simulation in the supply chain context: a survey

Simulation in the supply chain context: a survey

Sergio Terzia,*, Sergio Cavalierib

aPolitecnico di Milano, Department of Economics, Industrial and Management Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, ItalybDepartment of Industrial Engineering, Universita di Bergamo, Viale Marconi 5, 24044 Dalmine, Italy

Received 29 January 2003; accepted 13 June 2003

Abstract

The increased level of competitiveness in all industrial sectors, exacerbated in the last years by the globalisation of the

economies and by the sharp fall of the final demands, are pushing enterprises to strive for a further optimisation of their

organisational processes, and in particular to pursue new forms of collaboration and partnership with their direct logistics

counterparts. As a result, at a company level there is a progressive shift towards an external perspective with the design and

implementation of new management strategies, which are generally named with the term of supply chain management (SCM).

However, despite the flourish of several IT solutions in this context, there are still evident hurdles to overcome, mainly due to

the major complexity of the problems to be tackled in a logistics network and to the conflicts resulting from local objectives

versus network strategies.

Among the techniques supporting a multi-decisional context, as a supply chain (SC) is, simulation can undoubtedly play an

important role, above all for its main property to provide what-if analysis and to evaluate quantitatively benefits and issues deriving

from operating in a co-operative environment rather than playing a pure transaction role with the upstream/downstream tiers.

The paper provides a comprehensive review made on more than 80 articles, with the main purpose of ascertaining which

general objectives simulation is generally called to solve, which paradigms and simulation tools are more suitable, and deriving

useful prescriptions both for practitioners and researchers on its applicability in decision-making processes within the supply

chain context.

# 2003 Elsevier B.V. All rights reserved.

Keywords: Parallel and distributed simulation; Supply chain management; High level architecture; Survey

1. Introduction

Modern industrial enterprises operate in a rapidly

changing world, stressed by even more global com-

petition, managing world-wide procurement and

unforeseeable markets, supervising geographically

distributed production plants, striving for the provi-

sion of outstanding products and high quality custo-

mer service.

More than in the past, companies which are not able

to revise periodically their strategies and, accordingly,

to modify their organisational processes seriously risk

to be pulled out from the competitive edge.

In the 1990s, companies have made huge efforts for

streamlining their internal business processes, identi-

fying and enhancing the core activities pertaining to

the product value chain, and invested massively in new

intra-company information and communication plat-

forms, as data warehouse or ERP systems.

In the last years, globally active companies, as well

as SMEs, are realising that the efficiency of their own

Computers in Industry 53 (2004) 3–16

* Corresponding author. Fax: þ39-02-2399-2700.

E-mail address: [email protected] (S. Terzi).

0166-3615/$ – see front matter # 2003 Elsevier B.V. All rights reserved.

doi:10.1016/S0166-3615(03)00104-0

businesses is heavily dependent on the collaboration

and co-ordination with their suppliers as well as with

their customers [1]. This external perspective is termed

in literature under the broad concept of supply chain

management (SCM), which is concerned with the

strategic approach of dealing with trans-corporate

logistics planning and operation on an integrated basis

[2]. Adopting a SCM strategy means to apply a business

philosophy where more industrial nodes along a logistic

network act together in a collaborative environment,

pursuing common objectives, exchanging continuously

information, but preserving at the same time the orga-

nisational autonomy of each single unit. This business

vision is applied to different industrial processes (e.g.

procurement, logistics, marketing, etc.) and imple-

menting different policies (e.g. continuous replenish-

ment, co-marketing, etc.). Integrated management

frameworks (as the SCOR [3] model) support the

development of collaboration among multiple tiers

through mutually designed planning and execution

processes along the entire supply chain (SC).

