Estado Del Arte Supply Chain

This document is classified as VIVACE Public

STATE-OF-THE-ART REVIEW

TECHNIQUES TO MODEL THE SUPPLY
CHAIN IN AN EXTENDED ENTERPRISE
by
Chang-Seop Kim, James Tannock, Mike Byrne
Richard Farr, Bing Cao, Mahendrawathi Er
Operations Management Division
University of Nottingham

Abstract:
This document describes how simulation tools might be applied to investigate logistics at
both the extended enterprise level, and the internal, company level. Supply chain modelling
and management are discussed, and metrics are proposed whereby the efficiency of a
conceptual enterprise might be assessed. The concept of data-driven simulation is
introduced, an approach that may be of particular interest within VIVACE Task 2.5.1.

Dissemination:
Public

Deliverable/Output n°: D2.5.1_1 Issue n°: 1

Keywords:
State-of-the-art review, supply chain simulation, data-driven simulation, extended enterprise, virtual
enterprise, logistics

VIVACE WP2.5/UNOTT/T/04021-1.0 Page: 1/ 69
© 2004 VIVACE Consortium Members. All rights reserved.

VIVACE SoA Supply Chain Modelling

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY ......................................................................................6
2. DEFINITION OF TERMS ......................................................................................7
3. INTRODUCTION...................................................................................................8
4. THE BUSINESS ENVIRONMENT AND THE SUPPLY CHAIN ............................9
4.1. Supply chain definition ............................................................................................. 10
4.2. Behaviour of the supply chain .................................................................................. 10
4.3. Supply chain management....................................................................................... 12
4.4. Supply chain risk, robustness and resilience ........................................................... 14
4.4.1. Types of supply chain risk ................................................................................ 15
4.4.2. Definition of robustness and resilience ............................................................. 16
4.4.3. Strategies to achieve supply chain robustness and resilience ......................... 17
4.4.4. Qualitative approaches to supply chain robustness and resilience .................. 17
4.4.5. Quantitative techniques to supply chain robustness and resilience ................. 18
4.4.6. IT infrastructure and decision support systems ................................................ 19
4.4.7. Supply chain risk management......................................................................... 20
4.5. The evolution of the manufacturing business .......................................................... 20
4.6. Contemporary trends in supply chain management................................................. 22
4.6.1. The changing nature of competition ................................................................. 22
4.6.2. Collaboration..................................................................................................... 23
4.6.3. The extended enterprise ................................................................................... 24
4.6.4. The virtual enterprise ........................................................................................ 24
4.7. Enterprise integration ............................................................................................... 25
5. SUPPLY CHAIN MODELLING BEST PRACTICE .............................................26
5.1. Classification of supply chain modelling methods.................................................... 26
5.2. Techniques for supply chain modelling .................................................................... 27
5.2.1. Linear programming.......................................................................................... 28
5.2.2. Mixed-integer programming .............................................................................. 28
5.2.3. Network models ................................................................................................ 28
5.2.4. Simulation modelling......................................................................................... 29
6. SUPPLY CHAIN MANAGEMENT SOFTWARE .................................................31
6.1. Evolution of SCM software....................................................................................... 31


6.2. Supply Chain Management software functionality ................................................... 31
6.3. Supply chain planning .............................................................................................. 32
6.3.1. Demand planning.............................................................................................. 32
6.3.2. Production and distribution planning................................................................. 32
6.3.3. Production scheduling ...................................................................................... 33
6.4. Supply Chain Execution ........................................................................................... 33
6.4.1. Procurement and inventory management......................................................... 33
6.4.2. Order management........................................................................................... 33
6.4.3. Manufacturing execution................................................................................... 33
6.5. Logistics management ............................................................................................. 33
7. INTRODUCTION TO SIMULATION ...................................................................35
7.1. Types of simulation .................................................................................................. 35
7.2. Best practice simulation methodology...................................................................... 36
7.2.1. Formulating the problem and planning the study.............................................. 37
7.2.2. Collecting the data and defining the model....................................................... 37
7.2.3. Validation .......................................................................................................... 38
7.2.4. Constructing a computer model ........................................................................ 38
7.2.5. Verification ........................................................................................................ 39
7.2.6. Determining run parameters of the simulation .................................................. 39
7.2.7. Performing simulation experiments................................................................... 39
7.2.8. Analysing output data ....................................................................................... 39
7.3. Supply chain simulation ........................................................................................... 40
8. SOFTWARE SELECTION FOR STATE-OF-THE-ART SUPPLY CHAIN
SIMULATION ............................................................................................................41
8.1. Literature survey on discrete-event simulation software .......................................... 41
8.2. The evaluation and elimination process................................................................... 42
8.2.1. Initial cut off....................................................................................................... 42
8.2.2. Selection of packages best suited to the aeronautical supply chain................. 42
8.2.3. Detailed evaluation of short-listed software ...................................................... 42
8.3. Detailed discussion of critical software features for WP2.5 ..................................... 44
8.3.1. Model building using programming (scripting) / access to programmed modules
44
8.3.2. Run-time dynamic model reconfiguration ......................................................... 45
8.3.3. Simulation Engine ............................................................................................. 45
8.3.4. Optimisation engine .......................................................................................... 45
8.3.5. Input / output capabilities .................................................................................. 45


8.3.6. Price.................................................................................................................. 45
8.4. Simulation software selection conclusions............................................................... 46
9. SUPPLY CHAIN PERFORMANCE MEASUREMENT........................................47
9.1. Performance measurement frameworks .................................................................. 47
9.2. Performance metrics benchmarking and interrelationships ..................................... 54
9.3. Performance management....................................................................................... 54
9.4. Performance measurement for supply chain logistics in the project ........................ 55
9.4.1. Metrics based upon cost ................................................................................... 55
9.4.2. Metrics based upon customer service .............................................................. 56
9.4.3. Metrics based upon Capability.......................................................................... 56
9.4.4. Metrics for tasks 2.5.3 and 2.5.1....................................................................... 57
10. PROPOSED SIMULATION WORK .................................................................58
10.1. Supply chain logistics modelling........................................................................... 58
10.1.1. The case for data-driven simulation .............................................................. 58
10.1.2. Simulation scope and architecture ................................................................ 59
10.2. Internal logistics modelling ................................................................................... 59
10.3. Data collection methodology for the simulations .................................................. 60
10.4. Metric calculation and aggregation....................................................................... 60
10.5. The balanced scorecard ....................................................................................... 61
10.5.1. Performance on the aggregated level ........................................................... 61
10.5.2. Performance on the detailed level................................................................. 62
10.6. Incorporating performance metrics in performance management........................ 63
11. CONCLUSIONS ..............................................................................................64
12. REFERENCES ................................................................................................65



LIST OF FIGURES
Figure 1: Flows in the supply chain (from Spekman et al [1998])........................................... 10
Figure 2: Distortion and the Bullwhip Effect (Davis and O’Sullivan [1999]) ............................ 11
Figure 3: Supply Strategy (adapted from Schary and Skjott-Larsen, [1995]) ......................... 13
Figure 4: Key supply chain business processes [Lambert et al, 1988] .................................. 14
Figure 5: Key transition to collaboration in the supply chain (Spekman et al [1998]) ............. 23
Figure 6: An example of the extended enterprise [Tan, 2001] ............................................... 24
Figure 7: A typical virtual enterprise [Jagdev and Browne, 1998] .......................................... 25
Figure 8: Taxonomy of supply chain models [Min and Zhou, 2002] ....................................... 27
Figure 9: Types of integrated supply chain models [Min and Zhou, 2002] ............................. 27
Figure 10: Sample supply chain network [Swaminathan et al, 1998] ..................................... 29
Figure 11: Procedure for model development ........................................................................ 40
Figure 12: The supply chain measurement system [Beamon, 1999] ..................................... 48
Figure 13: Metrics at 5 basic links in a supply chain [Gunasekaran et al, 2001].................... 49
Figure 14: Applying supply chain metrics based on process [Chan and Qi, 2003] ................ 50
Figure 15: Tradeoff curve for inventory and service [Hausman, 2002]................................... 51
Figure 16: The AMR Research hierarchy of supply chain metrics [Hofman, 2004]................ 52
Figure 17: Supply Chain Operations Reference model, showing three levels of
process detail [Supply Chain Council, 2004]......................................................... 53
Figure 18: Matrix of production and logistic concepts evaluated............................................ 60
Figure 19: Sample cost metrics graph.................................................................................... 63

LIST OF TABLES
Table 1: Simulation package evaluation scores ..................................................................... 44
Table 2: Comparison of critical criteria for the five finalists .................................................... 46
Table 3: Goal of performance measure types [Beamon, 1999].............................................. 48
Table 4: Production / Logistics Metrics Scorecard ................................................................. 62



1. EXECUTIVE SUMMARY
In order to remain competitive in a business where products are tremendously complex,
collaborative partnerships must be formed, allowing each business to focus on core strengths
with the collaborative enterprise delivering the whole product and service offering. There is a
danger, however, that in pursuing core activities the partners miss opportunities to enhance
the competitiveness of the supply chain as a whole.
Only when a value can be put on desirable factors such as ‘responsiveness’ or ‘reliability’ can
it be determined where limited resources should be committed in order to reap the greatest
rewards. In the supply chain context it is necessary, among a number of issues, to select
which businesses will take part in the ‘virtual’ enterprise, since each potential partner will
have its own performance history.
Collaborative enterprise concepts need to be evaluated rapidly, yet the complexity of the
inter-related network of aerospace businesses makes this a very difficult prospect. This
document describes a procedure whereby this process of evaluation could be improved
considerably, making use of state-of-the-art management tools, simulation software, and
appropriate performance metrics – all of which are reviewed in this document.
This document represents a Month 12 deliverable for UNOTT staff, reporting on the state of
the art in supply chain modelling, with particular reference to the extended enterprise.



2. DEFINITION OF TERMS
The reader will encounter the following terms within the report:
Enterprise Resource Planning (ERP): A broad range of activities supported by a software
tool that assists in the management of important business processes, including product
planning, purchasing, inventory strategy, interactions with suppliers, management of
customer service activity, order tracking, etc.
Extended Enterprise: An enterprise where companies are interdependent and integrated
collaboratively in the design, development, manufacturing and delivery of a product to end
user
Material Requirements Planning (MRP): Process (and supporting software) for determining
material, labour and machine requirements in a manufacturing environment. Now
superseded by MRPII.
Manufacturing Resource Planning (MRP2): The consolidation of material requirements
planning (MRP), capacity requirements planning (CRP), and master production scheduling
(MPS)
Simulation: The imitation of the operation of a real world process or system over time.
Simulation involves the generation of an artificial history of the system and the observation of
that artificial history to draw inferences concerning the operational characteristics of the real
system that is represented. (Banks, 1998)
Supply Chain: A network of connected and interdependent organisations mutually and co-
operatively working together to control, manage and improve the flow of material and
information from suppliers to end users.
Supply Chain Management (SCM): The management of upstream and downstream
relationships with suppliers and customers to deliver superior customer value at less cost to
the supply chain as a whole.
Virtual Enterprise: An enterprise created to add value by selecting business resources from
different companies and integrating them into a single business entity.



3. INTRODUCTION
This document is a state-of-the-art review report prepared for the VIVACE project by the
University of Nottingham (UNOTT) with assistance from Volvo Aero Corporation (VAC) and
MTU Aero Engines (MTU). This report represents Deliverable D2.5.1_1 ‘Techniques to
Model the Supply Chain in an Extended Enterprise Environment’, which is a Month 12
deliverable for Task 2.5.1.
In addition, this document is intended to fulfil the requirements for Sub-task 2.5.3.1 which
requires a state-of-the-art review and description of ‘current techniques and methods to
evaluate, simulate and optimise different logistic concepts, primarily on a company level but
also as part of the supply chain’.
This document is organised as follows: Chapter four describes the concept of the supply
chain and extended enterprise, and identifies significant work in the field, while chapter five
describes supply chain modelling techniques, outlining a number of options for
representation. Chapter six provides an introduction to computer-based supply chain
management, a business function that may be a valuable source of data.
Chapter seven offers an introduction to simulation – particularly computer simulation –
identifying best practices; chapter eight provides a review of software tools available for this
purpose, making suggestions as to the most appropriate software for the work to be
undertaken.
Chapter nine addresses supply chain performance measurement methods, identifying those
that are applicable within simulation in general, and for the purposes of Work Package 2.5 in
particular.
In the tenth chapter, proposals are made for the simulation work to be undertaken by project
staff. The final chapter presents conclusions.



