, loading, actualtransit, unloading, customs clearance, transfer to the destination and ends with receiving’. It canbe assumed that this description adequately includes the four activities described.There are some other considerations that the four activities may have led the modeller toconsider. For instance, in the computer model an infinite capacity of loading and packing isassumed and that shipments are made instantly. In the real world, these activities would placeconstraints on the model and shipments will depend upon the availability and capacity of thetransportation models for each shipping method. The Taylor et al., (2008) paper did not discusswhether these issues were significant or not in the discussion of the process parameters. It wasassumed that these practices could be simplified by representing them as a time delay. A benefitof using the methodology is that the procedure did force the facilitator to justify decisions onwhether the processes should be included and how they should be represented.A significant advantage of the methodology is its ‘usefulness’ because it provides a detailed andcomprehensive set of actual practices to be represented. It was previously argued in this thesisthat little attention is paid to a documented description of a conceptual model in publications andthere is little evidence of structured approaches being followed (e.g. lack of methods,understanding). The design of a computer model or even, conceptual modelling, withoutfollowing a structured approach or documentation may lead to a model that is biased, basedupon the modellers perspective and experience of evaluating SCM problems using simulation (e.g.over-simplification or too much detail). Significant value is offered by following a structuredapproach as it utilises domain knowledge, consultation with client sources and checks have beenin place to ensure the correctness of the model. This reduces the prospect of over simplifying themodel or having too much detail which should lead to more valid and credible conceptual models. 8.5.3 How the methodology could be facilitatedThe methodology could be followed to create a conceptual model but at present the guidelinesmay require knowledge and expertise, particularly an understanding of the SCOR processreference model. The methodology aids a facilitator to describe and document a supply problem,formulate the model boundary, describe the detail of the model components to be included inthe model and a detailed validation procedure. Nevertheless, an expert facilitator is required to 202
    • follow the process, utilise SCOR and perform the analysis. The problems identified and areas thatneed to be addressed in further work focus upon this central issue.The purpose of using SCOR for domain knowledge was to potentially make the process ofconceptual modelling more focused and efficient. It is clear that focus is clearly delivered by theproposed methodology and efficiency can be greatly improved by identifying opportunities tosimplify the analysis conducted at the conceptual modelling stage. It has been argued in theliterature that SCOR can have many benefits when combined with a simulation approach (e.g.Kasi, 2005; Persson and Araldi, 2009). This thesis agrees with this proposition and demonstratesthat it is feasible and useful at the conceptual modelling stage but that some significantdrawbacks exist that need to be addressed by the SCC and the wider supply chain simulation usercommunity.Table 8.10 shows that a major strength of SCOR is the shear range and detail of its descriptions ofpractices, metrics, processes and flows but the list is still by no means exhaustive. The keyweakness is that SCOR lacks detail in how a supplier and customer interact with an organisation.Using Harland’s (1996) term it is strong at describing the business processes within the boundaryof the organisation and to an extent dyadic relationship although the flows between them are notadequately described. Moreover it does not describe how practices, metrics and interconnectionsbetween processes can be implemented across a chain or network. Improvement in these areaswould not only improve the utility of the SCOR model itself but for conceptual modelling and forbuilding and validating simulation models in general.Table 8.10 Evaluation of ‘facilitation’ when using SCOR Descriptions Observation in Commentoffered by SCOR CarCo caseSCOR practices Practice not Practices could be identified but they are by no means exhaustive adequately Difficult to ascertain how a practice may be implemented in more than one organisation covered and/or between suppliers and customers SCOR metrics are extensive compared to alternative offerings in the literature (e.g. Beamon, 1999; Gunasekaran et al., 2001, Shepherd and Gunter, 2006) No issues SCOR metrics Provide detailed description of how they can be calculated identified Some discussion of how they can be measured across a supply chain but main focus is within the firmSCOR No issue From a practice and/or metric the processes and inputs to that process can be identifieddecomposed identified If no practices could be identified the SCOR decomposed business process descriptionsbusiness process can be selecteddescriptions 203
    • 8.6 Identification of issues for testingTesting is required to improve the validity and generalisability of the methodology presented inthis thesis. This section identifies areas for testing, particularly the usability of the methodologywhich has yet to be preliminarily validated. Additionally, to further improve the feasibility andutility of the methodology.8.6.1 FeasibilityThe methodology is shown to be initially feasible but further refinement and testing is required todemonstrate the ‘general’ feasibility of the methodology. Platts et al., (1998, pg. 521) expresses aview to improve the general feasibility: ‘By repeating the process in several companies and using different facilitators provides greater confidence in the more general feasibility of the process can be achieved’In the context of this thesis this requires repeating the methodology for several different SCMapplications using different facilitators. The areas that should be particularly focused upon infuture refinements and feasibility testing include: 1. How different facilitators utilise the domain knowledge provided by SCOR – This would be of particular interest as the methodology requires at present ‘expert facilitation’. The question to be addressed is to demonstrate how SCOR can be used to ensure the process of conceptual modelling is more efficient and focused 2. Test how different participants are involved in each step – The methodology suggests how participants should be involved in the procedure but this has yet to be tested 3. Test how the information provided to draft a conceptual model is used at the final validation phase – The methodology does not test the final validation phase. This is a significant and substantial area for further research activity in its own right.8.6.2 UtilityThe methodology is shown to have initial utility but further refinement and testing is required togeneralise from the preliminary findings. Similar to the discussion for ‘feasibility’ furtherapplications and observation of actual facilitators is required. The refinement and testing wouldbenefit greatly by interviewing the facilitators and participants who are following themethodology to identify how it can be improved. This would include: 1. Relevance of the methodology to create a conceptual model that is more valid and credible – Evaluate how a modeller may use the output provided from the methodology to build a computer model 2. The usefulness of following a structured approach as opposed to no formal methods – The thesis does not claim that the methodology offers a ‘better’ approach but has 204
    • discussed the benefits of following a structured approach and presented opportunities to improve it. Further work should observe the differences between alternative approaches to conceptual modelling to learn from them and identify the value of following the methodology. 