From the IT perspective, a new wave of solutions is

arising with the main hype to overcome all the physical,

organisational and informational hurdles which can

seriously jeopardise any co-operation effort. Advanced

planning and scheduling (APS) systems aim to step

over the intra-company integration supplied by ERP

systems by providing a common inter-organisational

SCM platform, which supports the logistics chain along

the whole product life-cycle, from its initial forecast

data, to its planning and scheduling, and finally to its

transportation and distribution to the end customer [4].

Despite the various solutions currently available on the

market, the common features of the APS products

reside on the intensive usage of quantitative methods

in order to provide users with the best solution at time.

An example is given by mixed integer linear program-

ming techniques and genetic algorithms for solving

multi-site or transportation planning problems, or time-

series and regressive techniques for demand planning

problems.

Among these quantitative methods, simulation is

undoubtedly one of the most powerful techniques to

apply, as a decision support system, within a supply

chain environment.

In the industrial area, simulation has been mainly

used for decades as an important support for production

engineers in validating new lay-out choices and correct

sizing of a production plant (e.g. [5,6]). Nowadays,

simulation knowledge is considered one of the most

important competences to acquire and develop within

modern enterprises in different processes (business,

marketing, manufacturing, etc.) [7]. Within the Visions

for 2k-enterprises [8], simulation is considered one of

the most relevant key-success factors for companies

surviving, thanks to its predictable features. Several

organisations consider simulation as an essential deci-

sion support system, for example, since 1996, the USA

Department of Defence (DoD) has been asking to all its

services and parts suppliers to furnish a simulation

model of the product/service provided [9].

In particular, as the topic of the paper, supply chain

is a typical environment where simulation (in parti-

cular, discrete-event simulation) can be considered a

useful device. In fact, it is quite evident to find out

how, by using simulation technology, it is possible to

reproduce and to test different decision-making alter-

natives upon more possible foreseeable scenarios, in

order to ascertain in advance the level of optimality

and robustness of a given strategy.

Aim of the paper is to survey how simulation

techniques (in particular, discrete-event simulation)

could represent one of the main IT enablers in a supply

chain context for creating a collaborative environment

among logistics tiers.

After an introduction to simulation specifications

and terminology (Section 2), a detailed literature

review is proposed (Section 3) in order to analyse

the scope of use, the paradigms employed and the

main benefits reported from the adoption of simulation

techniques in the supply chain context. In Section 4,

final considerations from the authors are provided.

2. The role of simulation techniques in thesupply chain context

Despite the great emphasis given in the last decade

on the need for companies to smooth their physical

boundaries in favour of a more integrated perspective,

there is often among practitioners a lot of confusion

and a flawed use of the term ‘‘integration’’.

Stevens [10] provides a framework for achieving an

integrated supply chain, highlighting that integration

of logistics functions requires a progressive evolution

from intra-company functional integration (i.e. change

4 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16

from a functional to a process view of internal activ-

ities) to an internal corporate logistics integration

(supported by ERP, DRP systems), and finally to an

external integration in a logistic network extended

upstream to suppliers and downstream to customers.

The last step is undoubtedly the most challenging

one. However, in addition to the classical morpholo-

gical scheme in corporate logistics, a logistics network

requires, among others, alignment of network strate-

gies and interests, mutual trust and openness among

tiers, high intensity of information sharing, collabora-

tive planning decisions and shared IT tools [1].

These requirements represent often the major hurdles

inhibiting the full integrability of a logistics chain: even

in presence of a strong partnership and mutual trust

among logistics nodes, there are in practice evident

risks of potential conflict areas of local versus global

interests and strong reluctance of sharing common

information related to production planning and sche-

duling as for example inventory and capacity levels.

Hence, from the IT point of view there is the strong

requirement to adopt distributed collaborative solu-

tions, which could preserve at the same time the local

autonomies and privacy of logistics data. Moreover,

these solutions must necessarily be platform indepen-

dent and easily interfaceable with companies’ legacy

systems.

These requirements are profoundly changing also

the traditional paradigms underlying the world of

simulation. In literature, there is a progressive shift

of research and application works from local, single

node simulation studies to modelling of more complex

systems, as logistics channels are.