4. THE BUSINESS ENVIRONMENT AND THE SUPPLY CHAIN
Leading industries worldwide are placing increasing emphasis on integrating, optimising, and
managing their entire supply chain from component sourcing, through production, inventory
management, and distribution to final customer delivery. Over the last few decades, business
environments have been changing from mass-production to customisation, and from
technology and product-driven to market and customer-driven. Providing distinctive customer
value has become one of the main business drivers for companies. However, a single
company often cannot satisfy all customer requirements, including fast-developing
technologies, a variety of product and service requirements, and shortened product life-
cycles. Such developing new business environments have made companies look to the
supply chain as an ‘extended enterprise’, to meet the expectations of end-customers.
Participants within the extended enterprise will cooperate and collaborate with each other to
achieve common goals, hence gaining competitive advantages. The efficiency of the supply
chain, and its interaction with the company’s own logistics concept may determine the
performance of an individual company within the extended enterprise. In many cases, the
performance of a company will be highly dependent upon its upstream suppliers.
Since the 1980s, aero-engine and component manufacturers have faced increasing
competition from all over the world. The product introduction life-cycle is becoming shorter
and market requirements more diversified, while there is pressure to cut costs and product
lead-times.
Performance, quality and price used to be key factors for competitive advantage, but service
is increasingly becoming a differentiation factor. Companies can no longer maintain
profitability and competitive advantage simply with good quality products and technologies in
the traditional ways [Christopher, 1998]. Alternative approaches now being explored feature
a combined product and service offering in which the boundaries between manufacturer,
vendor and support provider are eroded. Within the aero industry, current product-service
concepts include ‘Total Care’ and ‘Power-by-the-Hour’.
Often, a single company can no longer compete effectively in the modern aero-engine
market, so interest in the extended enterprise has grown. Companies have benefited from
collaborative partnerships [Lummus and Vokurka, 1999] and risk-and-revenue sharing
arrangements. Because of the high initial costs associated with aero-engine development
and manufacture, it is particularly important that efficient supply chain operations allow
income streams to be secured throughout the product lifecycle.
The creation of distinctive customer value requires the provision of a differentiated offering
including short lead-times linked to high flexibility in the volume and variety of products and
associated services. These requirements are frequently too demanding for a company to
accommodate entirely using only its own resources. Traditional vertical integration is no
longer the solution because it would not be flexible enough to accommodate the variety of
requirements. Therefore, companies may need to deliver customer value in new ways,
obtaining and retaining vital business contracts. Companies have tended to focus on their
own core business and competencies, outsourcing other areas into the extended enterprise
[Lehtinen, 1999].
Christopher [1998] argued that real competition in the marketplace now exists between
supply chains, not between companies. This implies that an organisation can no longer act
as an isolated and independent entity in competition, but the fully-integrated supply chain can
provide competitive advantages in the market.


4.1. SUPPLY CHAIN DEFINITION
A number of definitions of the supply chain have been proposed. Christopher [1998] defined
it as, “a network of connected and interdependent organisations mutually and co-operatively
working together to control, manage and improve the flow of material and information from
suppliers to end users”. According to Johansson [2002], one of the most common
perceptions of the supply chain is, “A system whose constituent parts include material
suppliers, production facilities, distribution services and customer linked together via the
feed-forward flow of materials and the feedback flow of information”.
It is commonly accepted that there are three main flows in the supply chain: material flow,
information flow, and cash flow. The activities involved in the material flow are to deliver to
the end-user via procurement of raw materials, manufacturing, distribution and customer
service. All these activities must be managed using suitable information flows. (Cash flows
within the supply chain do not fall within the scope of WP2.5.) Figure 1 shows the forward
flow of materials from upstream to downstream, the bidirectional flow of information, and the
movement of money from downstream to upstream.

Figure 1: Flows in the supply chain (from Spekman et al [1998])

4.2. BEHAVIOUR OF THE SUPPLY CHAIN
Supply chains do not always behave as expected or desired. Excessive demand variability –
due to information distortion in the supply chain, between one member and the next – can
become a serious problem, and this led to some of the early studies of supply chain
behaviour.
Forrester [1961] initiated the analysis of demand variability amplification and pointed out that
it is a consequence of industrial dynamics; the time-varying behaviours of industrial
organizations. Demand variability can be amplified as one moves up the supply chain, and
small changes downstream can result in large variations upstream. As a result, the whole
supply chain can be distorted by very large demand swings; as each company within the
supply chain tries to solve the problem within their own perspective. This distortion is known
as the Bullwhip or Forrester effect (Lee, et al [1997], Metters [1997], Fransoo & Wouters,
[2000]) and has been observed across most industries (Figure 2).



Figure 2: Distortion and the Bullwhip Effect (Davis and O’Sullivan [1999])

The consequences are significant; piles of stock, frequent stock-outs and unpredictable
demands, and therefore bottlenecks in delivery. Lee et al [1997] identified four major causes
of the Bullwhip effect:
• Quality of the forecast and its update frequency
• Reorder frequency and the reorder batch size
• Price fluctuation
• Policy for expectation of shortage and level of safety stocks

In general, the solutions to the bullwhip effect should be in line with the causes. Lee et al
[1997] developed a framework for supply chain co-ordination initiatives to deal with bullwhip
effect. The framework includes three general counteracts proposed by the authors:



information sharing, channel alignment, and operational efficiency. In relation to operational
efficiency, for example, a company can reduce the bullwhip effect by mitigating price
fluctuation with an initiative called every day low price. By this initiative, the manufacturer can
reduce the incentives for retailers forward buying. On the other hand, to obtain better demand
transparency from the end customers, the manufacturer may have to initiate the use of point-
of-sale (POS) data or other means of transferring data such as web-based technology or
electronic data interchange (EDI). Machuca & Barajas [2004] studied the impact of EDI on
reducing bullwhip effect and supply chain costs. They concluded that the comprehensive use
of EDI results in substantial reduction of the bullwhip effect and associated supply chain
costs.

In addition to demand variability and information distortion, other main issues in supply chain
management relate to the uncertainties within the supply chain system. There are many
sources of uncertainties in a supply chain. Davis [1993] identifies three sources of
uncertainties:
• Supplier uncertainty measured in terms of suppliers’ on-time performance, average
lateness and degree of inconsistency;
• Manufacturing uncertainty that arises due to process performance, machine breakdown
etc;
• Demand or customer uncertainty arising from forecasting errors, irregular orders etc.
Lee and Billington [1992] claim that one of the potential pitfalls in managing supply chains is
failing to understand the likelihood and the magnitude of impact of these uncertainties.
Reiner and Trcka [2004] argue that the main objective of problem-solving methods in SCM is
to reduce uncertainties. Fisher [1997] proposes that the supply chain strategy has to match
the level of demand uncertainty of the product. Lee [2002] extends Fisher’s framework to
include supply uncertainties in developing the right supply chain strategy.

4.3. SUPPLY CHAIN MANAGEMENT
The term supply chain management was introduced in the early 1980s by Oliver and Webber
[1982] where they discuss the potential benefits of integrating purchasing, manufacturing,
sales and distribution. Houlihan [1987] repeats the term to describe the management of
materials across organisational borders. Since then, many researchers have worked on
establishing the theoretical and operational bases for supply chain management concepts
including Giannakis and Groom [2004], Lee and Billington [1992], Ellram and Cooper [1993],
Schary and Skjott-Larsen [1995], Fisher [1997], Lambert et al [1998], and Lee [2002].
Definitions of Supply Chain Management (SCM) have been supplied by several authors.
Ellram and Cooper [1993] described it as “an integrating philosophy to manage the total flow
of a distribution channel from supplier to ultimate customer”. Christopher [1998] defined SCM
as ‘the management of upstream and downstream relationships with suppliers and
customers to deliver superior customer value at less cost to the supply chain as a whole”.
From these definitions, SCM should integrate all the activities within the supply chain into a
seamless process. In other words, it links all the involved organisations including internal
departments, external partners and vendors, and third party companies, which means that
the whole set of processes and their activities must be viewed as one system.
According to Schary and Skjott-Larsen [1995], the full strategy in supply chain management
has three points of focus: structure, organisation and process. The interrelationships between


the three focuses are depicted in Figure 3. At a strategic level, supply strategy concerns the
supply structure and organisations. Structure of the supply chain deals with the issue of
location of facilities and processes by stages within the supply chain. In addition, Lambert et
al [1998] describe supply chain structure as the group of members, the structural dimensions
of the group (horizontal and vertical structure and the focal firm’s position in the horizontal
structure) and the links between members of the supply chain.

Supply Strategy

Corporate Level
Organisation
- Which organisation takes direct
Structure
responsibility for each stage of
Location of facilities and the supply process?
processes by stage within the
- Inter-organisational
supply chain
relationships

Operations

Process
- Planning, performing and
controlling operations
- Co-ordination

Figure 3: Supply Strategy (adapted from Schary and Skjott-Larsen, [1995])

The second focus of supply strategy proposed by Schary and Skjott-Larsen
[1995] covers the issues of organisations and their boundaries. The organisations of supply
chains include: 1) determining which organisation is responsible for each stage of supply
process and 2) inter-organisational relationships. The first point concerns with how much of
the supply chain a company should own. The issue of conducting activities in-house or
buying from outside organisations has been widely addressed in the literature (Fine and
Whitney [1996], Slack and Lewis [2002], Wisner et al [2004], pp 43). Equally, the issue of
inter-organisational relationships has also received a lot of attention in supply chain
management literature (Harland [1996], Peck and Juttner [2002]). According to Slack et al.
[2004], the type of inter-firm contact can be categorised based on:
• The structure of the market relationships in terms of the number of supply relationships
used by an operation.
• The closeness of the relationships, ranging from transactional or ‘arm-length’
relationships at one extreme to close relationships or ‘partnerships’ at the other
extreme.
In the new paradigm, the number of suppliers is likely to be reduced (Chen and Paulraj
[2004], Slack et al [2004]), but the quality of interaction – the level of information sharing -
with the remaining companies is increased. Supplier efficiency is considered through a



reconciliation of cost and quality throughout the whole supply chain, rather than simply as
direct suppliers offering the lowest price. Likewise, the relationships with downstream
players, such as distributors, are tightened. Sharing point of sales (POS) data is an example
of how information sharing is enhanced from downstream players of a supply chain.
The third focus of supply strategy proposed by Schary and Skjott-Larsen [1995] is on
process, which cover the issues of planning, performing and controlling operations.
Processes need to be co-ordinated in order to ensure their continuity and their ability to
respond as an integral unit in order to achieve the overall objectives of the system. Lambert
et al. [1998] propose a process-based framework for managing a supply chain. As depicted
in Figure 4, they view supply chain management as an integrated approach of delivering
values to the end customers, which involve key processes such as customer relationship
management, demand management, order fulfilment, procurement, etc. These processes are
facilitated by information technology solutions such as Enterprise Resource Planning (ERP),
distribution requirements planning, electronic commerce, Product Data Management (PDM),
collaborative engineering, etc. [Aberdeen Group, 1996]. Duplicated and non-value-adding
activities must be eliminated within the supply chain to improve the efficiency of the whole
extended enterprise.

Figure 4: Key supply chain business processes [Lambert et al, 1988]

4.4. SUPPLY CHAIN RISK, ROBUSTNESS AND RESILIENCE
The notion of risk is receiving greater attention in research on supply chain management by
academics and practitioners alike [Spekman and Davis, 2004]. Not only are there risks
inherent in supply chain flows, but also there are risks associated with security, opportunistic
behavior, corporate social responsibility, etc.



It is thus very important for organizations and supply chains to have the abilities to be
responsive to risks to achieve supply chain robustness and resilience.

4.4.1. Types of supply chain risk
Risk is an inherent feature of all operations [Slack and Lewis, 2002]. Supply chain risk
management has recently gained much greater attention as a result of natural disasters and
terrorist attacks, as well as the greater complexity and globalization of supply chains.

First supply chains are subject to disruption type of risks caused by natural or environmental
disasters. Norrman and Jansson [2004] cite a few examples of these:

• Hurricane Floyd flooded a Daimler-Chrysler plant producing suspension parts in
Greenville, North Carolina (USA). As a result, seven of the company’s other plants
across North America had to be shut down for seven days.

• The foot-and-mouth disease in the UK in 2001 affected the agriculture industry more
than its last outbreak 25 years ago. The reason for this was that former local and
regional supply networks had become national and international, and the industry was
much more consolidated. But other industries were also affected: luxury car
manufacturers like Volvo and Jaguar had to stop deliveries due to lack of quality
leather supply.

• Toyota was forced to shut down 18 plants for almost two weeks following a fire in
February 1997 at its brake-fluid proportioning valve supplier. Costs caused by the
disruption were estimated to be $195 million and sales loss was estimated to 70,000
vehicles ($325 million) [Converium, 2001]. This emphasized the problems of single
sourcing and partnerships for the supply of critical parts.
Norrman and Jansson [2004]

Peck and Juttner [2002] added a few more man-made problems: Y2K-related IT problems,
the fuel price protests of September 2000, recent transportation infrastructure failures – for
example, rail disruptions, terrorist attacks of 11th September 2001.
Today’s business world also faces challenges and pressures on an unprecedented scale
from customer demand and competition. According to Christopher and Peck [2004],
Christopher [2003], Haywood and Peck [2003], Peck [2004] many of these obstacles have
the potential to severely affect the continuity of a commercial enterprise, in particular, through
disruption to the wider supply chain.

A further reason for this increased risk has come, paradoxically, from the focus on efficiency
and cost reduction. Examples include the move to offshore sourcing and manufacturing in
pursuit of lower labour costs; the widespread adoption of ‘lean’ practices, particularly through
inventory and capacity reduction; and the continuing trend towards outsourcing and single
sourcing. All these strategies can lead to beneficial business outcomes, but can also radically
change the risk profile of the supply chain.

Second there are delay type risks on a more continuous and smaller scale [Chopra and
Sodhi, 2004]. Delays in material flows often occur when a supplier, through high utilization or
another cause of inflexibility, cannot respond to changes in demand. Other culprits include



poor-quality output at supplier plants (or at their suppliers’ plants), high levels of handling or
inspections during border crossings and changing transportation modes during shipping.

A third type of risks is the Forecast Risk. Forecast risk results from a mismatch between a
company’s projections and actual demand. If forecasts are too low, products might not be
available to sell. Forecasts that are too high result in excess inventories and, inevitably, price
markdowns. Long lead times, seasonal demand, high product variety and smaller product life
cycles all increase forecast error.

