The document summarizes research on developing an object-oriented simulation modeling environment to analyze supply chain performance and inventory management. The goals were to create a flexible modeling tool capable of stochastic modeling with user-defined rules, distributed simulation, and robust error handling. The research involved building supply chain simulation models, analyzing performance under varying demand patterns, and optimizing expected service quality, ordering policies, and information sharing levels.

1. Supply Chain Performance Improvement: The Role of IT Presented By: Bibhushan Entry No: 2002RME027 Supervisors: Prof. S. Wadhwa and Prof. Anoop Chawla

2. Presentation Outline Research Context and Motivation Research Objectives An Overview of the Research Work Significant Contributions of Research Work Publications Response to Examiner’s Comments

3. Research Context and Motivation Simulation for Supply Chain Modeling and Analysis Used for analysis of complex systems Type of problems modeled range from tactical to strategic Object-Oriented Simulation Modeling Detailed model of a complex system can be made by combining basic building blocks Has advantages of inheritance, encapsulation, modularity, etc. Multiple Entity Flow Perspective Five flows: Material, Information, Money, Resource, Decision Focus on Inventory Management to improve IT facilitated SC performance

4. Research Objectives Highlight the research motivation to Develop an object-oriented supply chain simulation-modeling environment Develop demonstrative models to illustrate the efficacy of the approach in SC performance Study the inventory management in supply chains working under stochastic demands

5. Research Objectives Develop an object-oriented supply chain modeling and simulation environment based on multiple-entity flow perspective which should be capable of: Modeling the flow of multiple entities Stochastic modeling Adding user-defined decision rules in addition to major control decision rules User-friendly and cost effective Robust modeling by means of effective error handling and fool-proofing in data input Distributed simulation

6. Research Objectives Analyze inventory management along multiple criteria (demand variance, inventory, service level etc.) Understand the effect of Expected Service Quality (ESQ) on different inventory policies Determine optimal ESQ for each node Determine optimal Information sharing level for the ESQ levels found above Understand the effect of ordering and capacity constraints on different inventory policies Determine Optimal Ordering and Capacity constraints for each node Determine the optimal information sharing level for ordering and capacity constraint levels determined above Determine the effect of change in Coefficient of Variance (COV) on each supply chain node Determine the optimal Information sharing level for different COV levels

7. Overview of Research Work Organization of Thesis Conceptual Framework Simulation Modeling Environment Performance of Supply Chain under Controlled Variability Optimizing ESQ for Supply Chain Nodes Optimizing Optimal Ordering and Capacity Constraint levels for Each Supply Chain Node Understand the Effect of Changing COV on supply chain

8. Organization of Thesis

9. Conceptual Framework A Generic Model of Supply Chain Object Oriented Modeling Perspective Modeling of Elementary Supply Chain Constructs Hierarchy of Object Used in Supply Chain Modeling Modeling Supply Chain Decisions

10. Simulation Modeling Environment Modeling the Supply Chain Building Blocks Modeling the manufacturing system Modeling the transports Modeling the Player Role Modeling the Supply Chain Node Modeling the Inter-Node Interactions Defining Inter-Node Relationships Defining Inter-Node Lead Times Defining Inter-Node Speeds Defining Inter-Node Distances Defining Product Demands

11. Simulation Modeling Environment Modeling Supply Chain Decisions Source Selection Policies Inventory Control Decisions Transportation Decisions Production Planning Decisions Performance Metrics Inventory Related Demand Related Service Related Supply Chain Model for Research Model Verification and Validation

12. Performance of Supply Chain under Controlled Variability Experimental Setup Demand impulses Simulation parameters Balancing the inventory policies Performance metrics considered Effect of Transformed Relative Impulse Amplitude (TRIA) on the Supply Chain Effect of TRIA on the Supply Chain using Demand Flow Policy (DFP) Effect of TRIA on the Supply Chain using Order Q Policy (OQP) Effect of TRIA on the Supply Chain using (s, S) Policy (sSP) Effect of TRIA on the Supply Chain using (s, Q) Policy (sQP)