Generally, simulation of such systems can be car-

ried out according to two structural paradigms: using

only one simulation model, executed over a single

computer (local simulation), or implementing more

models, executed over more calculation processors

(computers and/or multi-processors) in a parallel or

distributed fashion [11].

Consequently, a simulation model of a supply chain

can be designed and realised either traditionally as a

whole single model reproducing all nodes (Fig. 1), or

using more integrated models (one for each node),

which are able to run in parallel mode in a single co-

operating simulation (Fig. 2).

Fig. 1. Local simulation paradigm.

Model

Model Model

Model

Co-operativeSimulation

Fig. 2. Parallel and distributed simulation paradigm.

S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 5

The next section will be mainly addressed to the

specification of the parallel and distributed simulation

(PDS) paradigms.

2.1. The parallel and distributed simulation

paradigms

Parallel discrete-event simulation (PS) is concerned

with the execution of simulation programs on multi-

processor computing platforms, while distributed simu-

lation (DS) is concerned with execution of simulations

on geographically distributed computers intercon-

nected via a network, local or wide [11]. Both cases

imply the execution of a single main simulation model,

made up by several sub-simulation models, which are

executed, in a distributed manner, over multiple com-

puting stations. Hence, it is possible to use a single

expression, PDS, referred to both situations.

PDS paradigm is based upon a co-operation and

collaboration concept in which each model co-parti-

cipates to a single simulation execution, as a single

decision-maker of a ‘‘federated’’ environment.

The need of a distributed execution of a simulation

across multiple computers derives from four main

reasons [9,11,12].

� To reduce execution simulation time: A large simu-

lation can be split in more models and so executed

in a shorter time.

� To reproduce a system geographic distribution:

Some systems (as supply chain systems or military

applications) are geographically distributed. There-

fore, reducing them into a single simulation model

is a rough approximation. By preserving the geo-

graphic distribution, the execution of a PDS over

distributed computers enables the creation of virtual

worlds with multiple participants that are physically

located at different sites.

� To integrate different simulation models that

already exist and to integrate different simulation

tools and languages: Simulation models of single

local sub-systems may already exist before

designing a PDS (e.g. flight simulators in military

application, but also local production systems in a

supply chain context) and may be written in

different simulation languages and executed over

different platforms. By using a PDS paradigm, it is

possible to integrate existing models and different

simulation tools into a single environment, with-

out the need to adopt a common platform and

language and to re-write the models.

� To increase tolerance to simulation failures: This is

a potential benefit for particular simulation systems.

Within a PDS, composed by different simulation

processors, if one processor fails, it may be possible

for others processors to go on with simulation runs

without the down processor.

PDS paradigm derives from studies that academic

laboratories and also military agencies have been

realising since 1970. These studies can be classified

according to Fujimoto [11] in two major categories.

� Analytic simulation: This type of simulation is used

to analyse quantitatively the behaviour of systems. In

this case, PDS paradigm is applied to execute as fast

as possible the simulation experimental campaigns.

� Distributed virtual environment: A virtual environ-

ment is composed by more simulation applications

that are used to create a virtual world where humans

can be embedded for training (e.g. soldiers training

in battlefields) and also for entertainment (e.g.

distributed video games) purposes.

In recent years, PDS paradigm has been mainly

used in military applications, but also in several civil

domains (e.g. navy in [13], emergency management in

[14], transportation in [15]).

PDS practical execution needs a framework, which

enables to model the information sharing and synchro-

nicity among single local simulations. In literature, it is

possible to distinguish two different PDS frameworks,

separated by their basic co-ordination logic.

� A network structure, based on a distributed protocol

logic, in which single nodes are mutually intercon-

nected (Fig. 3a).

� A centralised structure, founded on a centric logic,

in which a single process manager is responsible for

linking participant nodes (Fig. 3b).

For the purposes of the paper, it is possible to

synthesise the two frameworks as follows.