Forecast inaccuracies can also result from information distortion within the supply chain.
Christopher and Lee [2004] describe this type of risk caused by, for example, the attitudes
and perceptions of the users and members of the supply chain. A manager running a supply
chain with these risks may lack confidence in the following:

• order cycle time
• order current status
• demand forecasts given
• suppliers’ capability to deliver
• manufacturing capacity
• quality of the products
• transportation reliability
• services delivered
The intangible lack of confidence in a supply chain leads to actions and interventions by
supply chain managers throughout the supply chain, which collectively, could increase the
risk exposure. The “bullwhip” effect (see Section 4.2), which describes increasing fluctuations
of order patterns from downstream to upstream supply chains, is such an example, partially
caused by the rational actions of managers aiming to reduce exposure to supply chain risk.
Other types of risk include inventory, capacity, systems, intellectual property, procurement
and receivables risks.

4.4.2. Definition of robustness and resilience

The ability to be respond to the risks listed in the previous system determines supply chain
robustness and resilience. Some authors distinguish between robustness and resilience.
Christopher and Rutherford [2004] define robustness as meaning “strong, and sturdy:
constitutionally healthy”. Thus a robust supply chain might reasonably be expected to
produce consistent results with very little variation in output; However, Resilience is “the
ability of a system to return to its original (or desired) state after being disturbed”. A resilient
supply chain must also be adaptable, as the desired state may be different from the original.
The key difference between the two is in their ability to respond to variations in input. A
robust supply chain can deal with reasonable variability in input whilst maintaining good
control over output variability. A resilient supply chain is certainly robust, but it offers much
more; as well as being responsive to predictable input variability it is also able to respond to a
sudden and unexpected shift in the level and variability of input.



Other authors tend to use robustness and resilience interchangeably. Conboy and Fitzgerald
[2004] refer to Robustness or resilience as the ability to endure all transitions caused by
change, or the degree of change tolerated before deterioration in performance occurs without
any corrective action. The RLSN Project Team of Altarum [2003], working on the Robust
Lean Supply Networks (RLSN) project, develop knowledge and capabilities that will allow
defence suppliers to be more responsive to demand surges and supply disruptions anywhere
in their supply chains (this by Christopher’s definition will be resilience).
In the context of this review, we will not intentionally distinguish the two as the strategies,
approaches and techniques described below could apply to both types of variation.

4.4.3. Strategies to achieve supply chain robustness and resilience
To achieve robustness and resilience, supply chain risk mitigation strategies should be
created at the top level. Christopher [2003] outlines a set of principles that underpin the
creation of a more resilient supply chain:
• Supply chain understanding: One fundamental prerequisite for improved
supply chain resilience is an understanding of the network that connects the
business to its suppliers and their suppliers, and to its downstream customers
and their customers. Mapping tools can help in the identification of ‘pinch
points’ and ‘critical paths’.
• Supplier base strategy: While there has been a move towards a reduction of
the supplier base in many companies, there could be limits to what might be
pursued. Where a firm has multiple sites, it may be possible to have a single
source for an item or service into each location, thus gaining some of the
advantages of single sourcing without the downside risk.
• Supply chain collaboration: It will be apparent that since supply chain
vulnerability is a network wide concept, management of risk has to be network-
wide too. A high level of collaborative working across supply chains can help
mitigate risk. The challenge is to create conditions in which collaborative
working becomes possible.
• Agility: One of the most powerful ways of achieving resilience in the supply
chain is to create networks which are capable of rapid response to changed
conditions. This is the idea of agility whereby the time required to respond to
new circumstances is dramatically reduced. Time compression is at the heart of
‘Agile’ strategies Agility is founded on two key principles – velocity and
visibility.
• Creating a supply chain risk management culture: It can also be argued that
supply chain risk assessment should be a formal part of the decision-making
process at every level. As in every case of cultural change within organisations,
nothing is possible without leadership.
[Christopher, 2003]

4.4.4. Qualitative approaches to supply chain robustness and resilience
On the tactical level, improvement approaches and techniques have been widely used in
operations management [Slack at al, 2001]. These can apply to the supply chain as well.


There are two types of improvement approaches: breakthrough and continuous. Business
process re-engineering is an example of a breakthrough improvement approach while TQM
incorporates a process-oriented continuous improvement process. The TQM improvement
process typically employs many types of improvement techniques, for example, statistical
process control, failure mode and effect analysis, flow charts, scatter diagrams, cause-effect
diagrams, Pareto diagrams and Why-why analysis, which can be of use in supply chains as
well as internal business processes.
From a supply chain point of view, the newly emerging field of supply chain event
management [Stiles, 2002] holds some promise. The idea behind event management is that
partners in a supply chain collaborate to identify the critical nodes and links through which
material flows across the network. At these nodes and links, control limits are agreed within
which fluctuations in levels of activities are acceptable, e.g. shipments from an off-shore
manufacturing source. If for whatever reason the level of activity goes outside the control
limit, then an alert is automatically generated to enable corrective action to be taken

4.4.5. Quantitative techniques to supply chain robustness and resilience
Although the number of supply chain variables is huge, and there are many complicatedly
intertwined supply chains affecting each enterprise, quantitative techniques offer the
opportunity to improve and even optimise supply chain robustness and resilience both on the
strategic and tactical levels.
There are three main types of quantitative techniques for supply chain robustness and
resilience analysis; analytical methods, simulation methods and combined approaches. (See
Chapter 7 for more information on simulation approaches). The main analytical approaches
are sensitivity analysis, scenario analysis, multi-dimensional dynamic programming,
stochastic programming, robust optimisation and real options. A short description of the
methods now in favour is given here.

• Scenario analysis: Scenario analysis has been in use for decades. By generating
scenarios with associated probabilities and effects, robust decisions can be made to
minimise downside risks (the risk of not meeting certain targets) and disasters. A
good example of its use is task 2.1.1.
• Stochastic programming: Stochastic programming with recourse was first
introduced by Dantzig in 1995. Since then, there has been significant development.
The most common stochastic programming problem is the two-stage stochastic linear
programming problem. Infanger [1994] describes a two stage stochastic linear
programming problem as consisting of a first-stage master problem involving structure
decision variables, and a number of second-stage problems involving operational
decisions variables. The objective is to optimise the expected values (cost or profit) of
all scenarios.
Santoso et al [2003] proposed a stochastic programming model and solution
algorithm for solving supply chain network design problems of a realistic scale. Their
solution methodology integrates a recently-proposed sampling strategy, the Sample
Average Approximation scheme, with an accelerated Benders decomposition
algorithm to quickly compute high quality solutions to large-scale stochastic supply
chain design problems with a huge (potentially infinite) number of scenarios.



• Robust Optimisation: Robust optimization tries to achieve a balanced or optimal
solution for all scenario realizations by minimizing either expected regret (e.g.
downside risk) or absolute variation. Bertsimas and Thiele [2003] propose a general
methodology based on robust optimization to address the problem of optimally
controlling a supply chain subject to stochastic demand in discrete time. This model
incorporates a wide variety of phenomena, including demands that are not identically
distributed over time and capacity on the echelons and links. When the parameters
are chosen appropriately, the proposed approach preserves performance while
protecting against uncertainty.
• Real Options: Real options is an approach which is used more and more for
investment planning. This is due to some of the drawbacks of the traditional
discounted cash flow approach.
The main idea about real options is that options can be created with a cost. With more
and better information available in the future from acquiring the option, a decision
maker can significantly avoid risks and improved expected returns on investment.
• Simulation: Siprelle etc. [2003] describe the benefits of using a supply chain
simulation tool to study inventory allocation. Simulation was used for answers to the
following questions:
– What is the relationship between inventory policies and the resulting inventory
levels, customer service levels, and redeployment of stock?
– Does the location of inventory storage for different classes of product have an
effect on total inventory levels and redeployment of stock?
– Would better forecasting methods reduce the amount of inventory in the system
and the redeployment of stock?
• Combined approaches: Truong and Azadivar [2003] describe a hybrid optimization
approach to address the Supply Chain Configuration Design problem. The new
approach combines simulation, mixed integer programming and genetic algorithms.
The genetic algorithms provide a mechanism to optimize qualitative and policy
variables. The mixed integer programming model reduces computing efforts by
manipulating quantitative variables. Finally simulation is used to evaluate
performance of each supply chain configuration with non-linear, complex relationships
and under more realistic assumptions.

4.4.6. IT infrastructure and decision support systems
Christopher and Lee [2004] identified the two main elements of the supply chain that can
reduce the lack of confidence – visibility and control. Two things that have happened in the
last few years have improved both supply chain visibility and control significantly. The first of
these is the availability of technology and software to enable the capture and sharing of
information across a supply chain, achieved mainly through IT infrastructure, extranets and
decision support systems including ERP, supply chain management software, and the
collaborative hub concept of WP 3.6. The second, even more fundamental change, is the
increasing willingness of members of the supply chain to put aside the traditional arms-length
relationship with each other and in its place move towards a closer, partnership-type
arrangements.



4.4.7. Supply chain risk management
Risk management is the process whereby decisions are made to accept a known or
assessed risk and/or the implementation of actions to reduce the consequences or
probability of occurrence. Typical risk management aims are to avoid, reduce, transfer, share
or even take the risk. To avoid is to eliminate the types of event that could trigger the risk. To
reduce risk applies both to reduction of probability and consequences. Examples of how to
reduce the impact could be to have an extra inventory, multiple sources, back-up
sites/resources identified, sprinklers in buildings, having risk managers and emergency
teams appointed, parallel systems or to diversify. Probability could be reduced by improving
risky operational processes, both internally and in cooperation with suppliers, and to improve
related processes, e.g. supplier selection. Risk could also be transferred to insurance
companies – or to supply chain partners by moving inventory liability, changing delivery times
of suppliers (just-in-time deliveries), to customers (via make-to-order manufacturing), or by
outsourcing activities. Furthermore, contracts can be used to transfer commercial risks.
Finally, risks could be shared, both by contractual mechanisms and by improved
collaboration.
Norrman and Jansson [2004] describe supply chain risk management as comprising two
elements: the risk management process and Business Continuity Management (BCM). The
risk management process is focused on understanding the risks and minimizing their impact
by addressing, for example, probability and direct impact. The stages of the risk management
process discussed can vary from risk identification/analysis to different forms of risk
management.
There are many methods for risk identification and analysis. One important tool is risk
mapping, i.e. using a structured approach and mapping risk sources and thereby
understanding their potential consequences.
After the risk analysis, it is important to assess and prioritize risks to be able to choose
management actions appropriate to the situation. One common method is to compare events
by assessing their probabilities and consequences and locating them in a risk map/matrix.
BCM is defined as “the development of strategies, plans and actions which provide protection
or alternative modes of operation for those activities or business processes which, if they
were to be interrupted, might otherwise bring about a seriously damaging or potentially fatal
loss to the enterprise” [Hiles and Barnes, 2001]. BCM includes crisis management (overall
processes to manage the incident), disaster recovery (recovery of critical systems,
applications, data and networks), business recovery (recovery of critical business processes)
and contingency planning (recovery from impact external to the organization). Developing
action plans is important in BCM, and business continuity planning (BCP) is a term often
used.
Sinha et al [2004] develop a generic methodology for mitigating risks in the aerospace supply
chain with a view to consistency across supply chains.
To aid the development of the methodology, IDEF0 (integrated definition) method is
employed. The methodology consists of 5 main tasks: identify risks, assess risks, plan and
implement solutions, conduct failure modes and effects analysis, continuously improve.

4.5. THE EVOLUTION OF THE MANUFACTURING BUSINESS
In addition to radical changes in the ways businesses interact, their internal operations have
also been subject to change during the past few decades, moving beyond the mass


production approach that had been predominant for most of the twentieth century. The main
benefit from mass production was to minimise unit production cost with a high level of
repetitive production bringing about a reduction in the proportion of fixed cost per unit. This
approach was very cost-effective, but allowed little flexibility in product or process. Due to the
high level of investment required, product life cycles were very long and there were few
product varieties. Buffer stocks were used to accommodate unpredictable demands, and to
cope with variability within the manufacturing system. Many companies had vertically-
integrated structures to secure supplies of critical materials, and to achieve cost-
effectiveness through economies of scale. Relations with external companies were neither
close nor cooperative because sharing information was considered as risky, as expertise and
technologies might be revealed to competitors. As a result, interactions with vendors were
often adversarial, win-lose relations.
In the 1970s, the introduction of computerised Material Requirements Planning (MRP)
systems had a great impact on material management methods, in terms of cost, lead-time
and level of work-in-progress (WIP), etc., whilst facilitating greater complexity and flexibility of
manufacturing operations.
Competition intensified during the 1980s, with continuing downward pressure upon cost
joined by requirements for a broad range of reliable, high quality products. Significant
changes during this period were the widespread adoption of Just-in-Time (JIT) work
scheduling and quality initiatives such as Total Quality Management (TQM). The JIT
approach stressed that stocks should not be kept in advance, either for forecast or
unpredictable demands. These concepts brought companies to a realisation of the potential
benefits of integration of functions, as well as the importance of strategic alliances between
customers and suppliers. The concepts of SCM emerged as manufacturers experimented
with strategic partnerships with their immediate suppliers and customers.
Further responses aimed at increasing competitiveness included Concurrent Engineering
(also known as Simultaneous Engineering, Design for ‘X’, etc.; Boeing simply call it ‘working
together’). This involves information being shared between departments, and also up and
down the supply chain with suppliers and customers playing a part in a multi-functional team.
(The application of Concurrent Engineering methodologies is at the heart of VIVACE Task
2.5.4, with which UNOTT has some involvement.)
Agile Manufacture is another route to increased competitiveness, gearing manufacturing
facilities to respond to changes in products or their demand patterns, while Lean
Manufacturing is a (sometimes abused) term describing a range of techniques meant to
eliminate the ‘seven wastes’, or ‘Muda’ in the original Japanese [Ohno, 1988]:
• Overproduction
• Waiting
• Transportation
• Inventory
• Motion
• Over-processing
• Defects
Some sources now include an eighth waste, underutilisation of employees, though there is
clearly a danger that in pursuing high utilisation – of people or machines – overproduction will
result. What is required is a balance where a certain level of inventory is permitted to collect
where it will smooth fluctuations or improve delivery reliability. Similarly, spare capacity may


be tolerated where it increases responsiveness and manufacturing system robustness. In
recognition of the need for a post-lean approach, some companies are now using a new
methodology that acknowledges the need for some of the ‘fat’ that is normally eliminated by
the Lean Manufacturing methodologies. This alternative is called Just Enough Desirable
Inventory, or JEDI.
Any approach meant to eliminate waste requires collaboration within the supply chain, since
inventory can only be reduced safely once delivery performance is assured. Whether an
entire supply chain can be made lean is open to question; often a prime’s desire to become
lean forces its suppliers to deliver small quantities of products at irregular intervals, frustrating
that business’ efforts reduce inventory.