13. Performance of Supply Chain under Controlled Variability Effect of Balance Gap (BG) on the Supply Chain Effect of BG on the Supply Chain using DFP Effect of Negative Impulse BG (NIBG) Effect of Positive Impulse BG (PIBG) Effect of BG on the Supply Chain using OQP Effect of NIBG Effect of PIBG Effect of BG on the Supply Chain using sSP Effect of NIBG Effect of PIBG Effect of BG on the Supply Chain using sQP Effect of NIBG Effect of PIBG

14. Performance of Supply Chain under Controlled Variability Effect of Number of Impulses (NI) on the Supply Chain Effect of NI on the Supply Chain using DFP Effect of Number of Negative Impulses (NNI) Effect of Number of Positive Impulses (NPI) Effect of NI on the Supply Chain using DFP Effect of NNI Effect of NPI Effect of NI on the Supply Chain using DFP Effect of NNI Effect of NPI Effect of NI on the Supply Chain using DFP Effect of NNI Effect of NPI

15. Performance of Supply Chain under Controlled Variability Effect of Impulse Width (IW) on the Supply Chain Effect of IW on the Supply Chain using DFP Effect of Negative Impulse Width (NIW) Effect of Positive Impulse Width (PIW) Effect of IW on the Supply Chain using DFP Effect of NIW Effect of PIW Effect of IW on the Supply Chain using DFP Effect of NIW Effect of PIW Effect of IW on the Supply Chain using DFP Effect of NIW Effect of PIW

17. Supply Chain Processes Plan Balances aggregate demand and supply Source Procures goods and services to meet planned or actual demand Make Transforms product to a finished state Deliver Provides finished goods and services Return Post-delivery customer support

18. A Generic Model of Supply Chain

19. Supply Chain Flows Primary Flows (Between Nodes) Material Flow Information Flow Cash Flow Secondary Flows (Only inside Node) Resource Flow Decision Flow

20. Object Oriented Supply Chain Simulation Simulation is a technique where computers imitate the operations of various kinds of real-world facilities or processes (Law and Kelton 1991) Discrete-event simulation Object oriented modelling OOPs based simulator for modeling flexible supply chains

21. Need for Object Oriented Supply Chain Simulation Supply chain flexibility offers many challenges and opportunities It offers decision choices as the system evolves which is dynamic in nature There is a need for developing a modeling environment to deal with flexibility and dynamic decision making A OOPs based simulation system is developed and explored for its efficacy in this research

22. Advantages of Object Oriented Modeling Inheritance A class of objects can itself be linked to one or several super-classes from which it acquires characteristics and behavior Encapsulation Describes its characteristics along with its relationships to other components and the functionality of the object Allows structured development of the model Hides unimportant details Modularity Provides a very high degree of code reusability

23. Advantages of Object Oriented Modeling Allows the model builder to develop the models with much less effort Suitable for modeling distributed systems having client-server architecture Plug-and-play software capability Interoperability across the network Platform independence Allows complex systems to be constructed with minimum of redundant work

24. Advantages of Object Oriented Modeling A logical choice for developing custom or dedicated simulation models Sub-components may be prefabricated by some expert group for a specific need or application Productivity of software development improves if code is reused, since the specific modules are already extensively tested by their developers

25. Advantages of Object Oriented Modeling Provides a natural mapping paradigm which allows one-to-one mapping between objects in the system being modeled and their abstractions in the object model Allows the developer to achieve a faster transition of the conceptual model into the software implementation Object-oriented models generally have a cleaner structure than the event oriented ones

26. Overall Architecture Basic building blocks are used to create some lower level complex objects Lower level objects are then used to define the higher level objects Level 1 objects are made up of basic building blocks Basic building blocks are combined with the object(s) of level 1 to form level 2 objects

27. Object Oriented Modeling of Supply Chains Supply chain decision making requires rapid and flexible modeling approach at various levels of detail Object oriented modeling can be used for Designing and implementing reusable classes for building models of supply chains Creating a supply chain object library Facilitates rapid model development Aid in application of the modeling architecture to specific scenarios at various levels of abstraction

28. Object Oriented Features in Arena Simulation Environment Offers model development in object oriented manner by means of objects called “modules” Modules are essentially composed of other basic level modules Once properly developed, these modules can be reused in other simulation models However…

29. Limitations of Object Oriented Features in Arena Modules can be run only on systems having ARENA Version Conflicts Not suitable for distributed computing Cost of buying this simulation package Additional cost of buying the customized module libraries What is the solution then?