3. Identify from the participants whether any problems arose or need to be addressed when using the methodology – Identify any issues and areas for further refinement from participants using the methodology8.6.3 UsabilityThe thesis does not claim that the methodology has been validated against the usability criteria.The research methodology chapter argued that the usability is a test that should be conductedonce the methodology can be shown to be initially feasible and have utility. Usability wouldinvolve observing actual participants to address the question of how easily the methodology asprescribed could be followed. Table 8.11 below presents a summary of criteria that should beused to test the usability of the methodology.Table 8.11 Summary of the usability criteria to be examined Usability Sub-criteria Question addressed in this thesis (Platts, 1993) (Tan and Platts, 2002) Are the steps in the methodology clear and well Clarity – Blackwell (2003) defined this as how well the structured in order for a potential user to methodology was structured achieve the desired outcomes?How easily can the Ease of use – Blackwell (2003) defined this as how Can the methodology be followed with ease andmethodology be easy the methodology was to be used and understood be understood by a potential user?followed? Appropriateness – Blackwell (2003) defined this as the Is the methodology the desired length, should length of the methodology and how it might be either any of the steps be extended or reduced? extended or reducedObservations at this stage of the research project can be made but these can only be claimedfrom the perspective of the single researcher. These observations are included in appendix I fromthe perspective of the researcher when applying the methodology to the CoffeePotCo validationcase. These initial researcher observations are used to identify issues that require furtherrefinement and testing with actual participants following the methodology as prescribed. Theobservations include: 1. The phases and steps in the methodology were clear, well structured and detailed – An overview is provided that demonstrates how each phase should be conducted and a detailed table describes each of the steps, information requirements and what it provides, and how participants are involved in the process. 2. The methodology should be used by both experts and benefit novice users – The methodology can be followed with ease by the researcher but further work should focus on simplifying the methodology and identifying ways to facilitate the process with little 205
    • human interaction. In particular, how to embed the domain knowledge offered by SCOR without the participants needing to be competent in its use. 3. The methodology is appropriate but can be improved to reduce time consuming activities – The methodology includes a host of guiding principles and procedure that is unique for evaluating SCM problems using simulation. Using SCOR had a number of advantages but the analysis could be improved to reduce time consuming and repetitive activities. It cannot be claimed at present that this speeds up the process of conceptual modelling but there are many opportunities to refine the methodology so some steps can be automated and thus become redundant.It would be of particular interest to observe both expert and novice potential participants andfacilitators of the methodology. This would build upon the studies by Willemain (1994; 1995) andWillemain and Powell (2007). An aim of further refinements must concentrate on identifyingways to minimise the need for expert facilitation so that novices could potentially use it. Threeopportunities to address this need are discussed in the following section.8.7 Opportunities to improve the SCM2The refinement and validation stages have led to three opportunities that can be identified tofurther improve the methodology. These opportunities provide further avenues to extend thework and ensure that the methodology is accessible to a wider audience and are both usable byexpert and novice users. These opportunities are listed below and discussed in the followingsections: 1. Role and impact of automating certain steps in the methodology 2. Strengthening the utilisation of SCOR to provide domain knowledge for conceptual modelling 3. Develop a web-based tool that can facilitate the methodology and make it more widely accessible for potential users8.7.1 Role and impact of automating the methodologyThe application of the methodology to the development and validation cases using predefinedtemplates in an Excel spreadsheet, added additional functionality to how the methodology wasused. There is an opportunity to take advantage of additional functionality offered by aspreadsheet package to automate a number of key concepts that were identified and described inchapter six. 206
    • The usability and facilitation of the methodology could also be greatly improved by incorporatinga guide for using SCOR in the context of conceptual modelling (discussed in 8.6.2). In particularany potential errors for using the domain knowledge to provide information can be reduced andsome steps can be made redundant (e.g. a macro could perform a set of activities). Theseopportunities are presented in table 8.12. 2Table 8.12 Opportunities to automate aspects of the SCM Link to key concept described in the Automation concept Description design Key concept 4: Identification of the core The detail provided from SCOR that describe practices and processes that need to be modelled, theirSelect predefined objectives at stage one and used to identify the core inputs generated from a source processpractices and objectives process in the model can be selected from a predetermined element (using SCOR detail provided in key catalogue concept 2 and 3) The inputs to each process are defined by SCOR. The dataIdentification of required to perform the analysis to identify candidateinterconnections process elements for consideration can be predefined and Key concept 4: see abovebetween processes generated as the methodology is followed (e.g. without copying and pasting from an independent SCOR guide) A predetermined macro incorporated into the spreadsheetDiscrimination of application can perform this activity without any human Key concept 5: Process elements that havecandidate process interaction. This would effectively make phase three yet to be included in the model can beelements redundant. The analysis would be activated when a user classed as candidates for possible inclusion makes decisions at the model boundary formulation phase. Key concept 6: Candidate process elementsProvide information to are considered in turn for inclusion in the A macro incorporated into the spreadsheet application candrive the model model as they form a critical provide the information from phase four to phase fiveboundary formulation interconnection between core processes and the real world Key concept 7: Included process elementsAutomate the phantom Processes that meet the ‘phantom’ criteria can be are considered in turn to identify thoseprocess rule automated making the step redundant in phase six that could be simplifiedCreate a draft The ’text’ descriptions provided from phases in theconceptual model to be methodology used to draft the conceptual model can be Key concept 10: The conceptual model isvalidated in the final generated using a predefined macro and template in a documented and validatedphase spreadsheet application.8.7.2 Strengthening the utilisation of domain knowledgeThe development of the methodology has shown that there is a need for domain knowledge inthe process of conceptual modelling. One source of this knowledge includes SCOR; an existingsupply chain operations process reference model widely used in practice. The benefits of usingSCOR for simulation applications, particularly at the conceptual modelling stage have been notedin this thesis. Additionally a number of observations have been made to improve the usefulnessof using SCOR and how it can be further developed for the purposes of simulation modelling.Firstly, the documentation of SCOR needs to be presented with more detail on how a practice andmetric is to be implemented between supply chain actors. Secondly, the documentation needs tobe clear on how different manufacturing environments (e.g. MTO, MTS, ETO) can use differentconfigurations of process elements, as there is significant commonality between process typeswithin each environment. Most significantly SCOR does not attempt to include typical practices 207
    • such as ‘MRP’ in its planning processes and some other practices such as ‘kanban’ are expressedin scheduling of product deliveries but not included in the planning descriptions (e.g. plan numberof kanban cards). These are areas that would strengthen the domain knowledge provided bySCOR to enable the information to be used more effectively and with greater ease.8.7.3 Development of a web based toolA web-based tool could be created that combines the methodology with the functionality of aspreadsheet which could perform the actions necessary to create a conceptual model. The aimwould be to create a user interface which would guide the user through the steps; noteinformation needs (e.g. from SME’s, SCOR) and involve the necessary participants (bringingtogether the opportunities noted in sections 8.6.1 and 8.6.2). A spreadsheet application wouldprovide many benefits as it can include predefined templates (e.g. collect and present data), aid inthe facilitation of a guide, perform some of actions that can be automated and be more widelyaccessible for potential users. This would improve the usability and utility of the methodologyand provide significant opportunities to reduce the complexities of developing simulationconceptual models.8.8 Chapter summaryThis chapter has implemented stage V of the research methodological programme and addressedthe final research objective of the thesis. The preliminary validation was undertaken by applyingthe methodology that has previously been refined and aligned to the specification to a validationcase (CoffeePotCo). The key observation was that the methodology is both initially feasible andhas utility but further refinement and testing is required. Particularly the ‘usability’ criteria haveyet to be validated and there is a need to extend the preliminary feasibility and utility evaluation.The key issue for testing is to apply the methodology in different industrial contexts and toobserve potential facilitators (independent from the researcher) and participants in how theyfollow the methodology.It was shown that a methodology can be followed to create a list of actual practices to bemodelled that are both useful and relevant. This was compared with an actual model published inthe literature so that the ‘utility’ of the methodology could be examined. The methodology atpresent requires ‘expert facilitation’ to follow the methodology but the majority of issues thatrequire further refinement surround the use of SCOR. This is an important finding as SCOR iscommonly used for simulation modelling (this is one of the first studies that has considered it forthe purpose of conceptual modelling) but the weaknesses have not explicitly surfaced except inthis study. 208
