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


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