30. Generic Programming Languages Not as easy as developing models using simulation packages However, It is more general and the SC flexibility related issues can be modeled in detail. Availability of customized object libraries for a variety of applications can significantly reduce the time and effort involved in model building process It offers platform independence to a large extent

31. IT tool used: VB.Net Ease of designing the user interface Now fully object-oriented Provides a very high degree of platform independence only for Windows based platforms however Supply chain flexibility and dynamic decision making can be developed as a customized option.

32. Research Gaps Need to develop simulation tools ideally suited for flexible supply chain simulation Effective modeling of Supply Chain Flexibility Web-based simulation environment Demonstrate benefits of collaborative decision making Non-deterministic and dynamic modeling Analyzing the impact of different control decisions in an integrated manner Distributed computing needs to be explored

33. Research Gaps Need to study the impact of information sharing under different IT options Supply Chain performance under different levels of Demand History, Service Level, Demand Variance needs to be studied There is need for demonstrative models to illustrate the benefits of IT tools focused on modeling of the flexible supply chains.

34. Overview of the Research Work Development of the IT tools for modeling Flexibility and Dynamic decision making Manufacturing systems and supply chains were modelled in terms of five types of flows: information flow, decision flow, material flow, resource flow and money flow Extended the Multiple Entity flow perspective proposed by Wadhwa & Rao (2003) Development of demonstrative simulation models for illustrating supply chain performance improvement by the use of IT

35. Overview of the Research Work Supply Chain performance improvement under flexibility and dynamic decision making. Focus on inventory management. Comparison of Inventory Control Policies under Deterministic Variability Effect of Demand History on Supply Chain Performance Effect of Service Level on Supply Chain Performance Effect of Demand Variance on Supply Chain Performance

36. Supply Chain Management Defined SCM is “the integration of business processes from end-user through original suppliers that provides products, services, and information that add value for customers” (Lambert et. al. (1998)

37. Modeling Elementary Supply Chain Constructs Classification of Objects Multiple Entity Flow Perspective Action Points as Processes in the System

38. Classification of Objects

39. Multiple Entity Flow Perspective

40. Action Points as Processes in the System

41. Hierarchy of Object Used in Supply Chain Modeling Modeling of a Supply Chain Network Modeling of Supply Chain Nodes Modeling of Supply Chain Operations Modeling the Manufacturing System

42. Levels of Abstraction for Supply Chain Modeling

43. Modeling of a Supply Chain Network As a collection of supply chain nodes Each node is a fully autonomous unit Define relationships between each pair of nodes Two types of relationships Buyers (can select Sellers) Sellers (can only be selected) Constrained relationships By the level of respective nodes

44. Integration of Supply Chain Nodes

45. Multiple Supply Chains in a Collection of Supply Chain Nodes

46. Modeling of Supply Chain Nodes Two kinds of Nodes: Manufacturing (Value-adding) Non-Manufacturing ( store the material and supply it to other nodes ) Flows through each node: Material flow Information flow Money flow Flows Inside node Resource Flow Decision Flow

47. Modeling of Supply Chain Nodes Five Processes Plan, Source, Make, Deliver and Return Return Out of scope of this work Store Additional Process

48. A Manufacturing Node

49. A Non-manufacturing Node

50. Integration of Major Supply Chain Operations

51. Make Manufacturing operations Product quantity is decided by planning Produces the goods according to the control policies determined by production planning Routing Scheduling

52. Source Decides the sellers from whom to procure necessary goods A sourcing policy is a decision rule that determines the best seller(s) out of a number of available sellers in accordance with some predefined criterion e.g. Maximum Inventory, Minimum Lead Time etc.