4.6. CONTEMPORARY TRENDS IN SUPPLY CHAIN MANAGEMENT
Thus far, this chapter has presented the economic case for a collaborative supply chain, and
has described its behaviour and means of control. Changes to the way manufacturing
businesses within the supply chain operate have also been explored. Contemporary trends
for the supply chain as a whole are discussed in the subsections that follow. The key issues
are competition, collaboration, the extended enterprise and the virtual enterprise.

4.6.1. The changing nature of competition
From the final customer’s perspective it is satisfaction, based on the overall value of the
product (or product/service bundle) that is vital, regardless of what happens earlier in the
supply chain. Although the operations of an individual company within the supply chain may
be focused on its core business and highly efficient, it may not create the desired value for
the customer unless the whole supply chain is also effectively organised and coordinated.
No single company can ensure that the entire offering is optimal because inefficiency, delays
and waste (i.e. non-value adding activities) may be found elsewhere within the supply chain.
There is also the very real possibility that a set of locally optimised solutions do not equal
optimal performance for the system as a whole. This can affect the competitiveness (and
hence financial situation) of all the collaborators.
By the nature of the modern aerospace industry, competition must coexist with collaboration
[ACARE, 2002]. The development of the extended enterprise concept facilitates effective
collaboration. Hence, competition is less evident between companies, but appears more
strongly between supply chains or extended enterprises. Only an effectively integrated
supply chain can create full end-customer value, with companies working together as
partners.
Collaborative partnerships with the companies that are found upstream and downstream in
the supply chain are a vital prerequisite to achieve a highly competitive posture for the
extended enterprise. Through collaboration, companies can enhance information and
technology as well as sharing the risks and costs, taking an equitable share in the profits
created. They will be motivated to help each other to improve operational efficiency and
eliminate waste, so that the whole chain will be optimised and integrated as a single system.
As a company faces this new era of competition, the winners will be those companies that
can collaborate and work with their partners, in a supply chain committed to better, faster and
closer relationships with their final customers [Christopher, 1998].



4.6.2. Collaboration
As efficient management of the supply chain becomes critical to achieving high performance,
the intensity of company partnerships must also increase. Cooperation has always involved
sharing information and involvement of suppliers and customers in the long term, but this
arms-length approach may not be sufficient for the extended enterprise.
Spekman et al [1998] state that the next level of intensity is coordination and collaboration,
as shown in Figure 5. According to these authors, in co-ordination relations, trading partners
can cooperate and coordinate to develop seamlessly linked activities between and among
trading partners, through JIT systems and other mechanisms. They consider that this is not
sufficient for total supply chain management, so companies are required to move from
coordination to collaboration.

Figure 5: Key transition to collaboration in the supply chain (Spekman et al [1998])

True collaboration partnerships are based on high levels of trust, commitment and
information sharing among the partners [Slack et al [2004]). Partners throughout the supply
chain must be integrated into others’ processes. Staff need to accept that a company,
although perhaps playing a comparatively minor role in the supply chain, has relations with
many partners, and that its business decisions can have a significant impact on their own
performance as well as that of the whole supply chain. Close collaboration relationships with
partners; including manufactures, suppliers, distributors, transporters and end-customers are
the key to success. Therefore, companies must collaborate with partners towards common
goals and mutual benefit, as well as for the benefit of the individual company. Failing to
collaborate would result in the distortion of information, which, in turn, can lead to
inefficiencies, excess stock, slow response and lost profits [Lee et al, 1997]. Collaboration
also enables partners to gain a better joint understanding of future product demand, and to
implement more realistic programmes to satisfy that demand, so that successful collaboration
yields major benefits: increased market share, stock reductions, reduction in cost and lead-
time, improved quality and shorter product development cycles [Corbett et al, 1999].
These changing environments have created the new concepts of enterprise, referred to as
the extended enterprise and the virtual enterprise. In addition, the concept of the ‘Adaptive
Supply Chain’ has been developed [SAP, 2002] to refer to a supply chain able to have
visibility of requirements and capabilities, and automatically manage variation in these issues
in real time, with greater ‘velocity’ of both information and physical assets within their
networks.



4.6.3. The extended enterprise
Current business environments have changed, as discussed above, so that the traditional
view of business organisation is no longer valid. The concept of the extended enterprise has
recently been developed as a new paradigm to reflect the high level of collaboration between
partners. A company’s operations and processes are not confined to the company, but cross
enterprise boundaries. Integration of the operations of independent companies into the
operations of their partners produces an extended enterprise. The extended enterprise can
be regarded as a kind of enterprise where companies are integrated collaboratively in the
design, development, manufacturing and delivery of a product to end user (Browne et al
[1995], Browne et al [1996]).
According to Spekman and Davis [2004], “the notion of the extended enterprise takes supply
chain management to the next level and focuses on those factors and characteristics that link
supply chain members by far more than just workflow and logistics”. They emphasise that in
an extended enterprise, firms are linked as learning organisations where knowledge
becomes “the currency of exchange”. Key suppliers and partners become virtually a part of
the principal company and its information infrastructure, with frequent exchange of status
information [Jagdev and Thoben, 2001]. Jagdev and Browne [1998] defined the extended
enterprise as the formation of close co-ordination across design, development, costing and
the co-ordination of the respective manufacturing schedules, for co-operating independent
manufacturing enterprises and related suppliers. The extended enterprise is responsible for
all operations related to the product, from procurement of raw material to end customer, plus
maintenance, customer service and final disposal of the product.
All activities for movement of materials and information should be operated through
collaboration with partners in a synchronised and coordinated way. Figure 6 shows a typical
example of an extended enterprise in the manufacturing and distribution supply chain.

Figure 6: An example of the extended enterprise [Tan, 2001]

4.6.4. The virtual enterprise
Like the extended enterprise, the concept of virtual enterprise has emerged as a form of
collaboration, but it has particularly emerged to respond efficiently to the reduced time-to-
market, fast-changing customer requirements for complex products in the digital age. A new
virtual entity can be organised by selecting business resources from different organisations
and integrating them into a single business entity. This is due to the fact that a single
company cannot have all the necessary skills and competencies to respond to the market
requirements. Many different resources within the joint entity collaborate with each other to
perform specific, allocated business operations. The whole joint entity should behave as if it
were a single company committed to a particular project. After the project finishes, the joined


resources can be split apart, to perform other projects - possibly joining again in different
configurations to tackle new projects. This concept of virtual enterprise is made possible by
sophisticated information technology and telecommunication systems.
Some authors define the virtual enterprise as a temporary network of independent
companies engaged in providing a product or service. Forbairt [1996] stated that the virtual
enterprise may have no physical facilities, very few full-time workers and exist as a
combination of resources with specific skills, expertise and competences from different
companies. Scholz [1997] pointed out that a characteristic of the virtual enterprise is the
absence of specific physical attributes and features such as a common administration or a
common legal status. Nevertheless, collaboration can be achieved through the application of
sophisticated information and communication infrastructure and mutual confidence. Figure 7
shows a typical virtual enterprise. The coordinating agent specialises in the coordination of
the activities of other independent companies including suppliers, subcontractors,
manufactures and distributors.

Figure 7: A typical virtual enterprise [Jagdev and Browne, 1998]

4.7. ENTERPRISE INTEGRATION
Enterprise Integration (EI) has emerged as a technique to bring together the various
elements that constitute an enterprise, whether extended or virtual. EI is an holistic approach
that can provide key definition, frameworks and methodologies. EI has largely been
focussed on IT system design to date, and many EI concepts are incorporated into the
VIVACE project in WP3.6 (Collaboration Hub for Heterogeneous Enterprises). Miller and
Berger [2001] describe a concept of the Totally Integrated Enterprise (TIE), with a reference
architecture with four dominant perspectives or reference planes. Miller and Berger propose
a hierarchical concept of the component-based extended enterprise, taking into consideration
the entire customer/product life-cycle.



5. SUPPLY CHAIN MODELLING BEST PRACTICE
As described in Chapter 4, a supply chain encompasses the integrated processes by which
raw materials are converted into finished products and delivered to end-users, perhaps to be
further maintained and serviced throughout the product lifecycle. These processes, including
procurement, production, and distribution, interact with each other and require collaboration
between partners in order to produce an integrated offering. Because of differences in
business environments and market requirements, the supply chain must be configured to
meet specific performance goals. Therefore, the appropriate design and management of the
supply chain are vital.
Modelling can assist in the design and implementation of a new supply chain. According to
Vernadat [1996], there are two basic aspects in supply chain modelling: first, the supply chain
should be modelled in order to manage it properly; second, the processes to be integrated
and coordinated need to be modelled. Therefore, the model should be able to capture the
complexities of the supply chain and facilitate supply chain integration. Li et al [2002]
summarised the main motivations for supply chain modelling:
• Capturing supply chain complexities by better understanding and uniform representation
of the supply chain
• Designing the supply chain management process to manage supply chain
interdependencies
• Establishing the vision to be shared by supply chain partners, and provide the basis for
internet-enabled supply chain coordination and integration
• Reducing supply chain dynamics at supply chain design phases

5.1. CLASSIFICATION OF SUPPLY CHAIN MODELLING METHODS
There are a number of supply chain modelling methods that have been proposed. Beamon
[1998] classified multi-stage models for supply chain design analysis into four categories by
analytical and mathematical approaches. The classifications are:
• Deterministic analytical models,
• Stochastic analytical models,
• Economic models, and
• Simulation models.

Deterministic models assume that all the variables are known and can be specified with
certainty, whilst stochastic models have at least one variable that is unknown and assumed
to follow a particular probability distribution.
Min and Zhou [2002] added more categories of supply chain modelling; hybrid models and
IT-driven models (Figure 8). They also classified deterministic models and stochastic models
in more detail. Deterministic models are divided into single-objective and multiple-objective
models, to tune conflicting objectives of different supply chain partners, and stochastic
models are sub-classified into optimal control theoretic and dynamic programming models.


Hybrid models have characteristics of both deterministic and stochastic models. These
models include inventory-theoretic and simulation models and can manage both deterministic
and stochastic variables. IT-driven models reflect the proliferation of IT applications for
supply chain modelling through rapid developments in Information Technology. These
models target integration and coordination of various activities based on real-time application
throughout the supply chain, including a variety of different systems and system modules,
such as warehousing management systems (WMS), enterprise resource planning (ERP),
geographic information systems (GIS), and aspects of various forecasting, distribution and
transportation systems.

Figure 8: Taxonomy of supply chain models [Min and Zhou, 2002]

In addition to classifications based on mathematical structure, Min and Zhou [2002] classified
supply chain models with regard to the problem scope and application area (Figure 9). They
confined the model problem scope to problems that cut across supply chains. This is due to
the fact that only these models can cover the different functions of the supply chain. These
models are involved with multi-functional issues such as location/routing,
production/distribution, location/inventory control, inventory control/ transportation, and
supplier selection/inventory control.

Figure 9: Types of integrated supply chain models [Min and Zhou, 2002]

5.2. TECHNIQUES FOR SUPPLY CHAIN MODELLING
Four techniques are commonly used to model the supply chain for problem-solving; linear
programming, integer/mixed-integer programming, network models and simulation modelling.
Each of these is described in the sub-sections that follow.



5.2.1. Linear programming.
Linear programming can be used to model various situations, and identifies optimal problem
solutions using linear mathematical equations. Only the relationships between decision
variables and impact on objective functions are considered. Therefore, there are no
qualitative aspects, but only quantitative ones, which means that only problems that can be
expressed mathematically can be solved. The technique is available with computer support
for more complex problems, and is useful for a variety of situations, where a wide range of
constraints can be modelled. Although linear programming helps to find optimum solutions, it
may not be realistic because of the dynamic and non-linear behaviour of many variables.