    • The chapter also discussed some opportunities to improve the methodology and provide furtheravenues for research. A central aim for developmental work should focus upon developing aweb-based tool that could aid in the facilitation of the methodology. This tool could also takeadvantage of opportunities to automate the key concepts and steps identified. There is also asignificant opportunity to strengthen SCOR for simulation modelling, particularly how it can beutilised to provide some of the domain knowledge required for conceptual modelling. 209
    • Chapter 9 Conclusion and future workThe final chapter concludes the thesis by summarising the primary and secondary contributionsmade by the thesis, conclusions from each of the research objectives and questions, limitations ofthe study and implications for further work. The primary contribution delivered by this thesis canbe summarised as: “The development, refinement and preliminary validation of a methodology that utilises domain-knowledge combined with a procedure that can be followed to create a simulation conceptual model for SCM applications”The thesis argues that the primary contribution is both relevant and significant; it will also providebenefits to both research and practice. The main limitation is that testing is required to expandupon the preliminary validation and an opportunity exists to develop the methodology into aweb-based application tool. The chapter structure therefore addresses: Original contributions made by this thesis (section 9.1) – details that the primary contribution of the thesis and associated secondary contributions Conclusions from the research objectives and questions (section 9.2) – Reviews each of the objectives and questions to demonstrate that they were adequately addressed Limitations of study (section 9.3) – Identifies that further testing is required in different industrial settings and with actual users to improve the ‘validity’ and ‘generalisability’ of the SCM2 Implications for further research and practice (section 9.4) – Reviews the contribution made in the thesis in light of Robinson (2006) list of opportunities for advancing research in the area of conceptual modelling9.1 Original contribution made by the thesisThis section summarises and justifies the primary and secondary contributions made by thisthesis. Firstly, section 9.1.1 discusses the primary contribution and secondly, section 9.1.2discusses each of the secondary contributions. These contributions are noted in table 9.1 alongwith the gaps in existing research contributions, the research objectives, questions addressed andwith references to the location of the discussion in the thesis. 210
    • Table 9.113 Research contribution made by this thesis Research contribution made by this Research Thesis Gap addressed in existing research thesis Obj./Q’s ref.Primary research contributions: The primary contribution of this thesis is a seven phase methodology that utilises domain-knowledge with a procedure that can be followed to create a simulation conceptual model for1. Development of a methodology SCM applications. The key concepts are identified from that utilises domain-knowledge considering the design issues for each of the requirements with a procedure that can be specified. The core idea is that SCOR is one source of domain Obj. 2; Chapter followed to create a simulation knowledge that can potentially enable a more efficient and Q3 6 and 7 conceptual model for SCM focused conceptual modelling process. There is little research on applications (summarised in the domain specific requirements for conceptual modelling and section 9.1.1.1) no methodology currently exists. Although research, particularly the development of new theory in this area, has been noted as critical for the rigour of supply chain studies (e.g. Manuj, et al., 2009). The methodology is refined and its initial feasibility and utility is preliminarily validated by applying it to a different case application. The evaluation illustrates that the methodology is2. Preliminary validation of a initially feasible but warrants testing of its feasibility and utility methodology for creating with original industrial case studies and potential users of the Obj. 3; simulation conceptual models methodology. Additionally, comments are made about the Ch. 8 Q4 for SCM applications usability of the methodology and an outline for rigorous testing is (summarised in section 9.1.1.2) noted. There are no other methodologies available and one general framework (Robinson, 2004a; 2004b) exists which has been illustrated (Robinson, 2008a; 2008b) but with little detail or critical evaluation.Secondary research contributions: The different approaches to simulation conceptual modelling are identified with examples in literature. These include principles, methods of simplification, frameworks and methodologies.3. Examination of existing Research identified that no methodologies exists, one framework simulation conceptual modelling Obj. 1; Chapter has been suggested, while there is a vast amount of contributions practice in SCM (summarised in Q1 4 that discuss guiding principles for modelling in general. Robinson section 9.1.2.1) (2004b) does devote a chapter of his text to conceptual modelling concepts, but it lacked a detailed examination of practice, in particular in the context of evaluating supply chain problems. The requirements for an effective conceptual model, requirements of good methodologies and the requirements for simulation conceptual modelling in SCM are examined. The requirements for an effective model have been discussed and the4. Specification of the requirements for good methodologies draw upon the work by requirements for simulation Platts (1993) and publications that have used the criteria more Obj. 1 Chapter conceptual modelling in SCM recently. The requirement for conceptual modelling in SCM has Q1 5 (summarised in section 9.1.2.2) yet to be adequately described; this includes defining what is meant by the term ‘supply chain problem’. This is detailed in terms of the range of supply chain improvements selected, to achieve supply chain performance measures within its supply setting.9.1.1 Primary research contributionThe primary contribution as previously stated is the ‘development, refinement and preliminaryvalidation of a methodology that utilises domain-knowledge with a procedure that can befollowed to create a simulation conceptual model for SCM applications’.This thesis has contributed to knowledge in the areas of conceptual modelling and supply chainanalysis.In the area of conceptual modelling, this thesis has demonstrated that a methodology can bedeveloped that can combine a generic procedure for simulation conceptual modelling with 211
    • domain-knowledge to improve the efficiency and focus of a process that guides a user to create aconceptual model. In this instance, domain-specific knowledge has been embedded in the formof the Supply Chain Council SCOR reference model. Hence, the methodology can be followed, andthen therefore used to describe a computer model, which then may be built from a description ofa supply problem. In conceptual modelling, there was no established methodology for thedevelopment of conceptual models; this work creates such a methodology. The initial feasibilityand utility of the methodology has been illustrated by applying the procedure with developmentand validation cases.In the area of conceptual modelling, we now know that: Decision rules can be used to consider which business processes to include within the model boundary from identifying the critical relationships between (core processes) and within the setting (real world) of the processes that are associated with each objective and improvement Decision rules can be embedded in a generic procedure to simplify inputs to the model and to determine when no further processes should be included in the scope of the model (i.e. model boundary is set) General principles, simplification methods, methods for representing model content and validation methods (both within and at a final phase) can be incorporated into a general and comprehensive procedure for conceptual modelling to minimise the types of problems that could be encountered in a simulation projectIn the area of supply chain analysis, the thesis has shown that embedding SCOR in a genericprocedure for simulation conceptual modelling can: Aid in the description of a problem from the perspective of the client using standard terminology and domain-specific process detail Aid in determining how an objective can be measured using standard descriptions of typical performance attributes and metrics; plus data collection needs from associated business processes at different levels of detail Aid in determining how each improvement can be represented by business processes to implement each improvement at different levels of detail Aid in determining the model boundary by providing information on the relationships between business processes (i.e. interconnections between inputs and outputs germane to each process element) 212