53. Deliver (Transportation) Out of a number of transports one or more transports are selected based on some pre-defined transportation policy like Maximum Speed Minimum Cost Maximum Capacity

54. Inventory Management Concerned with maintenance of sufficient amount of inventory to fulfil demands Whenever the inventory of any item falls below the critical levels, the inventory management sends the order(s) to procure the required material or product to planning operation Planning subsequently decides either to make or buy the required product

55. Modeling the Manufacturing System Can be modeled by combining two basic building blocks Materials (Transformed or Consumed) Resources (Negligible Transformation/Consumption) One or more resource is used to perform some operation on the material (called process ) Each product requires some processes to be performed in a specific sequence Two decisions are taken before each process Resource selection Material selection

56. Typical Material Processing in a Manufacturing System

57. Modeling Supply Chain Decisions Source Selection Policies Inventory Control Decisions Transportation Decisions Production Planning Decisions

58. Source Selection Policies Classification (based on no. of sources) Single Source Multiple Source Transport Based. Source Selection Rules Shortest distance Minimum cost Maximum inventory Preference selection Probability based selection (only for multiple source policies) User defined selection (only for single source policies)

59. Source Selection Policies

60. Probability Based Source Selection

61. Multiple Source Selection

62. Inventory Control Decisions Demand Flow Order Q Order Upto (s, Q) Policy (s, S) Policy Updated (s, S) Policy Days of Supply, Demand Based (DOS Demand) Days of Supply, Forecast Based ( DOS Forecast)

63. Inventory Control Decisions

64. Updated (s, S) Policy

65. Updated (s, S) Policy Reorder Level ( s ) is calculated as Where LT is the lead time of the selected source σ is the estimate of standard deviation of the demand in previous n periods Z is the standard normal variate corresponding to the desired service level Special Cases n -period moving average ( Z = 0 ) Demand Flow ( n = 1, Z = 0 )

66. DOS Demand

67. DOS Forecast

68. Transportation Decisions Each transport uses some transportation mode e.g. rail , road , air , or water Depending on the location, not all transports may be possible for a node pair Alternative transports are selected based on which transport modes are available between a node pair

69. Transportation Decisions Alternative transports differ according to their specific characteristics Each transport has some properties like capacity speed, cost, etc Transport selection rules Maximum speed Maximum volume capacity Maximum weight capacity Minimum cost User defined transport selection

70. Transportation Selection Policies

71. More Classifications in Transports The loading in the selected transport may again be of two types Pooled (all the products shipped between a node-pair are sent through the same transport) Non-pooled (different products are shipped through different transports) Based on capacity utilization of transport FTL (Full Truck Load) LTL (Less than Truck Load)

72. Transport selection with pooled transports

73. Transport selection with non pooled transports

74. Production Planning Decisions Routing Concerned with selection of best possible resources out of a number of available resources Scheduling Decides the timing of each process or each job in the manufacturing system Quantity to be produced is determined by the inventory policy

75. Production Planning Decisions Options Available Produce as directed by inventory policy Produce short batches Once the product is routed to a resource, it is added to the queue of the corresponding resource Routing policy is used to select the next resource for the next process when current processing is over Sequence of resource selection and allocation continues until all the processes on the job are completed

76. Production Planning Operation

77. Determining the Make Quantity

78. Resource Selection Policies

79. Modeling the Supply Chain Building Blocks Consists of Modeling the Manufacturing System Modeling the Transports Modeling the Player Role Modeling the Supply Chain Node Modeling Node Interactions Different objects in the SC Network are linked with each other, they can be represented using the concepts of Relational Database Management System (RDBMS)

80. Modeling the Manufacturing System

81. Modeling the Transports

82. Modeling the Player Role

83. Modeling the Supply Chain Node

84. Performance Metrics Inventory related Minimum Inventory Maximum Inventory Total Inventory Average Inventory Standard Deviation of Inventory

85. Performance Metrics Used Service related Backorders Stockouts Fill Rate Service Level Demand related Minimum Demand Maximum Demand Total Demand Average Demand Standard Deviation of Demand

86. Supply Chain Performance Under Deterministic Variability Experimental Setup Demand Impulses Simulation Parameters Balancing the Inventory Policies Effect of Impulse Amplitude Effect of Impulse Width Effect of Step Width Effect of Number of Impulses