5.2.2. Mixed-integer programming
Integer programming is similar to the linear programming, but all the variables must be
integers. Linear mathematical equations can still be used for developing solutions in this
approach. On the other hand, Mixed-integer programming (MIP) can use a mixture of integer
and real variables, to cover a wider variety of supply-chain modelling scenarios. Typically,
the real variables relate to materials flow, while integer or binary types are used for model
configuration variables.
Arntzen et al [1995] describes a mixed-integer programming model, called Global Supply
Chain Model (GSCM) that incorporates a global, multi-product bill of materials for supply
chains with arbitrary echelon structure and a comprehensive model of integrated global
manufacturing and distribution decisions. Melachrinoudis and Min [2000] used a dynamic,
multiple objective, mixed-integer programming model for assessing the viability of a proposed
facility site from multi-echelon supply chain perspectives and determining the optimal timing
of relocation and phase-out in multiple planning horizons. Models of the supply chain under
uncertainty generate large mixed-integer programming problems, which can make searching
for solutions based on the standard MIP solution algorithms very time-consuming
[Goetschalckx, 2004].

5.2.3. Network models
Network models represent a supply chain graphically as shown in Figure 10. The network is
represented with nodes and connections. Nodes generally represent plants, distribution
centres, suppliers or customers, while connection represents transportation lanes. The
network can be translated into mathematical representations such as linear, integer and
mixed-integer programming [Hicks, 1997]. A typical example is to find a solution to minimise
the transportation costs from factories to distribution centres with certain production output
from each factory [Johansson, 2002]. The transportation cost could be minimised by
determining the shipping quantity of the product from each plant to each distribution centre.



Figure 10: Sample supply chain network [Swaminathan et al, 1998]

Due to the complexity of representing entire supply chains with networks of this kind,
analyses are often conducted with respect to a single focal company, together with its
suppliers and customers for a limited number of steps up and down the supply chain. Key
issues to be represented in such a model might typically include:
• Identifying which suppliers can offer a given material or component
• The manufacturing lead time for each item, including degree of variation
• The time required to transport materials or components, including degree of variation
• Constraints such as minimum order sizing
• The cost of a material or components, from each source, including transportation cost
• The level of finished goods stock that is typically held at each node within the model
• The time required to raise an order

Equipped with information of this kind, the responsiveness of a virtual enterprise may be
assessed, together with the cost of achieving that level of performance.

5.2.4. Simulation modelling
The main problem with most analytical models is that numerous additional issues and
constraints have to be considered before the results can be applied in practice. Many
analytical models are highly simplified, and consider only a few variables, such as inventory
and the cost of running out of stock, ignoring other costs such as order processing and
transportation. In short, mathematical approaches often require too many simplifications to
model realistic supply chain problems, although they may be valuable for gaining an
understanding of general supply chain principles and effects.
Simulation is the process of designing and creating a model of a real or proposed system,
using abstract objects in an effort to replicate the behaviour of their real-world equivalents.


The parameters of the model are dynamic, and change over a period of time to show the
behaviour of the system under given conditions.
Simulation is considered as one of the most powerful techniques to apply within a supply
chain environment [Terzi and Cavalieri, 2004]. Wyland et al [2000] argue that the increasing
popularity of simulation as a tool in supply chain management is due to its strength in
evaluating system variation and interdependencies. This enables a decision-maker to assess
changes in part of the supply chain and visualise the impact of those changes on the other
parts of the system, and ultimately on the performance of the entire supply chain. Simulation
has been used to model supply chains in various industrial sectors including mobile
communication systems [Persson and Olhager, 2002], food [Reiner and Trcka, 2004],
apparel [Al-Zubaidi and Tyler, 2004], and the aerospace industry [Bilczo et al, 2003].
This approach is judged to have particular merit for the experiments to be conducted within
Tasks 2.5.1 and 2.5.3, and is therefore described in detail Chapter 7.



6. SUPPLY CHAIN MANAGEMENT SOFTWARE
SCM software is highly relevant to WP 2.5, because such applications are designed to plan
and manage many of the issues that will be addressed by this work package. A survey of the
nature and functionality of SCM software has accordingly been carried out.

6.1. EVOLUTION OF SCM SOFTWARE
The evolution of supply chain software began in the early 1970s, when core logistics
applications were developed, including demand forecasting, planning and scheduling, plant
location and layout. The concept of Material Requirements Planning (MRP) emerged,
involving detailed material plans in the form of a Bill-of-Materials (BOM) that broke the
product down in a hierarchical manner, to individual raw materials and components, and
sources of supply. In 1980s MRP systems were extended to Manufacturing Resource
Planning (MRP II) including scheduling and other associated functions. Further increases in
scope brought about the Enterprise Resource Planning (ERP) systems of today.
Through the 1990s, SCM software has been further developed towards managing integrated
supply chains, through seamless delivery of the relevant information within the company as
well as between companies. This resulted in the Advanced Planning Systems (APS).
The distinction between ERP and SCM is fuzzy, and varies between software suppliers.
Certain modules in an ERP system may be referred to as SCM modules. Both provide
planning modules as well as execution ones, but many modules are different, although some
will overlap. ERP generally is a transactional system, covering the full range of
manufacturing, sales and accounting functionality, sufficient to perform virtually all of the
information technology transactions required by an individual enterprise. SCM tends to be
more oriented towards specific logistics functions within the supply chain, with specialised
modules devoted to demand forecasting, production, transportation, delivery and distribution
[Green, 2001]. Both types of system aim to ensure that information from any source is
entered only once, and that the right information is made available for all module/user
requirements.

6.2. SUPPLY CHAIN MANAGEMENT SOFTWARE FUNCTIONALITY
SCM includes modules for supply chain planning, such as forecasting of requirements for
components or products, and supply chain execution through procurement, manufacturing
and distribution. Some of the modules are used for internal processing, including
manufacturing scheduling, planning, inventory management and order management, but
others provide functionality across company boundaries. Many different systems are still
being developed in the market, so that it is not yet possible to define all the standard
functionalities of SCM. However, SCM software generally consists of three major segments:



• Supply chain planning and execution software,
• Warehouse management systems, and
• Transportation systems

In addition, applications such as SAP SCM have added coordination and collaboration
functionalities, such as integration and sharing of data with collaboration partners.

In this report, due to the requirements of the aero engine industry, the focus is on supply
chain planning and execution. Logistics management is considered only briefly, although
distribution management and downstream logistics (i.e. warehousing and transportation
systems) are the most important functionalities for many SCM users. Functionality has been
summarised, based on information from the websites of leading software companies who
deliver SCM software (www.sap.com, www.jdedwards.com, www.oracle.com, www.idex.com,
www.i2.com and www.manugistics.com).

6.3. SUPPLY CHAIN PLANNING
Supply chain management software will typically support three planning activities; demand
planning, production and distribution planning, and production scheduling. Each is described
in the subsections that follow.

6.3.1. Demand planning
Increasingly complex supply chains have made it difficult for an individual company to
forecast demand for products. Demand planning and inventory modelling are key issues in
planning deliveries and shipments, which is an important area of SCM for distribution and
logistics companies. Demand planning involves forecasting uncertain events and planning
under uncertainty for a constrained environment in which both the supplier and customer can
exercise only limited control. More competitive and rapidly-changing market environments
exacerbate the situation. Hence, an accurate demand forecast and planning system is very
important. Improved forecasts can not only improve customer satisfaction, but also increase
sales and reduce costs through reducing inventory and stock-outs. Many SCM applications
provide sophisticated demand management functions, considering various factors that may
affect future demand, and proposing the most appropriate forecasting model for products.
The forecasting information so created has a direct impact on both production and
distribution planning.

6.3.2. Production and distribution planning
This functionality provides optimised top-level production planning for each product,
considering product mix, plant capacities, and cost structures for the entire supply network.
Transportation resources are considered, to optimise the entire distribution network and
reduce overall transportation costs. The outputs of the production and distribution planning
module will be integrated with the detailed in-company production scheduling.



6.3.3. Production scheduling
This functionality aims to create detailed, feasible, production schedules with associated
material requirement planning, even for very complex products with deep BOM structures,
considering dependences between manufacturing stages. It takes account of the
manufacturing constraints, such as utilisation rates, capacities, capabilities, working time and
etc.

6.4. SUPPLY CHAIN EXECUTION
At the execution stage, supply chain management software typically supports procurement
and inventory management, order management and manufacturing execution. Each is
described in the subsections that follow.

6.4.1. Procurement and inventory management
This functionality provides for the management of ordering and inventories, plus evaluation of
supplier performance such as current supplier capacities, capabilities, cost and lead times.
Where inter-company agreements allow, real-time access can be given to current stock
levels, expected delivery levels and delivery time, which may be critical to suppliers. A
company can also track order processes, as well as inbound and outbound inventory. One
of main objectives of this module will be to ensure that all the raw materials and components
required for manufacturing are available in the right place at the right time, with the minimum
inventory possible.

6.4.2. Order management
The fast-changing demands of customers operating in competitive business environments
are making supply chains and processes more complex than before. Specialist order
management functionality allows coordination with multiple supply channels and distribution
centres. Complex and configuration orders can be managed. Orders can be managed and
tracked throughout the order life cycle.

6.4.3. Manufacturing execution
This functionality allows management and coordination of the material, capacity and other
constraints which impact on manufacturing. Many applications support different types of
manufacturing arrangements: engineer-to-order, build-to-order, make-to-order, assemble-to-
order and stock-to-order. This module will also have the ability to share information with
supply chain partners, to coordinate production.

6.5. LOGISTICS MANAGEMENT
Logistics management consists of warehouse and distribution management, which are less
important to the aerospace industry, but perhaps the bulk of ‘supply chain modelling’
software is aimed at this transportation management need. Purchasing, manufacturing and



sales orders systems may be directly connected and integrated with warehouse
management. Incoming products from purchasing and manufacturing will be stored for quick
retrieval for future use, and outgoing products for sales orders will be withdrawn from the
warehouse. For these materials movements, various functionalities are included, such as the
use of different measures, bulk movement and bar coding. Routing of movements within the
warehouse can be optimised and additional tasks can be managed, e.g. labelling, kitting and
packaging. Transportation management provides the most efficient and cost-effective
method, through determination of the best transportation mode, carrier and routes with
lowest total delivery cost. Orders can be consolidated so that loads and shipments can be
built based on customer, region and product.



7. INTRODUCTION TO SIMULATION
The term “simulation” is a broad one, referring to wide range of methods. The common factor
is the intention to investigate the behaviour of some real system by experimentation with a
model, rather than with the real system itself. In most cases simulation involves the use of
computers, exploiting the spectacular improvements in hardware and software over recent
decades.
In simulation the model is not solved by mathematical analysis, but by computing the
progress of variables over time. It follows from this characteristic that simulation does not
provide a definitive solution, but that experiments must be carried out with a variety of
different input values to obtain corresponding outputs. Further statistical analysis is then
frequently required to evaluate the results obtained. Simulation also offers the possibility of
repeating the same experiment a number of times - a scientific concept not normally available to
the management scientist [Pidd, 1988].
Simulation requires the preparation of a simulation model: some representation of the real
world process or system that it is desired to imitate. The simulation model may take many
forms, depending on the simulation technique used. Simulation models can represent real
systems in their “as-is” state, or proposed changes to the system - “what if” scenarios. The
consequences of changes are therefore easy to assess without expensive and time-consuming
real-life trials. There are many different types of simulation tool available, from domain specific,
event-based discrete simulations, through continuous simulation, to general-purpose
mathematical tools and simulation languages.
The use of a model of a system, rather than the system itself, has a number of important
advantages [Carson, 2003]:
• Speed: many months or years of activity in the real system can be modelled in seconds
or minutes;
• Cost: it is often prohibitively expensive to experiment with the real system. Use of a
computer model is relatively inexpensive;
• Safety & convenience: simulation often involves investigation of a system’s behaviour
under unknown conditions, which could cause major problems in the real system;
• Prospective investigation: if the real system has not yet been built then experimentation
with a model is the only possibility.

Once a suitable model of the system has been built, experiments can be carried out to
investigate how the real system might behave. The results of these experiments can
significantly aid understanding of how the real system will behave. Possible alternative
strategies for operation of the real system can be evaluated and compared.

7.1. TYPES OF SIMULATION
There are many different types of simulation model. Although every specific model has its
own unique characteristics, it is useful to classify different models according to the following
three factors (Kelton et al [2002], page 9):



• Static or dynamic: In static models the fundamental conditions do not change with time,
whereas in dynamic models there may be various changes over time.
• Discrete or continuous: In a continuous model the changes that take place can be
considered as happening gradually and continuously over time (and hence could be
plotted as smooth curves). In contrast, a discrete model considers changes which
happen discontinuously at points in time.
• Deterministic or stochastic: Deterministic models have no random inputs: all the
conditions and parameters are considered to be known with certainly. Stochastic
models take account of the fact that in reality there are frequently factors that are
uncertain and variable (such as the arrival of customer orders, the occurrence of
machine breakdowns). Such factors are modeled using random sampling from
appropriate probability distributions.

It is possible for models to incorporate factors from both sides of these distinctions, but
normally one or the other will dominate the simulation approach. In the case of most
applications of simulation in manufacturing and operations management the appropriate
models are dynamic, discrete and stochastic.

7.2. BEST PRACTICE SIMULATION METHODOLOGY
The process of building and developing a model, and hence also the processes of validation
and verification, involves an incremental, iterative procedure. It is very unlikely that the
correct model, and a correct computer version of the model, will be produced first time.
Instead there is commonly a process of gradual development and improvement of the model,
with validation and verification required at each stage.
Simulation is a widely used tool for reconfiguration of existing systems and design of new
systems, and it involves a complicated programming effort. However, a successful simulation
study consists not only of computer programming, but should also consider various other
aspects in order to achieve its aims.
Law and McComas [1990] suggest that a successful simulation project requires the following
ingredients:
• Knowledge of simulation methodology and operations research.
• Choosing appropriate simulation software and utilizing it correctly.
• Modelling system randomness in a reasonable manner.
• Establishing model validity and credibility
• Using statistical procedures correctly for interpreting simulation output.
• Employing good project management techniques.