    • Aid in providing clear domain-specific guidelines for extracting information from a pre- defined process reference model and when necessary focus consultation with people who are knowledgeable about the system being represented Aid in focusing consultation with people who are knowledgeable about the system being represented to determine the detail of the actual practice that needs to be included from the descriptions provided for each process element included in the model boundary and simplified inputsDespite SCOR purporting to be a comprehensive supply chain reference model, we know now thatit has the following deficiencies which could be improved to enable the information to be usedmore effectively and with greater ease: SCOR documentation needs to be presented with more detail on how a improvement and metric is to be implemented between supply chain actors (e.g. not just within the focal firm) SCOR documentation needs to be clearer on how different manufacturing environments (e.g. MTO, MTS, ETO) use different configurations of process elements as there is significant commonality between process types within each environment SCOR does not attempt to include typical practices such as ‘MRP’ in its planning processes and some other practices such as ‘kanban’ are expressed in scheduling of product deliveries but not included in the planning descriptions (e.g. plan number of kanban cards)The following section discusses in more detail the combination of a procedure and domain-specific key concepts that incorporate domain-knowledge for SCM applications (discussed insection 9.1.1.1). This is followed (discussed in section 9.1.1.2) by a discussion on the way in whichthe research programme was structured to firstly develop the theory (stages I – III), followed byrefinement (stage IV) and a preliminary validation (stage V). Further work has been outlined inthis thesis to advance the area of simulation conceptual modelling, in particular for the purposesof supply chain analysis and to test the methodology. This is outlined in section 9.4.9.1.1.1 Summary of the SCM2: A procedure and domain-specific key conceptsThe methodology has seven phases which have been described in detail in chapter 7 andpreliminarily validated in chapter 8. It incorporates a set of key concepts and a procedure forsimulation conceptual modelling of supply chain problems. These key concepts were identified inchapter 6 after considering the design issues for each of the requirements specified in chapter 5. 213
    • The phases for the methodology were identified that incorporated each of these key concepts inlight of a general process for conceptual modelling. It can be argued that this is a first attempt toidentify specific concepts and a bespoke process for the supply chain domain.The general phases of the methodology and associated key concepts are summarised in table 9.2.The detail of the methodology is described and justified in a series of tables for each phase inchapter 7. Each phase describes how information is required and provided, who participates ineach step, the tools that should be used to represent and document the model content, and thenecessary ‘checks’ to ensure the output is valid and credible. Iteration is required between stepsif a check had not been satisfied (requires a phase to be repeated), or a return to a previous stepis required if new information is generated (i.e. in the case of formulating the model boundary).The methodology is entered when a client has a supply problem to be evaluated using asimulation approach. 2Table 9.214 SCM : Procedure and key concepts for SCM applications Procedure Key concepts for SCM applicationsPhase one: Describe the supply problem - The supply 1. Supply chain problem: describe theproblem is described from the perspective of the client objective, improvement and supply settingPhase two: Determine how each objective is to be 2. SCOR SCM performance metrics can be usedmeasured – The objective is described in terms of how to identify how an objective is to beit will be measured measuredPhase three: Determine how each improvement is to 3. SCOR practices can be used to describe eachbe represented – The improvement is described in improvement to be evaluatedterms of how it is to be represented 4. Identification of the core processes thatPhase four: Determine how the inputs and their need to be modelled, their inputs generatedsources interconnect within the model and with its from a source process elementimmediate supply setting - Provide a list of model 5. Process elements that have yet to beinputs and candidate process elements (NB: supplies included in the model can be classed asinformation only to formulate the model boundary) candidates for possible inclusion 6. Candidate process elements are considered in turn for inclusion in the model, as they form a critical interconnection between thePhase five: Formulate the model boundary – Provide core processes and the real worlda list of processes and inputs included in the model 7. Included process elements are considered in turn to identify those that could be simplified 8. The detail that needs to be included can bePhase six: Design the level of detail to implement identified from the actual practice for eacheach process and input included in the model process element included and simplified inboundary – Provide a description of how the actual the modelpractices are represented by the model components 9. Modelling practice should represent theand relationships between them essential complexity and detail of the actual practice to be evaluatedPhase seven: Document and validate the conceptualmodel – The draft descriptions provided from the 10. The conceptual model is documented andphases and steps in the methodology are documented validatedand validated 214
    • The core proposition that underpins the key concepts is that the utilisation of domain knowledgecan enable a procedure for conceptual modelling that is potentially more efficient and focused. Itwas proposed in section 6.3 that the Supply Chain Council SCOR model presents a number ofopportunities to realise the proposition in comparison with alternatives. For instance, it hasstrengths in each of Becker et al., (2003) qualities for an effective model. In particular, it is widelyused in both practice (relevance) and for simulation purposes (economic efficiency), as it providesclear (clarity) and correct (correctness) domain knowledge that can be used to compare supplychain configurations (comparability).Section 6.4 demonstrated that SCOR could be used to provide detail on the improvements (has alist of over 420 ‘best practices’), objectives (describes a comprehensive list of ‘performanceattributes and metrics’) and the supply setting (the ‘processes’ and relationships between themdepicted by their source ‘inputs’). This was demonstrated by extracting the detail necessary todescribe two typical supply problems and how they have been evaluated from five representativecases in the literature. Although SCOR presents great potential in the area of conceptualmodelling a number of limitations were identified. These were noted in the discussion of therefined design meeting the requirements for conceptual modelling of supply chain problems (C.f.section 7.5.3) and demonstrated in the evaluation of the methodology (C.f. section 8.3). Theselimitations were considered in more detail in section 8.5 as opportunities to enhance and furtherdevelop the SCOR model and areas for further study to improve the SCM2.9.1.1.2 Development, refinement and validation of the SCM2A review of the research issues for conceptual modelling in the context of SCM demonstrated thata gap exists for a SCM2 that utilises domain-knowledge combined with a procedure. This gap wasproposed to be both a relevant and significant research issue for both developing new theory andpractical application. It was argued (C.f. section 2.1) that improving supply chain performance canhave a significant impact upon an organisation’s performance (e.g. Stewart, 1997; Tan et al.,1999; Kannan and Tan, 2005; Li et al., 2006). One of the challenges faced by companies is toidentify how this potential could be evaluated (Weaver et al., 2007). This is problematic due tothe inherent complexity in supply chain problems (C.f. section 2.2). It was shown (C.f. section 2.3)that simulation is one popular method that is widely used to evaluate the complexity of supplychains (e.g. Ridall et al., 2000; Huang et al., 2003; Van der Zee and Van der Vorst, 2005; Kleijnen,2005). One of the critical, but least understood, stages of a simulation project is the creation ofthe conceptual model (e.g. Law, 1991; Robinson, 2006). 215