87. Demand Impulses

88. Simulation Parameters Variable 1 1 1 Impulse Width 1 Variable 1 1 Number of Impulses 0 0 Variable 0 Balance Gap 0.9 and 1.9 0.9 and 1.9 0.9 and 1.9 Variable Impulse Amplitude 100 100 100 100 Mean Demand 2 2 2 2 Transportation Lead Time (Days) 2 2 2 2 Information Lead Time (Days) 90 90 90 90 Observation Period (Days) 20 20 20 20 Warmup Period (Days) 110 110 110 110 Run Length (Days) Step Width Number of Impulses Balance Gap Demand Amplitude Parameter

89. Balancing the Policies Each policy was balanced so that all of them gave same results for the test demand under steady state condition Demand Flow: The test demand was a constant demand of 100 units per week. To fulfill the current obligations, each node has to keep a minimum of 100 units. Each node has to keep an initial inventory equal to four weeks of demand. As a result, an initial inventory of 400 units was allocated to each node. Order Q: In this policy, orders are placed even when no there is no demand. Therefore, inventory builds up for each node, until the actual demand is received. As a result, all nodes only need to keep an inventory equal to the value of demand per week (100 units).

90. Balancing the Policies (s, Q) Policy: The initial inventories for each node were same as those for demand flow policy. A reorder point ( s ) of 400 and order quantity ( Q ) of 100 was set for this policy. (s, S) Policy: Initial inventories were kept same as the demand flow policy. Both reorder point ( s ) and reorder level ( S ) were set to be 100 units.

91. Effect of Impulse Amplitude Effect on Individual Supply Chain Nodes Effect on Retailer Effect on Wholesaler Effect on Distributor Effect on Manufacturer Effect of each policy on the Supply Chain Effect along the Supply Chain

92. Effect of Amplitude on Individual Supply Chain Nodes Performance Metrics Used Total Inventory Std. Dev. of Inventory Backorders Stockouts Std. Dev. of Demands

93. Retailer’s Total Inventory

94. Retailer’s Std. Dev. of Inventory

95. Retailer’s Backorders

96. Retailer’s Stockouts

97. Wholesaler’s Total Inventory

98. Wholesaler’s Std. Dev. of Inventory

99. Wholesaler’s Backorders

100. Wholesaler’s Stockouts

101. Wholesaler’s Std. Dev. of Demand

102. Distributor’s Total Inventory

103. Distributor’s Std. Dev. of Inventory

104. Distributor’s Backorders

105. Distributor’s Stockouts

106. Distributor’s Std. Dev. of Demand

107. Manufacturer’s Total Inventory

108. Manufacturer’s Std. Dev. of Inventory

109. Manufacturer’s Backorders

110. Manufacturer’s Stockouts

111. Manufacturer’s Std. Dev. of Demand

112. Effect of Amplitude on Supply Chain as a Whole Demand Flow Policy Order Q Policy (s, S) Policy (s, Q) Policy

113. Demand Flow Policy

114. Total Inventory

115. Std. Dev. of Inventory

116. Backorders

117. Stockouts

118. Std. Dev. of Demand

119. Total Inventory

120. Std. Dev. of Inventory

121. Backorders

122. Stockouts

123. Std. Dev. of Demand

124. Order Q Policy

125. Total Inventory

126. Std. Dev. of Inventory

127. Backorders

128. Stockouts

129. Std. Dev. of Demand

130. (s, S) Policy

131. Total Inventory

132. Std. Dev. of Inventory

133. Backorders

134. Stockouts

135. Std. Dev. of Demand

136. (s, Q) Policy

137. Total Inventory

138. Std. Dev. of Inventory

139. Backorders

140. Stockouts

141. Std. Dev. of Demand

142. Effect of Amplitude on the Supply Chain as a Whole Type of Impulse Positive Impulse (0.9) Negative Impulse (-0.9) Performance Metrics Used Total Inventory Std. Dev. of Inventory Backorders Stockouts Std. Dev. of Demands