The development of a simulation model should follow a logical, systematic process; that is, a
series of steps should be followed. Law and McComas [1990], and Law [2003] explain the steps
in a successful simulation study as follows:
• Formulating the problem and planning the study



• Collecting the data and defining the model
• Validation
• Constructing a computer model
• Verification
• Determining run parameters of the simulation
• Performing simulation experiments
• Analysing output data

Each of these activities is described in the subsections that follow.

7.2.1. Formulating the problem and planning the study
Before beginning to develop a simulation model, it is very important to have clear-cut objectives.
Once the objectives are established, the scope of the simulation can be tailored to answer only
necessary questions. Time and effort required to complete the model can be greatly reduced if
the user concentrates only on fulfilling the objectives.

The following issues should be completed before commencing detailed simulation modelling:
• Identify any performance problems for the existing system.
• State the study’s overall objectives and specific issues.
• Decide how the model will be used in the decision making process.
• Determine the model’s end user.
• Specify measures of performance.

7.2.2. Collecting the data and defining the model
To build a conceptual model, the analyst should have sufficient data about the problem in order
to develop the mathematical and logical relationships in the model for it to adequately represent
the problem entity for its intended use. The data should be collected on system operating logic
and characteristics before modelling the system. The required data depends on how much
detail of the system will be included in the model. Data are collected on two levels: The data that
is collected from system operating characteristics are used to specify the model input
parameters and probability distributions. The second type of data is behaviour data on the
problem entity to be used in the operational validity step of comparing the system performance
behaviour with the model’s performance behaviour. The data collection step is often a difficult
task because the simulation analyst needs different data sources, such as different people
involved in the system, and different documents.



7.2.3. Validation
Despite the many benefits of modelling and simulation, there are also various significant
difficulties to be addressed. The most important issue arises from the fact that simulation
involves experimentation on a model, and not on the real system of interest. If the results of
a simulation are to be of any value to obtain insight into the real system it is vital to ensure
that the model is a sufficiently good representation of reality. Because a model is (by
definition) different in some respects from the real system it represents it is vital that the
model is built such that the model’s behaviour reflects that of the real system sufficiently well
for the specific purpose.
The processes of validation and verification are the quality assurance tools of simulation. If
properly used they can minimise the probability of costly errors being made as a result of
wrong conclusions being drawn from a model that does not adequately represent the system
of interest.
Validation is an attempt to answer the question: “does this model (whether in its verbal,
diagrammatic or computer form) adequately represent the real system of interest for the
specific purposes of the study?”
Since any simulation model is only an abstraction of the real system being studied, the analyst
should always retain a healthy scepticism about how the model coherently represents the real
system (Hoover and Perry [1989], pp 277).
Validation is part of the total model development process and is itself a process. This process
consists of performing tests and evaluations within the model development process to determine
whether a model is valid or not. It is a confirmation that the model is a credible representation of
the real system. Usually it is not feasible to determine that a model is absolutely valid over the
complete domain of its intended application. Instead, a sufficient degree of confidence is enough
for validation of the model.
One of the basic processes of validation is an attempt to duplicate in the model a set of
known conditions in the real system. For example a manufacturing process is being
simulated then the model can be run under known conditions, and the outputs from the
model compared with the historical outputs from the real system. If a good match is obtained
then the model may be considered valid, at least for that particular set of conditions.
A fundamental difficulty of validation is that simulation is normally employed to gain
understanding of a system under conditions that have not (yet) occurred in reality. This basic
type of comparison validation clearly cannot be carried out for such conditions. The best that
can be achieved is to test the model under a combination of known conditions and
predictable extreme conditions. If it behaves well and predictably under such conditions then
the user can have some confidence that the model’s behaviour under unknown conditions is
also likely to be a good match to the real system. However, there will always be a margin for
error, and a model can therefore never be considered to be 100% validated.

7.2.4. Constructing a computer model
In a simulation study computer modelling of the system is an inevitable task of model
development and solution procedure. This can be done with either general-purpose
programming languages or simulation packages. The level of detail, model validity,
execution time etc. largely depend on the chosen software.



7.2.5. Verification
Verification is the process of attempting to ensure that the computer representation of the
model (i.e. the embodiment of the logical model in computer code and data) is a correct
representation of the logical model itself. This is essentially a process of ‘debugging’ the
computerised model to identify and correct any ‘bugs’ or errors that have been incorporated
in the process of computerisation.
While validation is related to system’s credible representation, verification is associated with the
computer coding of the model. It is the process of determining whether the computer coding of
the model corresponds to the model logic or not. Program verification must be performed in
order to ensure the analyst’s confidence. Although in validation of a model the people who are
involved in the system play a significant role, the verification step can be performed only by the
modeller. Verification is as much art as science, so there is no one method of performing it.
Compared with the problems of validation, verification is a relatively clear cut process.
Although if the model (and hence its computer representation) is complex there will always
be some probability that some bugs remain undetected, the essential comparison that
verification involves is much more concrete than that involved in validation.

7.2.6. Determining run parameters of the simulation
The performance measures that are produced by a simulation is not the measure itself, but only
a statistical estimate of it. The variance and bias of the statistical estimator heavily depend on
the choice of following factors:
• Length of each simulation run.
• Number of independent simulation runs
• Initial conditions for each simulation run.
• Length of warm-up period, if one is appropriate.
Thus, it will be necessary to populate any models developed with a reasonable set of
commitments, stocks, work-in-progress etc. and perhaps allow the model to settle into a
steady state before attempting to gather data from a simulation run.

7.2.7. Performing simulation experiments
The parameters determined in the previous stage are used for a series of experimental runs
of the model, and corresponding results are collected for subsequent analysis.

7.2.8. Analysing output data
In simulation studies it often happens that insufficient effort is made to analyze the simulation
output data appropriately, although a great deal of time is spent on the model development and
programming (Law and Kelton, [1991], pp 522). Since a simulation analysis is usually performed
for a system under stochastic operating conditions, the performance measure estimation that is
obtained from only one simulation run may be far from the true parameter because it may have
large variance. More accurate estimation is obtained by the mean of multiple run results
produced with different random number streams.



Figure 11 summarises the procedure for model development:

Figure 11: Procedure for model development

7.3. SUPPLY CHAIN SIMULATION
Simulating the operations of an extended enterprise could offer all the benefits normally
associated with simulation (see Section 7.0); the supply chain configuration could be
proposed early in the lifecycle of the product – almost at the design stage – allowing the
viability of the enterprise to be tested. A simulation of this kind would allow the limitations of
the enterprise to be explored, and might identify sources of waste or delays. Calculations
possible with such models could allow safety stock levels to be investigates, taking into
account the cost of holding stock, and the cost of stock-outs.
In the following chapter, software tools that might be employed for supply chain simulation
are reviewed.



8. SOFTWARE SELECTION FOR STATE-OF-THE-ART SUPPLY
CHAIN SIMULATION
For this part of the state-of-art review, a simulation software evaluation exercise has been
conducted to make suggestions on possible software platforms to be used for Tasks 2.5.1
and 2.5.3.
The approach adopted consisted of the following steps; firstly, a literature survey was
conducted, on simulation concepts, simulation software overviews, descriptions and
comparisons. Experimentation followed, with simulation software likely to be used, especially
Arena, Simul8, eMPlant, Extend; these were benchmarked, suggestions were made.
After an initial investigation of different types of simulation software (Discrete, continuous and
system dynamics) (Carson [2003], Ingalls [2002], Kirkwood [1998], Lyneis [1999]) and
different levels of simulation software sophistication (Spreadsheet [Seila, 2003], high level
programming languages like c++ and commercial simulation software), it was decided to
concentrate on commercial discrete (or combined discrete and continuous), stochastic
process oriented simulation software that can be used for supply chains.
The initial list of possible discrete event simulation software was produced, based on the
typical applications of the software. This was then significantly reduced by removing those
obviously not suitable for our purposes. More detailed characteristic/feature benchmarking
was then carried out for the remaining candidates.
Although no clear winner emerged from the final evaluation, a few packages did meet the
requirements for simulating an aeronautical supply chain. These were suggested as a
shortlist for final selection. To aid this selection, the critical features required for WP2.5 were
discussed in further details.

8.1. LITERATURE SURVEY ON DISCRETE-EVENT SIMULATION SOFTWARE
The sixth biennial survey of simulation software for discrete-event systems simulation and
related products [Swain, 2003] provides a good starting point for our evaluation. This survey
summarizes important product features of software packages, including price. Products that
run on desktop computers to perform discrete-event simulation have been emphasized.
There are 48 products listed in that survey.
Simulation News Europe (http://www.argesim.org/comparisons/index.html) has also
published a number of case studies that provide solutions based upon two or more
simulation products. Two of the case studies are of particular relevance: case study 2, for
‘Flexible Assembly System’, compares features for sub-model structures, control strategies,
and optimization of process parameters; case study 14, for ‘Supply Chain Management’,
addresses discrete modelling and simulation.
A recent selection methodology developed for the Accenture world-wide simulation team
[Tewoldeberhan et al, 2002], provides a framework in which users can evaluate products in
seven areas (e.g., model development, animation) as the basis for deciding upon a suitable
tool. The user can score competing products in the seven areas and use those scores as the
basis for making an informed decision about product choice.



Besides, Schriber and Brunner [2003], provide simulation practitioners and consumers with a
grounding in how discrete-event simulation software works. April et al [2003] provide a
“practical introduction to simulation optimization” and how different optimization methods
work and are implemented. More detailed information on individual simulation software
products can be found from software overviews, introductions, invited papers, presentations
and product websites.

8.2. THE EVALUATION AND ELIMINATION PROCESS
In the above surveys, there are 48 products listed in the sixth biennial survey (some are
different versions from the same vendor), 32 products (some overlapping with the 48 above)
on the Simulation News Europe website related to supply chain/logistics. Other papers and
web search also produced additional ones.
To reduce this initial list to a finalist list of several most suitable software packages, we
followed a three-stage process:
• Initial cut off
• Selection of packages best suited to the aeronautical supply chain
• Detailed evaluation of short-listed software

8.2.1. Initial cut off
This step removes all packages which could be used for continuous simulation only or not
suitable for supply chain simulations based on the typical applications of the software and
their primary markets.
After this step, the following list of remaining software was obtained: Analytica, AnyLogic,
Arena, AutoMod, Crystal Ball, DESMO, DecisionPro, eM-Plant, Enterprise Dynamics, Extend,
Factory Explorer, Goldsim, Flexsim, GPSS/H, HighMAST, MAST, PCModel, MicroSaint,
PIMSS, ProcessModel, ProModel, Quest, Resource Manager, ShowFlow, SIGMA, SimCAD
Pro, SIMUL8 , Slam, SLIM, SLX, Supply Chain Builder, TOMAS, VisSim, Visual Simulation
Environment, Witness.

8.2.2. Selection of packages best suited to the aeronautical supply chain
Based on the information from Swain [2003], product websites and the hard criteria
screening process as described in Tewoldeberhan et al [2002], each product was cross
checked against each of the hard criterion for Tasks 2.5.1 and WP2.5.3. If a product fails a
hard criterion, it is further removed.
After step 2), only the following eleven packages were retained for further detail evaluation:
AnyLogic, Arena, AutoMod, Enterprise Dynamics, Extend, Flexsim, ProModel, Quest,
SIMUL8, Witness.

8.2.3. Detailed evaluation of short-listed software
Similar criteria as described in Tewoldeberhan et al [2002] were used, namely



• Vendor: includes vendor pedigree, maintenance support, and documentation
• Model development and input: includes the model building and coding aspect, batch
processing, library of reusable modules, conditional routing, statistical distributions,
queue policy, input modes, automatic documentation, batch input modes, random
number generator, standard commands, model packaging
• Execution: includes multiple run, automatic batch run, reset capability, start in non-
empty state, interaction with user and unit conversions
• Simulation and optimization engines
• Animation: includes icon and animation development, screen layout and animation
running
• Testing and efficiency: includes validation and verification tools, display features,
tracing, step functions, breakpoints, model size and model speed
• Output: includes report, integration with external packages and business graphics
• User: required experience and cost of package
• Experimental design use of tools that can help to model correctly.

The results from the evaluations carried out by Tewoldeberhan et al [2002, 2004] were used
as a starting point. These were expanded and modified in two ways; firstly the additional
criterion of ‘simulation and optimisation engine’ was added to the table, then additional
columns were added for additional packages. The result is shown in Table 1. (Weighting is
on a 0-10 scale and individual scores are on a 0-3 scales.)