    • An examination of simulation studies in the context of SCM showed that little has been writtenabout the conceptual modelling stage. However, Manuj et al., (2009) identify conceptualmodelling as an area that requires more attention to improve the rigour of future research studiesthat use a simulation approach. One reason for this could be the lack of detailed reporting of thedesign choices made during the conceptual modelling stage. A second reason has been suggestedby Robinson (2006a; 2006b) and Wang and Brooks (2007a) of a severe lack of methods andprocedures that could be used by experts and novice users. The thesis has argued that amethodology could incorporate aspects of existing practice and identify novel concepts that couldimprove understanding, usage and the teaching of simulation conceptual modelling.The design for the methodology is based upon a strong conceptual base, (theoretical) and therefinement and preliminary validation of the methodology, by applying it to three SCMdevelopment and validation case applications (empirical). This includes the establishment of therequirements for the methodology (stage II of the research programme) and a discussion of eachof the design issues for each requirement in the outline design (Stage III of the researchprogramme). The refinement of the methodology demonstrated that the methodology has meteach of the requirements for 1) an effective conceptual model; 2) a good methodology and 3) thespecific requirements for conceptual modelling in SCM simulation projects. The outline designpresented a synthesis of the key issues that arose when considering each requirement. The aimwas to identify the key concepts for inclusion into a specific procedure that addressed the need ofconceptual modelling for SCM applications.The developed methodology has been refined using two development cases, one fictional(‘BeerCo’), and the other being a complex and detailed industrial case (‘CarCo’) against thespecification of requirements. A preliminary validation using a different supply problem(‘CoffeePotCo’) was undertaken to evaluate the methodology against the ‘feasibility’ and ‘utility’criteria. This is in line with similar contributions that develop methodologies, such as those thatdevelop a methodology but have no claimed testing (e.g. Apaiah, 2006; Lima, 2008) and thosethat have refined and preliminarily validated a methodology (e.g. Gan Kai, L.W., 2007; Benton,2009). The limitations of the methodology were identified in chapter 8, after identifying issues forfurther testing of the ‘feasibility’ and ‘utility’ in more industrial settings with potential users (C.f.section 8.6). This also includes identifying issues for future testing of the ‘usability’ of themethodology. 216
    • 9.1.2 Secondary research contributionsIn addition to the primary research contribution noted in section 9.1.1, three other advances havebeen made in the area of simulation conceptual modelling in SCM. The contributions evolvedaround underpinning the theoretical context of the developed and evaluation of the SCM2. Thesecan be termed advances to knowledge as it has already been argued that research in the area ofconceptual modelling is spare and very little has been applied in a SCM context. Each of thesecondary research contributions are noted also in table 9.1 with associated detail.9.1.2.1 Examination of existing simulation conceptual modelling practice in SCMA secondary advance made by this thesis includes the examination of existing simulationconceptual modelling practice in SCM. Many researchers have noted that no widely acceptedapproach exists in the area of conceptual modelling (e.g. Pace, 2000a; 2000b; Robinson, 2006).The approaches that can be found are classified as guiding principles, methods of simplificationand frameworks. A fourth is proposed to include ‘conceptual modelling methodologies’; thesewere differentiated from the other three approaches and is the focus of this thesis. The literatureshowed an abundance of contributions that offered general guiding principles and methods ofsimplification. The majority of the findings are relevant to conceptual modelling but very littleresearch was dedicated to providing specific methods and procedures in this critical stage of asimulation project.Frameworks could be identified for simulation modelling in general (e.g. Shannon, 1975; Law andMcComas, 1991; Musselman, 1994; Banks, 1999), which all recognise the conceptual modellingstage. The detail of how to create a conceptual model is an area underdeveloped except, to anextent, in Robinson (2004a; 2004b) general framework. However, Robinson’s frameworkpresented a number of weaknesses, including: that the framework did not address ‘how’ to createa conceptual model in detail; did not address the specific needs of the SCM domain and had notundergone any extensive testing or even evaluation. Interestingly, Robinson recognised the needto review methods for simplification, conceptual model validation, and ways to represent andcommunicate a conceptual model, although they were not incorporated into the framework.The methodology developed in this thesis has integrated, where possible, the guiding principles,methods of simplification, and ways for representing and documenting the content of aconceptual model. In addition to this, the discussion of conceptual model validation is arguably ofgreat importance and interest to practice. In relation to this, a ‘hypothesis test’ and ‘expertreview’ were found to be applicable to the conceptual modelling stage. Additionally, themethodology can incorporate principles and methods that can improve the validity and credibility 217
    • of the model into the procedure. Therefore, the methodology is detailed and comprehensive, andhas been developed specifically in the context of SCM applications.The problems encountered in simulation modelling are examined demonstrating the benefits andneed for a greater understanding of conceptual modelling. The majority of work has centered ona critical aspect of conceptual modelling: determining the most appropriate model complexity andlevel of detail (e.g. Brooks and Tobias, 1996; Chwif et al., 2000). Additionally, a mechanism isdiscussed for communicating and representing a conceptual model so that it can be sharedamongst the project team and fundamentally used to develop a computer model. A range ofapproaches were identified for representing a conceptual model, which could be used in theprocess of conceptual modelling.9.1.2.2 Specification for simulation conceptual modelling in SCMAnother secondary advance includes a specification for simulation conceptual modelling in SCM.This details the requirements for an effective conceptual model, requirements of goodmethodologies, and the specific requirements for conceptual modelling in SCM.The requirements for conceptual models (e.g. WIllemain, 1994; Brooks and Tobias, 1996;Robinson, 2004a; 2004b) and characteristics of methodologies (e.g. Platts, 1994; Platts and Tan,1996a) have been documented in previous research. These are both reviewed in light of theapplicability to conceptual modelling and the evaluation of SCM problems. The aim was to drawupon recent SCM examples and to reinforce a set of requirements that would guide the design forthe methodology. It was identified that the methodology should address the fundamental needto ‘keep the model as simple as possible’ and to create a conceptual model that is both ‘valid’ and‘credible’. This focuses upon the ‘probable’ accuracy of the conceptual model from both themodeller and client’s perspective. In relation to the characteristics of a good methodology, thesewere found to include ‘participation’, ‘points of entry’ and a ‘procedure’.One area that is novel includes identifying the requirements of evaluating supply chain problems.It was argued, in section 5.3, that the methodology should capture the complexity and detail of asupply chain problem for a given objective within its supply setting. Therefore, the implicationsfor this were to define what is meant by complexity and detail in the context of SCM, the range ofobjectives to measure performance and how the interconnections between them and the supplysetting can be determined. A detailed review provided a synthesis of the characteristics for eachof these needs. This included: 218