� Distributed protocols map interaction messages that

each participant model sends continuously to other

nodes, to bring their update of proper simulation

state. MPI-ASP [16] and GRIDS [17] are examples

of distributed protocols logic.


� The centric logic provides a software instrument

that is able to receive standard messages from each

connected node, and, therefore, to sort out needed

communications between single participant simula-

tion nodes.

The last logic, as it will be possible to understand

by the following literature survey, is becoming the

most widely used, since it clearly divides connection

and model activity. In fact, in a PDS centric struc-

ture a user is only interested in the model creation,

while the central software solves all connection

problems.

High level architecture (HLA) [12] is the most

known PDS framework. HLA is a standard PDS

architecture developed by the US DoD for military

purposes and nowadays is becoming an IEEE stan-

dard. A PDS in HLA is named ‘‘federation’’ while

participant models are termed ‘‘federates’’. One HLA-

PDS is based on the ‘‘federation and federate rules’’,

which establish 10 ground rules for creating and

managing the simulation. In particular, 10 ‘‘rules’’

identify:

� the HLA interface specification, that defines ser-

vices for federation execution;

� the Object Modelling Template (OMT) language,

for the specification of communications amongst

federates.

Within the HLA framework, a distributed simula-

tion is accomplished through a ‘‘federation’’ of con-

current ‘‘federates’’, interacting between themselves

by means of a shared data model and federation

services (basically time and data distribution manage-

ment services). The federation services are provided

by the Run Time Infrastructure (RTI) software tool,

compliant to the HLA interface specification.

2.2. PDS and supply chain simulation

Many software vendors (e.g. i2 in [18], or IBM in

[19]), universities and consultancy companies have

traditionally used a local simulation approach in the

supply chain context. Only in recent years, some of the

features of PDS were recognised as important benefits

for enabling sound simulation models in support of

SCM policies [20,21].

� PDS ensures the possibilities to realise complex

simulation models which cross the enterprise

boundaries without any need of common sharing

of local production system models and data; as

previously discussed, companies that do not belong

to the same enterprise might not be willing to share

their data openly. Gan et al. [22] explain that PDS

paradigm guarantees the ‘‘encapsulation’’ of differ-

ent local models within one overall complex simu-

lation system, so that, apart from the information

exchanged, each model is self-contained.

� PDS provides a connection between supply chain

nodes that are geographically distributed throughout

the globe, guaranteeing that each single simulation

model is really linked to its respective industrial site.

� In some cases, the execution of a PDS model allows

to reduce the time spent for simulation, since sepa-

rated models run faster than a single complex model.

3. Literature survey

The survey has been conducted over the scientific

literature in order to ascertain which general objec-

tives simulation is generally called to solve, using

which paradigms and simulation tools or languages,

and derive useful prescriptions both for practitioners

and researchers on its applicability in decision-making

processes within the supply chain context.

More than 80 papers have been reviewed. Intro-

ductive papers on supply chain simulation were also

analysed, but they are not classified within the tables.

Reader may note that the survey considers only papers

and references that propose applications of supply

chain simulation, as (i) industrial test cases, or (ii)

simulation software specifically designed for model-

ling supply chains or (iii) simulation tests conducted

over a logistics network.

Fig. 3. PDS frameworks.


Table 1

Literature survey—local simulation paradigm

Pap

ers

Alf

ieri

and

Bra

nd

imar

te[3

2]

Arc

hib

ald

etal

.[2

8]

Bag

chi

etal

.[1

9]

Bel

hau

etal

.[2

4]

Ber

ryan

dN

aim

[50

]

Bott

eret

al.

[25

]

Burn

ett

and

Le

Bar

on

[51

]

Cav

alie

riet

al.

[52

]

Chen

etal

.[4

0]

Haf

eez

etal

.[5

4]

Hir

sch

etal

.[2

3]

Ing

alls

etal

.[5

5]

Jain

etal

.[5

6]

Lu

oet

al.