143. Negative Impulse Total Inventory

144. Std. Dev. of Inventory

145. Backorders

146. Stockouts

147. Std. Dev. of Demand

148. Positive Impulse Total Inventory

149. Std. Dev. of Inventory

150. Backorders

151. Stockouts

152. Std. Dev. of Demand

153. Effect of Demand History on Supply Chain Performance Experimental Setup No Information Sharing Effect on Individual Nodes Effect on Whole Supply Chain and Effect along the Supply Chain Partial Information Sharing Full Information Sharing Comparison of Information Sharing Levels

154. No Information Sharing

155. Effect on Individual Supply Chain Nodes Retailer Wholesaler Distributor Manufacturer

156. Retailer’s Total Inventory

157. Retailer’s Maximum Inventory

158. Retailer’s Std. Dev. of Inventory

159. Retailer’s Backorders

160. Retailer’s Stockouts

161. Wholesaler’s Total Inventory

162. Wholesaler’s Std. Dev. of Inventory

163. Wholesaler’s Backorders

164. Wholesaler’s Stockouts

165. Wholesaler’s Std. Dev. of Demand

166. Distributor’s Total Inventory

167. Distributor’s Std. Dev. of Inventory

168. Distributor’s Backorders

169. Distributor’s Stockouts

170. Distributor’s Std. Dev. of Demand

171. Manufacturer’s Total Inventory

172. Manufacturer’s Std. Dev. of Inventory

173. Manufacturer’s Backorders

174. Manufacturer’s Stockouts

175. Manufacturer’s Std. Dev. of Demand

176. Effect on Supply Chain and Effect Along the Supply Chain

177. Total Inventory

178. Std. Dev. of Inventory

179. Backorders

180. Stockouts

181. Std. Dev. of Demand

182. Organization of Thesis

183. Organization of Thesis

184. Organization of Thesis

185. Organization of Thesis

186. Organization of Thesis

187. Significant Contributions of the Research Work Development of an object-oriented supply chain simulation environment Role of IT based tools are developed and used to study IT facilitated information and decision flows in flexible supply chains is studied Development of a framework that incorporates different IT facilitated control policies in SCs Comparison for inventory policies under deterministic variability and information sharing Analysis of Supply chain performance under different levels of demand information (IT focus)

188. Significant Contributions of the Research Work Analysis of Supply chain performance under different of Service levels Analysis of Supply chain performance under different levels of demand variance Analysis of supply chain performance under different level of information sharing with Different levels of demand history Different service levels Different demand variances

189. Limitations and Scope for Future Research The modeling environment can be extended in more directions like Closed loop supply chains by adding return process Manufacturing operations can be extended to include different kinds of production facilities …. Focus on Inventory management only…

190. List of Publications Published/Accepted for Publication Postponement strategies for re-engineering of automotive manufacturing: knowledge-management implications , International Journal of Advanced Manufacturing Technology, Article in Press, doi 10.1007/s00170-006-0679-z. Hybrid Tabu-Sample Sort Simulated Annealing (SSA) with Fuzzy Logic Controller: CIM System Context , Studies in Informatics and Control, June 2006, Volume 15, Number 2. Flexible Supply Chains: A Context for Decision Knowledge Sharing and Decision Delays , Global Journal of Flexible Systems Management, Volume 7 Numbers 3 & 4, July -Dec 2006 (Accepted for Publication). Impact of Supply Chain Collaboration on Customer Service Level and Working Capital , Global Journal of Flexible Systems Management (Accepted for Publication).

191. List of Publications Under Review A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach , International Journal of Computers Communication and Control. Inventory performance of some supply chain inventory policies under impulse demands , International Journal of Production Research, Manuscript ID: TPRS-2007-IJPR-0111. Communicated An Object Oriented Framework for Modeling Control Policies in a Supply Chain , International Journal of Value Chain Management

192. List of Publications National / International Conferences Web Based Virtual Supply Chain Modeling to Enhance Learning , The International Conference on e-Learning (ICEL 2006), University of Quebec in Montreal, Canada, June 22-23. Supply Chain Modeling: The agent based Approach , 12th IFAC Symposium on Information Control Problems in Manufacturing (INCOM-2006), Saint-Etienne, France, May 17-19. Object-Oriented Approach for Simulation of Supply Chain , International Congress on Logistics and SCM Systems (ICLS-2006), Kaohsiung, Taiwan, May 1-2. Comparison of some Supply Chain Management Software Applications , National Conference on Advances in Mechanical Engineering (AIME-2006), January 20-21.