    • Complexity of a supply problem – size, business process types and linkages between actors and business processes Detail of a supply problem – level 0: supply network composition (supply chain structure); level 1: business process types (roles an actor plays in the supply chain); level 2: process categories (describes an organisation operations strategy); level 3: process elements (fine tuning of the operations strategy) and level 4 and beyond: implementation level (activities and actions required to implement SCM practices that are unique to the organisation) Range of supply chain objectives – level of detail within the business process, business unit and across the whole supply chain Interconnections within the supply setting – identification of the critical and necessary links between the improvement and objectives9.2 Conclusions from the research objectives and questionsThe overall aim and purpose of this thesis was to develop and preliminarily validate amethodology to aid in the creation of a conceptual model for supply chain problems. Therequired specification for the methodology was documented (objective one); a SCM2 developedand refined to meet the required specification (objective two) and preliminarily validated todemonstrate the SCM2 is initially feasible and has utility (objective three). These researchobjectives were addressed in different parts of the thesis (shown in table 1.1) and implementedby the stages distinguished in the methodological programme (shown in figure 1.1). This sectionconsiders each research objective and underlying research question to demonstrate that theobjectives set out in this thesis have been achieved.9.2.1 Objective one: Documentation of required specificationThe first objective concerns the documentation of the required specification for the SCM2. Thisobjective was addressed by answering two research questions: How are simulation conceptual models created in the context of supply chain applications? What is the specification of a simulation conceptual modelling methodology for evaluating supply chain problems?A review of existing conceptual modelling practice in the context of SCM addressed question one(detailed in the first stage of the methodological programme, chapter four). As reported insection 9.1 the chapter identified the scope of research contributions that could be evaluated inthe context of SCM. The majority of the literature identified was written in general for the entire 219
    • simulation project. The contributions had to be evaluated to identify the applicability of theprinciples, advice/methods of simplification; representation and documentation methods andhow a conceptual model can be validated for the conceptual modelling stage. The latter provedto be demanding as little discussion considered validation at the conceptual modelling stage. Thiswas surprising as ‘model validation’ is extensively discussed and methods are well known andwidely used. This is compounded by many publications citing the importance of validating aconceptual model but has not been addressed in any detail. It could be argued that this could bea secondary contribution in its own right. However, it is suggested in this thesis that this is an areathat should receive substantial attention by researchers in future work.The required specification for a SCM2 was documented to address the second research question(detailed in the second stage of the methodological programme, chapter five). The outcome ofthis discussion has been summarised in section 9.1.4.2. The objective was achieved byconsidering firstly the existing discussions in the conceptual modelling literature, which lackedbreadth and depth. The wider literature on simulation modelling proved more fruitful and couldbe evaluated to identify what was applicable for the conceptual modelling stage. Thesediscussions were novel and useful when designing the SCM2. In order to satisfy the need toidentify the requirements for developing a good methodology the wider OM and SCM literatureneeded to be reviewed. The rationale for this was that no methodologies exist for conceptualmodelling and, even when looking at frameworks, they have been developed from the modeller’sexperience with no extensive testing or initial evaluation. The final requirement included theidentification of the set of requirements for conceptual modelling in a SCM context. This wasnoted as an original area due to a lack of discussion in the literature. This is classified under theheading of identifying the range of supply chain improvements, supply chain performance withinthe setting of a supply problem. This was achieved by reviewing the SCOR model to extract thenecessary information for two typical supply problems synthesised from five publications that hadevaluated supply chain problems.9.2.2 Objective two: Development of SCM2 addressing the specificationThe second objective concerns the development of the methodology to create simulationconceptual models for supply chain applications. This objective is realised in chapter six (stage III:Outline design for the SCM2) and chapter seven (Stage IV: detailed design for SCM2) and evaluatedagainst the required specification presented in chapter five. The question that is answered in thisthesis to achieve objective two asks: Can a simulation conceptual modelling methodology be developed to meet the required specification? 220
    • Firstly, stage three of the methodological programme provides an outline design for the SCM2.The design of the methodology is underpinned by the conceptual base established by objectiveone discussed in the previous section (addressed in stage I: Review of existing conceptualmodelling practice in SCM, chapter four, and Stage II: Required specification for a SCM2, chapterfive). The design issues for each of the requirements identified in chapter five are considered indetail to identify the key concepts to be incorporated into the SCM2. At the core of the discussionis how domain knowledge can be utilised to enable a more efficient and focused methodology. Itis argued that SCOR presents a number of benefits to extract information and use standardterminology for describing a supply problem. The key concepts are aligned to the general stagesof a conceptual modelling project in order to identify an outline process specific to the needs ofsupply chain applications.Secondly, the outline design is further detailed and refined in stage four of the researchprogramme. This is achieved by applying the outline design presented in chapter six to twotypical and representative SCM applications. The aim was to incorporate the key concepts intothe phases specified for the purposes of conceptual modelling in the context of supply chainproblems. These are justified from a discussion of how each step and key concept can be appliedto the two case applications; observations are detailed in appendix A. The proposed methodologyis evaluated against the required specification to show that the requirements had been metbefore proceeding to the preliminary validation in chapter eight.9.2.3 Objective three: Preliminary validation of the SCM2The final objective of this thesis preliminarily validates the initial ‘feasibility’ and ‘utility’ of thedetailed and refined methodology. These two criterions were identified in section 3.2.6 andexpanded upon in section 8.3 to establish a set of sub-criteria that could be used to evaluate theSCM2. The objective is realised in chapter 8, corresponding to the final stage of themethodological programme addressed in this thesis (stage five: Preliminary validation of theSCM2). The question posed to address objective three was: Can the methodology be followed (feasibility) and aid a user (utility) to create a simulation conceptual model for a SCM application?The evaluation of the feasibility showed that the methodology could be followed to describe theactual practices to be included in the model. It was found that there was sufficient informationrequired and provided by the steps to complete them successfully. Table 8.8 showed that theinformation required was the clarity of terms and means of communication; describing the 221
    • objectives; describing the improvements; describing the inputs and interconnections in themodel; deciding what to include in the model and to identify and describe actual practice.The evaluation of the methodology utility demonstrated that the methodology aids a user todescribe useful and relevant set of actual practices to be included in the computer model. Theoutcome from the methodology was applicable for the purpose at hand – it satisfied therequirement to describe the actual practices to be represented in the model. A comparisonbetween the outcome from the methodology and the actual computer model (how the actualpractices to be represented by the model components and their relationships) presented in Tayloret al., (2008) showed there were no significant omissions. However, there were some differencesin how the actual practices were implemented. To an extent, this can be expected as the actualpractices are represented by the model components and their relationships from the perspectiveof the modeller. The significance of this is that the methodology was more comprehensive andrigorous in its approach. It facilitated the decisions made on the model content, documentationand validation of the conceptual model. It can be suggested that the decisions made regardingthe ‘shipping delay’ in the Taylor et al., (2008) warrant further explanation and consideration ofthe impact of delivery practices on the model (e.g. sensitivity analysis). A number of issues werealso identified corresponding to the facilitation of the methodology and were addressed in thediscussion of opportunities to improve the SCM2 (C.f. section 8.7).9.3 Limitations of studyThere are a number of limitations of this study that were outlined and evaluated to determine anyareas that require further work. The main limitations include the tautological criticisms that arepresented by the nature of the research programme. This includes that the SCM2 has only beenrefined and initially validated in three case study applications, using secondary data and appliedby the actual researcher. A review of existing methodological approaches and key issues in thedevelopment and validation of a methodology suggested this is favourable in comparison to othersimilar published work. It is not possible to declare that the SCM2 is ‘generalisable’ although it canbe suggested that it does commend ‘literal replication’.This thesis claims that scientific knowledge has been developed on how to use domain-knowledgewith a procedure for simulation conceptual modeling for SCM applications. It cannot be claimedthat theory has been developed as this would involve further iterations through the normal cycleof research as described by Meredith (1993). This includes continual iteration from description,to explanation, to testing. The research programme was justified to firstly develop a strong 222