Enterprise
Criteria Weight AnyLogic Arena AutoMod Dynamics Extend Flexsim ProModel Quest Simul8 Witness

Vendor 5.6 1 3 2.5 2 2.67 2 2 3 2.33 3
Model
develop. &
input 9.5 3 2.71 2.3 2.57 2.71 2.7 2 3 2.43 2.5
Simulation &
optimization
engine 8 2.5 2.5 2.7 2.6 2.5 2.5 2.7 2.5 2.5 2.5
Execution 7.6 2 2 2 2.33 2.33 2 2 2.5 2 2
Animation 6.3 2.5 2.67 3 2.33 1.33 3 2.67 3 1 3
Testing &
efficiency 7.6 2 2.38 2.5 2.38 2.5 1.5 2 2.5 1.75 2
Output 6.6 2.5 2.33 2 1.67 2.33 2.7 2 2 2.67 2
Experimental
design 5.9 2 3 2 2 2 2 3 2 2 2
User 5.6 1 2 2 1.5 2.5 1.5 2 1 3 2.5
Total 124.2 156.9 138.8 138.1 147.0 140.4 141.1 152.8 137.2 148.9
Rank 1 4 5 2 3
Table 1: Simulation package evaluation scores

8.3. DETAILED DISCUSSION OF CRITICAL SOFTWARE FEATURES FOR WP2.5
From Table 1, the five packages with the highest scores were retained for detailed analysis.
The subsections that follow describe the qualities that were sought in the simulation software:

8.3.1. Model building using programming (scripting) / access to
programmed modules
It is vital that the selected system allows us to express specific, complex rules within the
models produced. Although many software vendors describe their tools in terms of their ease
of use, with drag-and-drop interfaces and menu-driven configuration, it is almost certain that
we will ultimately need to carry out programming.



8.3.2. Run-time dynamic model reconfiguration
It is anticipated that a model of an extended virtual enterprise will require that the model’s
scripting language (see Section 8.3.1) will allow the model to reconfigure itself. There are two
reasons why a facility of this kind would be desirable.
Firstly, this is an enabler for data-driven simulation, where a product’s bill of materials and
lead times, etc. allow a hypothetical collaborative network of businesses to be constructed in
minutes, much faster and more reliably than a human operator could reconfigure a model.
Secondly, the virtual enterprise is likely to change over time; partners come and go as
different levels of inputs are required at different stages of a project, and different processes
and logistic concepts may become viable at different volumes of manufacture.

8.3.3. Simulation Engine
The quality of simulation results is to a large extent dependent on the simulation engine, the
collection of approaches or procedures responding to particular events.

8.3.4. Optimisation engine
Since we may well be asked how our models might be used to provide ‘optimal’ solutions, the
selected modelling tool should include a module that allows targets to be pursued. (This may
be complicated, given that not all our success criteria appear to be immediately quantifiable,
as Chapter 9 shows.)

8.3.5. Input / output capabilities
Common applications such as those within the Microsoft Office suite allow us the best
opportunity to create models that will be appreciated by the industrial partners. If a model can
be designed in such a way that alternative scenarios can be defined by changing values in a
table or entries in a database, the usefulness of the model is increased tremendously. For
output, the reports generated by the simulation should likewise be compatible with the tools
that are used to edit documents within the University and the partners.

8.3.6. Price
Price is a significant factor, especially when multiple users are involved. All the products in
the final list have different price structures for industry and academic users, with the price for
the latter less than half of that for the former. Product prices include license fees, annual
maintenance fees, additional module costs (which will be needed for the VIVACE project). In
addition, some vendors also have compulsory training costs.
Table 2 gives some indications of the relative strength of the 5 finalist software against each
of the above 6 critical criteria.



Criteria Arena Extend ProModel Quest Witness
Model building using
programming VBA Own VBA etc Own Own
Run-time dynamic model Through Through
reconfiguration VBA VBA Yes
Simulation Engine Good Good Good Good Good
Evolutionary Witness
Optimization engine OptQuest Optimizer OptQuest OptQuest Optimizer
MS Excel,
Input / output ODBC ODBC MS Excel Excel HTML, XML
$495–
Academic price $25,000 $450- $1995 $995 £13500 $ 13500
Table 2: Comparison of critical criteria for the five finalists

8.4. SIMULATION SOFTWARE SELECTION CONCLUSIONS
Based on the total scores in Table 1, and the critical features in Table 2, taking into
consideration of other factors such as familiarity and user base, it seems that the five
products Arena, Quest, Witness, Extend and ProModel all meet the main requirements for
our purpose, although Arena is slightly ahead of the rest. The authors thus suggest that one
of these five be selected.



9. SUPPLY CHAIN PERFORMANCE MEASUREMENT
If the performance of a system cannot be measured, it cannot be efficiently managed.
Unfortunately, it is difficult to select appropriate supply chain performance metrics because of
the complexity of the supply chain and ever-changing business environments. Beamon
[1998] noted that the establishment of appropriate performance measures is an important
element of supply chain design and analysis. An ability to effectively measure supply chain
performance will be critical to any extended enterprise, and to the organisations within.
Since conventional measurement systems may not be valid beyond organisational
boundaries, a new performance measurement system is required. Lambert and Pohlen
[2001] summarised the need for new types of metrics for SCM as follows:
• The lack of measures that capture performance across the supply chain
• The requirement to go beyond internal metrics and take a supply chain perspective
• The need to determine the interrelationship between corporate and supply chain
performance
• The complexity of SCM
• The requirement to align activities and share joint performance measurement
information to implement strategy that achieves supply chain objectives
• The desire to expand the “line of sight” within the supply chain
• The requirement to allocate benefits and burdens resulting from functional shifts within
the supply chain
• The need to differentiate the supply chain to obtain a competitive advantage
• To goal of encouraging cooperative behaviour across corporate functions and across
companies in the supply chain

It is generally known that properly designed performance metrics give rise to more
opportunities to identify and eliminate problems, and to meet customer expectations. On the
other hand, inappropriately designed metrics will result in failure to respond to the customer.

9.1. PERFORMANCE MEASUREMENT FRAMEWORKS
A number of performance measurement frameworks and related metrics have been
proposed. Beamon [1998] classified performance metrics into two categories; qualitative
metrics for which there is no single direct numerical measurement, and quantitative metrics
that may be directly described numerically. Qualitative measures include customer
satisfaction, flexibility, information and material flow integration, effective risk management
and supplier performance. Quantitative measures include measures based on cost and
measures based on customer responsiveness. This author also developed a new framework
for performance measurement. Within this framework, a supply chain performance
measurement system that consists of a single performance measure is generally inadequate,
since it is not inclusive and ignores the interactions among important supply chain
characteristics. Key strategic elements in the organisation include the measurement of



resources, output and flexibility. Therefore, as shown in Figure 12, a supply chain
measurement system must put emphasis on three separate types of performance measures:
resource measures (R), output measures (O) and flexibility measures (F). Each of the three
types of measures has important characteristics and interacts with others.

Figure 12: The supply chain measurement system [Beamon, 1999]

Beamon believed that an effective supply chain performance measurement system must
contain at least one individual measure from each of the three identified types shown in
Table 3.

Table 3: Goal of performance measure types [Beamon, 1999]

Gunasekaran et al [2001] classified performance metrics into four groups along the four links
of an integrated supply chain, named as follows: plan, source, make/assemble and
delivery/customer. Measures for plan include the order entry method, order lead-time and
the customer order path. Measures for source include supply chain partnership and related
metrics such as the level of information sharing and buyer-vendor cost saving initiative.
Measures for make/assemble include the range of products and services, capacity utilisation
and effectiveness of scheduling techniques. Lastly, measures for delivery/customer include
measures for delivery performance evaluation such as on-time delivery, and measures for
total distribution cost. A final class of metric addressing customer satisfaction was added.
The framework is illustrated in Figure 13.



Figure 13: Metrics at 5 basic links in a supply chain [Gunasekaran et al, 2001]

Gunasekaran et al [2003] extended this framework to include a temporal dimension. Metrics
for each of the four processes were further divided into strategic, tactical and operational
metrics.
Chan and Qi [2003] developed a performance measurement framework for the supply chain
with a process-based approach, as Figure 14 shows. In this model, a process in the supply
chain is a series of activities from original suppliers and manufactures, through to retailers,
which add value for the end customers, each performing a specific set of functions. The
performance of each process is the aggregated results of the performance of all preceding
activities. Therefore, assessing the performance of activities can depict the effect of
corresponding processes. Based on the model, the authors proposed a ‘metrics board’ of
performance measures, covering inputs and outputs, both tangible and intangible. The
metrics board include cost, time, capacity, capability (effectiveness, reliability, availability,
and flexibility), productivity, utilisation and outcome. When identifying new performance
metrics, all the related dimensions in the metrics board can be considered.



Figure 14: Applying supply chain metrics based on process [Chan and Qi, 2003]

Hausman [2002] emphasized that businesses need to migrate from single-dimensional
measures to multi-dimensional ones and from a single-enterprise focus to a cross-enterprise
focus. He identified that Supply Chains need to perform on three key dimensions:
• Service
• Assets
• Speed

Hausman also stressed that businesses using multi-dimensional performance measures
should recognize that not all dimensions are equally important, and some tradeoffs are
necessary. Understanding tradeoffs and as a result, knowing how to set priorities and targets
is crucial. An example of an important tradeoff is the balance between inventory level and
customer service. Figure 15 shows such a balance before product localization postponement
(also called ‘late customization’) and after postponement.



Figure 15: Tradeoff curve for inventory and service [Hausman, 2002]

Hofman [2004] described AMR Research’s three-tiered Hierarchy of Supply Chain Metrics
(Figure 16) and a top-down approach of executive assessment, diagnosis and identification
of corrective action.



Figure 16: The AMR Research hierarchy of supply chain metrics [Hofman, 2004]

The CorVu white paper “Improve Supply Chain Performance” [CorVu, 2002] describes a list
of supply chain metrics based on the four perspectives of the balanced scorecard method:
financial, customer, internal, and innovation and growth.
It is perhaps the Supply-Chain Council [2004] that set the industry standard with its Supply
Chain Operations Reference (SCOR) model, which integrate the well-known concepts of
business process reengineering, benchmarking, and process measurement into a cross-
functional framework. SCOR contains:
• Standard descriptions of management processes
• A framework of relationships among the standard processes
• Standard metrics to measure process performance
• Management practices that produce best-in-class performance
• Standard alignment to features and functionality

Figure 17 illustrates the SCOR model’s three-level structure:



Figure 17: Supply Chain Operations Reference model, showing three levels of process detail
[Supply Chain Council, 2004]

The system is not ideal for all supply chains, however. Dutta [2004] described some of its
present limitations, explicitly excluding sales and marketing (demand generation), research
and technology development, product development and some elements of post-delivery
customer support. All of these have some impact and influence on supply chains.
In summary, a performance measurement framework should strive to include metrics from
each different category in the following dimensions:



• The transformation dimension [Beamon, 1998]: The resource measures (R), output
measures (O) and flexibility measures (F)
• The business process dimension [Supply-Chain Council, 2004]: top level five distinct
management processes: Plan, Source, Make, Deliver, Return. This is then drilled down
to important 2nd level and 3rd level processes.
• The business excellence dimension [Chan and Qi, 2003]: cost, time, capacity, capability
(effectiveness, reliability, availability, flexibility), productivity, utilisation and outcome
• The management level dimension [Gunasekaran et al, 2001]: strategic, tactical and
operational

9.2. PERFORMANCE METRICS BENCHMARKING AND INTERRELATIONSHIPS
Christopher [1998] outlined the need for supply chain/logistics benchmarking, what to
benchmarking (based on the SCOR model), supply chain processes mapping and setting
benchmarking priorities.
Kleijnen and Smits [2003] summarized how economic theory differs from business practices
in the treatment of multiple metrics. Economic theory tends to use scoring methods such as
Kiviat graphs, empirical utility measurement, uncertain attribute values, mathematical
programming (including goal programming), fuzzy set theory etc. In practice managers use
multiple performance measures; a single measure does not suffice.

9.3. PERFORMANCE MANAGEMENT
Cokins [2004] of the SAS institute described performance management as a framework that
tightly integrates the business improvement and analytic methodologies executives are
already familiar with. These include strategy mapping, balanced scorecards, costing
(including activity based cost management), budgeting, and forecasting, and resource
capacity requirements.
Miller [2000] identifies the key issues that companies face in addressing supply chain
performance improvements as the capture of critical quantitative performance data across
(and between) functions, plus qualitative insight into supplier and partner relationships.
Toni and Tonchia [2001] found that the main performance management systems in the
literature can be grouped into five categories:



1. Models that are strictly hierarchical (or strictly vertical), characterized by cost and
non-cost performances on different levels of aggregation, till they ultimately
become economic-financial.
2. Models that employ a balanced scorecard or tableaux de bord, where several
separate performances are considered independently; these performances
correspond to diverse perspectives (financial, internal business processes,
customers, learning/growth) of analyses.
3. Models that can be called ‘frustum’, where there is a synthesis of low-level
measures into more aggregated indicators, but without the scope of translating
non-cost performance into financial performance.
4. Models that distinguish between internal and external performances.
5. Models that are related to the value chain.

9.4. PERFORMANCE MEASUREMENT FOR SUPPLY CHAIN LOGISTICS IN THE PROJECT
As described in Section 6.1, there are several measurement frameworks and classification
systems for performance metrics in recent literature. Generally, performance measurement
is based upon the firm’s strategy, aiming to support the implementation and monitoring of
strategic initiatives. For the purposes of WP 2.5 it is necessary to define a framework within
which the efficiency of a simulated supply chain can be assessed. A measurement strategy
with three categories of metric has been adopted. These are cost, customer service and
capability, each described in the subsections that follow.