193. Response to Examiner’s Comments

194. Comments of Examiner 1 No Information Sharing: Is it best for individual wholesalers and retailers? As we move higher in the supply chain, the demand variability increases because of the inventory policy used It is not that no information sharing is good for individual wholesalers and retailers, information sharing is just less important for them. Full Information Sharing: Is it best for the overall system? Whether full information sharing is best for the system or not is dependent on the inventory policy used The thesis aims to demonstrate that after some particular level of information sharing, the investment in IT may not be economically justified

195. Comments of Examiner 1 If answers to (1) and (2) above is yes, then explain why optimization on IS level (information sharing) is necessary? Why would an intermediate value (of IS) would be optimal? Whose objective have you considered? Individual wholesalers/retailers or the whole system? Or a combination of the two? It is important to find the level and type of information sharing On page xxvii: IT should be information technology The required change has been made. Uncertainty in supply chain is demand side and the lead time size. When you consider disturbances: you could have considered lead time disturbances. We consider this as a future area of research

196. Comments of Examiner 1 Advanced IT means: continuous review policies (for inventory). What implications does it have for your thesis? In a continuous review policy, the inventory position is continuously monitored Review period is one day; all the policies in our research are the continuous review policies For single node (such as wholesaler or retailer): given demand and lead time uncertainty: optimal policy for lot sizing can be devised. Then it could be used in your simulation. Decentralized decision making is found to deteriorate the supply chain performance The decisions of one node may indirectly affect the performance of other (interaction effects)

197. Comments of Examiner 1 A schematic diagram of supply chain (number of plants), distributor (numbers) and the wholesaler/retailers (numbers) considered in the thesis can be given. A schematic diagram of the supply chain considered in this thesis is given on page 136. Description of the same is given in section 4.5 Main focus of thesis is determination of optimal levels of controllable factors such as … modifying the thesis title. Motivation of this research is to bring the information technology (IT) as a performance improvement solution in the supply chain Information sharing and IT are mutually complimentary The research highlights where and how much information needs to be shared for the optimal performance Factors beyond the control of decision makers (uncertainty) and factors under decision-makers’ control … readability of the thesis. The required tables have been added in the Appendix A

198. Comments of Examiner 2 The current developed framework is limited to only two players, i.e. manufacturing and inventory … more than three players? There are four players in the supply chain considered in this research: Retailer , Wholesaler , Distributor and Manufacturer In addition to the inbuilt player roles like supplier, manufacturer, distributor, wholesaler and retailer, users can also define their own Player Roles . Network manufacturing is a new arena for modern manufacturing environment. How could … contribution in this field? For network manufacturing also, this framework can still handle the execution side In network manufacturing, the manufacturing of the finished product takes place through a coordination of multiple autonomous players. Such a network will have most of the players as manufacturing type players. This framework can be used where higher level modeling of the manufacturing system is sufficient

199. Comments of Examiner 2 In this research, “overall supply chain cost” has been used as the major criterion for the supply chain performance. In fact, there are many Key Performance Indicators (KPI) reported in the supply chain management research work, such as agilability, lead time, flexibility, expandability, trust, etc. How could you consider these issues into your research framework? The research framework, in its present form, has only the KPIs which were required for this research work, i.e. those related to inventory management Since the framework is based on object oriented methodology, multiple KPI libraries can be added to it as and when need arises What are the major bottlenecks in the implementation of the developed framework in real-life industrial case? The framework has been developed considering a very generic nature of the supply chain

200. Comments of Examiner 2 What are the major limitations of the developed framework in this thesis? The return operation of a supply chain is not available in the framework. In the future, some other major supply chain operations may be added in the framework. The effectiveness of the simulation environment can be immensely improved by incorporating some optimization algorithms for simpler supply chain decisions and some meta-heuristics for complex problems. Another important direction for future work is to provide animated simulation similar to that available in other simulation languages.

201. Thank You

202. Questions