    • conceptual base for an outline design for a SCM2 after an extensive literature review on existingpractice and identification of the SCM2 required specification. Secondly, develop and finallyvalidate the SCM2 using secondary data from three existing cases.In order to develop theory, Meredith (1993) suggests the SCM2 needs to be tested against realityuntil they are eventually developed into theories as research study builds upon research study.Each iteration through the research cycle adds confidence to previous findings or will force theresearcher to refine the methodology and thus develop more valid and complete theory(Meredith, 1993). This research has demonstrated that the SCM2 has initial feasibility and utilitybut the next stage requires rigorous testing. To remedy these limitations further work shouldinclude: Application of the methodology in different industrial contexts with primary data (discussed in section 9.3.1) Use actual participants to follow the methodology as it is prescribed (discussed in section 9.3.2). Further applications are required to build upon the initial validation of the feasibility and utility and to validate that the methodology is usable (discussed in section 9.3.3)Table 9.3 summarises the issues for future testing presented in section 8.6 for each criterion andneed to apply to different contexts and participants.Table 9.315 Summary of issues for future testing Need to use different Related test Issue for future testing Need to apply in different contexts facilitators and criteria participants Yes, does SCOR enable aHow different facilitators utilise the domain Feasibility - more efficient and focusedknowledge provided by SCOR process?Test how different participants are involved Yes, different participants Feasibility Yes, different contexts requiredin each step requiredTest how the information provided to draft a Yes, how differentconceptual model is used at the final Feasibility Yes, different contexts required participants make use ofvalidation phase informationRelevance of the methodology to create aconceptual model that is more valid and Utility Yes, can the procedure be repeated? -credibleThe usefulness of following a structured Yes, Is the output provided from the Yes, from a users Utilityapproach as opposed to no formal methods SCM2 more complete and accurate? perspectiveIdentify from the participants whether any Yes, what issue arises in different Yes, from a usersproblems arose or need to be addressed Utility contexts? perspectivewhen using the methodologyThe methodology is clear as it is well Yes, from a users Usability -structured and detailed perspectiveThe methodology should be used by both Yes, test with experts and Usability -experts and benefit novice users novice usersThe methodology is appropriate but can be Yes, identify opportunities to improveimproved to reduce time consuming Usability the facilitation of the methodology -activities (C.f. section 8.6.3) 223
    • 9.3.1 Application in different industrial contexts with primary dataThe SCM2 has been applied in three different supply chain applications representing typical typesof supply problems using existing cases (i.e. secondary data). These include a simplified beersupply chain of five actors in a chain used as a teaching and often as a research case, anindustrially rich and complex automotive seat supply chain and a dyadic coffeepot supply chainwith geographical constraints. Each of the cases offered a different set of circumstancesrepresenting different levels of complexity and detail.The actual SCM2 needs to be applied to a range of supply chain problems from differing industrialcontexts to be able to generalise and to claim its completeness. The research presented in thisthesis has relied upon secondary data and not original cases. Hence, there is a need to observeactual participants using primary data. The utilisation of the Supply Chain Council does improvethe relevance, correctness, economic efficiency and clarity of the domain knowledge used in theprocess of conceptual modelling. This is a separate issue but it has been argued in this thesis thatusing domain knowledge can potentially enable a more efficient and focused process. The SupplyChain Council covers five special interest groups: aerospace and defence, automotive, electronics,retail and consumer packaged goods and pharmaceuticals. Further work would need toconcentrate on the application of the actual methodology in each of these application areas witha range of representative problems.9.3.2 Use of different facilitators (potential users) to follow the SCM2The methodology has been developed and validated by the researcher as the main facilitatorfollowing the steps in the methodology. It was argued that this is necessary to ensure consistencyin application and the primary focus should to be to demonstrate it has initial feasibility and hasutility. Questions regarding the reflexivity and bias of the researcher were discussed in section3.3.1; issues were minimised by using existing case data that has been collected from a range ofsources and methods. In addition to this, the output from the methodology was compared with apublished computer simulation model. Future work will require a number of potential users (e.g.expert modellers, novice modellers such as students) to be trained to use the SCM2. Theresearcher in this instance should observe and use feedback to further refine and validate themethodology.The output generated from the methodology is useful and relevant and the use of SCOR in theprocess of conceptual modelling has been shown to potentially enable a more efficient andfocused approach. The methodology at present requires ‘expert facilitation’ but its particularvalue will be in improving the teaching and practice of conceptual modelling with novice users. 224
    • Further work is required to identify opportunities to improve the facilitation of the methodologyand to observe how expert and novice users can benefit from the approach in comparison to notfollowing any structured methods and approaches at all.9.3.3 Validation of the usability of the SCM2Section 8.6.3 notes that the thesis does not claim that the SCM2 has been validated against the‘usability’ criteria. It was noted that this would involve observing actual participants to addressthe question of how easily the methodology as prescribed could be followed. Although, the threeapplications from the perspective of the facilitator identified three areas summarised in table 8.11that should be the primary focus for future tests. This includes the clarity and structure; how timecan be reduced in each of the stages and observations with both expert and novice users. To anextent, ‘usability’ is considered as a major area to improve the facilitation of the SCM 2. This canbe achieved by embedding the methodology in a web-based application coupled with the SCORdomain knowledge. This will aid in the facilitation of the analysis and in areas automate theprocess (e.g. identification of candidate process elements).9.4 Implication for further research and practiceThe primary and secondary contributions made in this thesis have implications for research andpractice within the context of conceptual modelling and in other related fields. Using Robinson’s(2006) discussion of issues in conceptual modelling, it can be demonstrated how this research hasmade significant and relevant advances in the field, and provides avenues for further work (shownin table 9.4).The research presented in this thesis has made a number of significant advances for each of theissues noted in table 9.4. It has been noted that there is little guidance on conceptual modelling.Reviewing the issues noted by Robinson (2006a; 2006b) indicates that this is a significant andcomprehensive study on conceptual modelling in general and specifically for the SCM domain.This also includes the applicability of general discussions in the modelling literature to theconceptual modelling stage. The thesis has contributed to advancing a definition for conceptualmodelling, in particular in the context of SCM (e.g. utilising domain knowledge). A methodologyhas been developed which is underpinned by existing practice including modelling principles,methods of simplification and a procedure to follow for conceptual modelling of SCMapplications. It also includes steps in the procedure to build valid and credible models and a finalvalidation stage. The output from the methodology delivers a conceptual model using suitablemethods for representation and communication. 225