9.4.1. Metrics based upon cost
Cost will clearly be a metric for supply chain performance measurement because cost is
incurred in so many activities. Cost-effectiveness can be achieved through efficiency in
resource utilisation. The cost of inventory will be important, including the costs of raw
materials, work-in-progress, finished goods and obsolescence; mean inventory level and
stock turnover rates are appropriate performance measures.
Examples of cost-based metrics for supply chain performance measurement include:
• Total cost: Total cost of resources used through the supply chain
• Manufacturing cost: Total cost of manufacturing, including labour, maintenance,
scrap and rework costs
• Distribution cost: Total cost of distribution, including transportation, warehousing and
handling costs
• Stock Turns: Indicates the comparison between the total number of items in stock
and their usage rate. Operational efficiency regarding inventory level can be measured
through the stock turnover. Inventory across the entire supply chain might be used, or
just finished product inventory



9.4.2. Metrics based upon customer service
One of the top-level aims of the enterprise will be to achieve a high level of customer
satisfaction. One of the principal services would be to provide a required product at the right
place on time. Hence, product availability and the delivery performance will both be
important measures. Availability of products can be assessed through metrics such as fill
rate, backorder rate and stock-out rate. Some researchers claim that reliability is more
critical than fast delivery as collaboration and cooperation become key issues across the
supply chain, and on-time delivery will become a critical measure of customer service. In
addition, reduction in lead-time will reduce response time and reduce uncertainty, so that
customer satisfaction will also be improved.
Examples of supply chain metrics based upon customer service include:

• On-time delivery rate: Percent of orders delivered on time or before the due date
• Delivery lead-time: Lead time typically refers to the amount of time elapsed from
order, until delivery to the customer.
• Manufacturing lead time: Amount of time elapsed to produce a required product or
batch
• Fill rate: The rate of orders filled immediately from stock
• Backorder rate: Percent of items backordered due stock-out
• Stock-out rate: Percent of being out of stock for a requested item

9.4.3. Metrics based upon Capability
Chan and Qi [2003] define capability as a talent or ability to be used, treated or developed for
specific purposes and required functions. They suggest it is the aggregate ability by which
an activity or process functions. In other words, it is not a separate measure, but a measure
combined with other dimensions such as measures on cost and customer service, as
explained previously. In particular, rapidly-changing customer expectations and supply chain
dynamics make capability a very important measure because companies have to respond to
market requirements and the business environment. The detailed capability metrics
proposed are as follows:

• Capacity: The maximum number of a product that can be delivered by using available
resources over a specified time period.
• Efficiency (Productivity): The rate of output against the cost of resources.
• Reliability: The ability to perform the required functions (i.e. producing and delivering a
required product/service) over a time period (Manufacturing reliability, delivery
reliability, transportation reliability and order processing reliability are specific
performance measures).
• Flexibility: The ability to respond to changes and new requirements. Beamon [1999]
identified four sub-types of flexibility: volume flexibility for a product, delivery flexibility,
flexibility for producing a variety of products, and new product flexibility.



• Quality: The ability to perform a required process without mistakes. Detailed quality
measures include the number of customer complaints, proportions of scrap and rework
and frequency of engineering changes and concessions, etc.

9.4.4. Metrics for tasks 2.5.3 and 2.5.1
For Task 2.5.3, it is proposed that a weighted multi-criterion profile is developed, based on
the above measures. The weighted profile will be used to assess the performance of various
supply chain scenarios, as simulated. Weightings will be adjusted and validated to provide a
robust and realistic performance assessment system.
The weighted measure should also be used jointly with a set of Key Performance Indicators
(KPI), which is a small subset of the above measures. Due to the nature of the aerospace
supply chain (e.g. the predominant risk and revenue sharing agreements), the KPIs selected
for Task 2.5.3 consist of delivery lead-time, WIP costs, and manufacturing reliability. These
can be calculated as follows:

Delivery lead time = ordering lead time + manufacturing time + delivery time;
Manufacturing reliability = (operating time - downtime) / (operating time);

WIP costs = material costs + holding cost.

(All these KPIs will be averaged over a certain time period.)
For task 2.5.1, we introduce the concept of measuring total supply chain performance via
cross-enterprise metrics. These metrics will still be based on the three categories but on a
much simpler basis.
In addition to the two similar measures of total supply chain cost and supply chain cycle time,
we also introduce a new supply chain resilience/robustness measure, similar to that
proposed in [RLSN, 2003]; the supply chain cycle time resilience ratio. This new measure is
defined as:

Supply chain cycle time under variation
Normal supply chain cycle time

This measure would reflect supply chain’s capability to respond to variations, especially step
changes. The closer to 1, the more resilient the supply chain is.



10. PROPOSED SIMULATION WORK
Within the work package, UNOTT staff are working to construct simulations that represent
industry operations at two levels:
• Supply chain logistics (Task 2.5.1)
• Internal logistics (Task 2.5.3)

It is desired that the experimental models under development should demonstrate some
interdependency, such that the internal logistics of a single business within the supply chain
can be investigated in detail, and the effect of any changes observed could then be passed
to the supply chain logistics model, yielding further data about the performance of the
extended enterprise.
The simulation work to be undertaken within the modelling tasks is detailed in Sections 10.1
and 10.2 respectively.

10.1. SUPPLY CHAIN LOGISTICS MODELLING
The network modelling approach described in Section 5.2.3 provides a useful way to
represent the extended enterprise, with each node representing a supplier or customer with
whom the focal company has entered into a relationship. Each business is defined in key
terms such as the stock of finished goods it holds, and the time it takes to produce a batch of
products. Similarly, the interconnections between nodes are assigned properties,
representing the cost and delay incurred during transportation.

10.1.1. The case for data-driven simulation
In a data-driven simulation, the model is constructed not by a user defining a model step-by-
step using the user interface of the simulation tool, but automatically by a software program.
This program makes use of a stored array of information – a document such as a
spreadsheet or database file. Of particular interest is the relational database that exists at the
heart of a Supply Chain Management (SCM) tool, since it might be possible to interrogate
this directly, saving time and making use of high-quality information. Alternatively, a
database representing a detailed Bill of Materials might be used, as is demonstrated within
the UNOTT Supply Chain Modelling software (The document ‘VIVACE
2.5/UNOTT/T/04//005-0.1’ provides a comprehensive description.)
The advantage of a data-driven simulation approach is that the model can be reconfigured
rapidly, by changing the external document. Theoretically, a person could explore the
implications of radical changes to a simulated system with little knowledge of the simulation
software itself.
In the case of WP 2.5.1, a data-driven simulation would mean that a database of supplier
information could be interrogated, and a corresponding set of nodes would be created within
a network model, each node with appropriate properties representing the relevant capabilities
of the supplier.



Data-driven simulation requires that a program capable of interrogating external sources of
information be written, and thus requires a powerful simulation tool; hence the detailed
investigation into the qualities of the leading commercial products. Experiments now being
undertaken at UNOTT are exploring the possibilities described here.
This matches companies’ demands for responsiveness; a data-driven simulation allows
virtual enterprise concepts modelled rapidly, for evaluation without requiring a lengthy model-
construction phase for each concept. The initial workload in terms of software development
will be much greater than for a single model, but the result should demonstrate a state-of-the-
art approach to responsiveness in supply chain planning.

10.1.2. Simulation scope and architecture
Since the overall VIVACE integrated project has such a large number of stakeholders, it is
preferable that any solution developed should be scalable, permitting application to primes
and tier three suppliers alike.
With the exception of raw material suppliers whose source of supply is the earth itself,
complexity can be expected at every stage in the supply chain, and analysis will yield
benefits in terms of competitiveness for the virtual enterprise as a whole. As Kamm [1996]
identified, “everybody buys components, but sells systems,” … it is common to assume that
upstream processes are somehow simpler or less deserving of study than those nearer the
customer. The data entered into the simulation will vary at each focal company, but external
workflow simulation has the power to eliminate a great deal of cost, and lead time.

10.2. INTERNAL LOGISTICS MODELLING
The manufacture of the turbine exhaust casings (TECs) for the PW2000 and V2500 engines
has been selected for simulation work with WP 2.5.3. Initial modelling will involve the
construction of an ‘as-is’ model. The current setup at VAC involves a focused factory driven
by an MRP system, though a closer look reveals that, like most real-life manufacturing
systems, it is something of a hybrid. By no means are all the manufacturing processes found
within the focused factory; some of the operations the TEC requires are undertaken at
shared resources, and some products from outside the focused factory will sometimes dip
into the capacity of the area that is to be our case study. Furthermore, in terms of scheduling,
the system is not purely a ‘push’ system, including local solutions that demonstrate some
aspects of a ‘pull’ system, and also of period batch control.
Task 2.5.3 requires that alternative production layouts, and alternative logistic concepts be
evaluated. Figure 18 shows in the form of a matrix, the total range of options that have been
considered. (Some have been discounted as irrelevant to an aero engine manufacturing
context, leaving those marked with an ‘X’ as requiring further investigation.)



Figure 18: Matrix of production and logistic concepts evaluated

The VIVACE document WP2.5/UNOTT/T/04013-0.1: ‘Production layouts and logistics
concepts for the conceptual supply chain’ describes each of the layouts and logistic concepts
in detail.

10.3. DATA COLLECTION METHODOLOGY FOR THE SIMULATIONS
Simulation can be carried out for multiple periods, and replicated as required. With sufficient
replications this can be used to produce average results, and to show the degree of
variability in the model. In the context of the extended enterprise model, this means that risk
can be measured, expressed in terms of probability distributions that indicate the likelihood of
a failure to meet a delivery date.
As any metrics gathered in this way will be averaged, sensitivity analysis should be carried
out, especially for Key Performance Indicators (KPIs).

10.4. METRIC CALCULATION AND AGGREGATION
It is proposed that metrics should be calculated and aggregated from simulation results in the
following way:
• Collection of relevant results
• Calculation of individual metrics for a certain period
• Recording of individual metrics over multiple periods
• Summing up metrics in the same category, e.g. lead times
• Normalizing metrics in each categories to a scale, e.g. from 0 to 10
• Application of weightings to the metric categories, as appropriate
• Calculating the single, aggregated metric



The normalization method proposed is based on a linear 0–10 scale. It appeals to one’s
imagination and makes readability and interpretation of actual metric values easy. Two steps
need to be taken for normalizing the metric scores:
Setting performance targets––The target is the starting point for defining the metric score
range that corresponds with the 0–10 scale.
Normalizing scores to a 0–10 scale––a target will lie somewhere between 0 and 10. Since
consistency is recommended when using a normalized scale, the values 0–10 should always
have a same meaning, regardless of the metric under study.
Aggregation means nothing more than calculating an average of the normalized scores. This
may or may not be a weighted average. Agreement on the relative importance (weightings) of
the metrics for the aggregation process will be sought among the partners.

10.5. THE BALANCED SCORECARD
The balanced scorecard concept (The Balanced Scorecard Institute [2004], Rohm [2002])
attracted a lot of attention as a means of broadening performance
measurement/management initiatives. It cascades strategies into operational activity
measures, balancing the financial and non-financial perspectives. It also emphasizes that to
develop meaningful performance measures one has to understand the desired outcomes and
the processes that are used to produce outcomes.
For the purposes of VIVACE, this approach is of interest due to:
• The straightforward tabular form of representing metrics
• The display of relationships between different measures.

For Task 2.5.3 it is proposed to adapt and simplify the approaches as described by Rohm
[2002], and Lohman et al [2002]. A structure with two layers is used for displaying the
information, described in the subsections that follow.

10.5.1. Performance on the aggregated level
A scorecard table is used on this level to include the following information: the performance
metric categories, individual metrics, corresponding weightings, simulated metrics values,
weighted and normalized values for categories, absolute benchmarks, relative benchmarks
and finally a single weighted and normalized multi-criterion metric. Table 4 shows a prototype
of such a simplified scorecard as it might appear for Task 2.5.3:



Category Metrics Weighting Simulated Weighted Absolute Relative Normalized
(0-10) values values benchmark benchmark value
(0-10)

Cost

Customer
Service

Capability

Aggregation
Table 4: Production / Logistics Metrics Scorecard

10.5.2. Performance on the detailed level
The three key performance indicators for task 2.5.3 (delivery lead-time, WIP costs and
manufacturing reliability) can be plotted on two-dimensional graphs. The vertical axis
represents the simulated values, the absolute and relative benchmarks. The Horizontal axis
represents the time. Graphs will also be plotted to show trade offs between the KPIs. An
example graph for cost is shown in Figure 19.



60

50
Simualted
40 Value
Absolute
30
benchmark
20 Relative
benchmark
10

0
1st 2nd 3rd 4th
Week Week Week Week

Figure 19: Sample cost metrics graph

Interrelationships between the KPIs can be explored with simple models [Persson and
Olhager, 2002] such as:

y = Ax α .
where y and x and individual KPIs and A and α are parameters to be decided..
Metrics for task 2.5.1 can be treated in a similar way.

10.6. INCORPORATING PERFORMANCE METRICS IN PERFORMANCE MANAGEMENT
The purpose of this study is to compare different logistics and production concepts for sub-
task 2.5.3 and the effect of supply chain structure on the performance of extended supply
chain for sub-task 2.5.1. Thus the selection of metrics is limited to these areas. To evaluate
full enterprise performance, other metrics may be needed to avoid sub-optimizing from
focusing on parts of the supply chain.



11. CONCLUSIONS
Beginning with an overview of the modern manufacturing business environment for a high-
technology product, this document has set out the case a collaborative enterprise, and has
discussed the means by which a competitive supply chain might be modelled, measured and
managed. A précis of the literature survey work conducted by UNOTT staff has been
included, identifying the current state of the art in all these areas.
Commercial tools have been reviewed, and strategies for modelling work to be undertaken
within the project have been discussed.
The key findings from this stage of the work are that two different forms of simulation should
be attempted within the project. Task 2.5.1 calls for a high-level extended enterprise model
that can rapidly be reconfigured to allow a variety of supply chain configurations to be
modelled, while Task 2.5.3 requires a more detailed simulation in which a facility’s layout,
and the logistic concepts by which work is controlled, can be the subject of experimentation.



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