    • Table 9.416 Revisiting Robinson (2006a; 2006b) issues in CM Issue in conceptual modelling Advance made in this Opportunity for further work (Robinson, 2006) thesis Synthesis and critique of existing contributions in the literature hasDeveloping consensus over the presented some clarity on what constitutes a conceptual model indefinition of a conceptual Yes, in SCM context particular for SCM applications. Opportunity exists to use themodel/conceptual modelling definitions provided for research, teaching and practice. First research to present the requirements for conceptual modelling for SCM applications. This may have implications for other domains. It was argued that domain knowledge is key to conceptual modellingIdentifying the requirements for a Yes, in SCM context and this will shape the process. There is an opportunity to identifyconceptual model common stages for conceptual modelling and to extend the methodology for other domains (e.g. manufacturing, military applications) Synthesis and critique of existing contributions may lead to consensusDevelopment of methods for of the different approaches to conceptual modelling. The keydesigning conceptual models Key concepts and concepts are unique and novel; they have presented newincluding modelling principles, process in SCM context opportunities to identify principles and methods applicable tomethods of simplification and conceptual modelling. These opportunities could be extended tomodelling frameworks other domains and in general. Methods have been considered in a SCM context, but more so. a standard method using a spreadsheet application has been proposedMoving towards standard Yes, spreadsheet which can be further enhanced into a web-based application usingmethods for representing and application proposed automation between steps. The intention is to further increase thecommunicating a conceptual (using automation) usability of the methodology. Further work should also concentratemodel on how process reference models can be used and enhanced for use in a conceptual modelling project. Synthesis and critique of existing contributions may lead to consensus Yes, embedded in in validating conceptual models. A ‘hypothesis test’ and ‘expertDeveloping procedures for SCM2. The discussions review’ has been argued as the only existing methods for validating avalidation of a conceptual model of applicable methods conceptual model. This has been noted as an area for considerable are made in general. scope and of importance to the simulation community. Development of a Yes, methodology is intended to aid novice and expert users in a SCMInvestigating effective means for methodology that context. The work provides a comprehensive study of existingteaching the art of conceptual synthesises existing practice that is applicable at the conceptual modelling stage. Themodelling approaches to ideas presented in this thesis can be used for teaching of conceptual conceptual modelling modellingTable 9.4 presents a number of opportunities to extend the SCM2 and provide avenues for furtherstudy. It is anticipated that extending this research will improve the accessibility of the SCM2 to awider audience, teaching and application of conceptual modelling in practice and improve therigour of simulation studies. The issues identified that concerned the limitations of SCOR haveimplications more generally for researchers and practitioners who wish to use SCOR forsimulation purposes. More generally, the implication for wider research, centres on definingwhat constitutes a conceptual model for different domains, process for creating a conceptualmodel and identifying existing and new principles and methods that are applicable at theconceptual modelling stage of a simulation project. Building upon Robinson (2006a; 2006b)issues, the opportunities for further research can be refined in the context of what has beendelivered by this thesis: Acceptance of what constitutes a conceptual model and their requirements – The work contributes to gaining a consensus on a definition for conceptual modelling. These have been proposed in general but the content of a model is specific to a particular domain. Future research should focus on the domain requirements for conceptual modelling. 226
    • Identify principles and methods specific to the conceptual modelling stage – The work suggests some key concepts for conceptual modelling for SCM applications. The literature has focused upon modelling principles and methods, but opportunities exist to identify those that are applicable to conceptual modelling and to develop new principles and methods for conceptual modelling. Advance further how domain knowledge can be used in the process of conceptual modelling – It was argued that domain knowledge is key to the process of conceptual modelling and that a process reference model is useful. Although, SCOR has been used widely for simulation, there are many opportunities to extend SCOR so that its utility for simulation purposes can be improved. This includes in the detailed descriptions offering advice for the potential modeller and identifying the interconnections between practices, processes and metrics between supply chain actors. Gain consensus on the purpose and methods for validating a conceptual model – This was noted of significance importance. Although this was embedded into the procedure, an opportunity exists to research more general validation methods and a procedure for conceptual modelling Identifying ways to extend and facilitate the methodology – Opportunities to improve the SCM2 were presented in section 8.7. These were identified as the role and impact of automating certain steps in the methodology, strengthening the utilisation of SCOR to provide domain knowledge for conceptual modelling, and developing a web-based tool that can facilitate the methodology.9.5 Chapter summaryThe primary contribution presented in this thesis is the ‘development, refinement and preliminaryvalidation of a methodology that utilises domain-knowledge combined with a procedure that canbe followed to create a simulation conceptual model for SCM applications’. The methodology atits heart incorporates into its design a set of ten key concepts that utilise domain-knowledge witha procedure for conceptual modelling of supply chain problems. The methodology has beenpreliminarily validated, and evaluated, to demonstrate that it is initially feasible and has utility.Other notable advances include an examination of existing simulation conceptual modellingpractice and a specification of the requirements for simulation conceptual modelling. Adiscussion of the research issues in conceptual modelling in SCM demonstrated that thesecontributions are both a significant and relevant area for research. 227
    • The primary contribution and other advances made by this thesis were arrived at by forming,outlining, detailing and preliminarily validating the SCM2. A five stage methodological programmewas adopted to address the research aims and explore each research question using an iterativetriangulation method. This included reviewing existing conceptual modelling practice to establishthe need for a SCM2 (stage I, chapter 4), form the specification for the SCM2 (stage II, chapter 5),outline design for the SCM2 (stage III, chapter 6), detailed refinement design of the SCM2 (stage IV,chapter 7) and a preliminary validation of the SCM2 (stage V, chapter 8).The limitations of this research have been identified, along with some future avenues andimplications for both research and practice. The primary limitation lies in the number ofrefinement and applications in different industrial contexts, involvement of different facilitatorsand participants and the need to evaluate the ‘usability’ of the SCM2. There is a need for rigoroustesting with a full range of original case-study applications to extend the initial feasibility andutility validation and evaluate the SCM2 usability’. This is important as the thesis has presentedthree development and validation applications, to demonstrate confidence in the evaluationcriteria but further applications are necessary to build eventual theory. This will involve actualusers (both expert and novice simulation users) to evaluate how the methodology is to be used inpractice, without bias from the researcher, and to obtain feedback to refine the SCM2 further.There are a number of implications to both extend the SCM2 (e.g. develop a web-basedapplication), and to provide further avenues of study (e.g. strengthen SCOR for the purpose ofconceptual modelling, teaching and practice of conceptual modelling). 228
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