[57

]

Mie

lke

[58

]

Per

sso

nan

dO

lhag

er[5

9]

Pet

rov

ic[6

0]

Ph

elp

set

al.

[42

]

Ph

elp

set

al.

[61

]

Pro

mo

del

[27

]

Rit

chie

Du

nh

aman

dA

nd

erso

n[3

7]

Sip

rell

eet

al.

[29]

Sch

un

k[2

6]

Van

der

Vors

tet

al.

[31]

Zh

ang

etal

.[3

0]

Zh

ang

etal

.[3

8]

Scope and objective

Objective

Network design

Design � � � � � � � � � � � � � � � � �Localisation � � � � � � � � �

Strategic decision

Management archetype � � � � � � �Strategic model � � � � � � �

Process

Demand and sales planning � � � � � � � �SC planning � � � � � � � � � �Inventory planning � � � � � � � � � � � � � � �Distribution and

transportation planning

� � � � � � � � � � � � � � � �

Production planning

and scheduling

� � � � � � � � �

Morphology

SC ownership

SC single ownership � � � � � � � � � � � � � � � � � � �SC multi-ownership � � � �

SC levelsa Na 2 Na 2 2þ Na Na 2 Na 2þ Na Na Na 2þ Na Na Na Na Na Na Na Na Na Na 2þ Na

Simulation paradigm and technology

Local � � � � � � � � � � � � � � � � � � � � � � � � � �Specific tool � � � � � � � �General tool � � � � � � � � � � � � � � �

Other (simulation tools

and languages)

Mod

Sim

IBM

SC

A

IBM

SC

A

Cre

ate!

Dy

nam

o

Are

na

Au

tom

od

Jav

a

IBM

SC

A

Dy

nam

o

LO

CO

MO

TIV

E

Are

na

Are

na

Are

na

Are

na

Tay

lor

II

Gen

eral

pu

rpo

se

SD

I

SD

I

SC

Gu

ru

SD

I

Su

pp

lyS

olv

er

Gen

eral

pu

rpo

se

Are

na

Development stageb Ex, Cn Ex Sw Ex, Cn Ex Ex Ex Ex Cn Ex Ex Ex Ex, Cn Ex Ex Ex Ex, Cn Sw Sw Sw Cn Sw Sw Ex, Cn Ex Cn

a Na means information not available.b Cn: conceptual; Sw: software; Ex: experience; Ts: testing.

8S

.Terzi,

S.

Ca

valieri/C

om

pu

tersin

Ind

ustry

53

(20

04

)3

–1

6

Table 2

Literature survey—parallel and distributed simulation paradigm

Papers

Barnett and

Miller [39]

Brun et al.

[35]

Gan et al.

[16]

Gan et al.

[22,33,43]

Gan et al.

[21,44]

Gan and

McGinnis [53]

Kim et al.

[36]

Seliger

et al. [48]

Strasburger et al.

[34,45,46]

Sudra et al.

[17]

Ventateswaran

et al. [62]

Zulch

et al. [63]

Scope and objective

Objective

Network design

Design � � �Localisation

Strategic decision

Management archetype

Strategic model

Process

Demand and sales planning �SC planning � �Inventory planning � �Distribution and transportation planning � � � � �Production planning and scheduling � � �

SC features

SC ownership

SC single ownership

SC multi-ownership � � � � � � � � � �

SC levelsa Na 2 2 2 2 Na Na 2 2 Na 2 Na

Simulation paradigm and technology

PDS � � � � � � � � � � � �Network logic � � �Centric logic � � � � � � � � � � (�)

Other (simulation tools and languages,

PDS frameworks)

HLA HLA (WILD)MPI-HLA DP HLA HLA DEVS/COR-

BA

HLA HLA GRIDS HLA Osim

Development stageb Cn Ts, Ex Ts Ts Ts Ts, Cn Cn Ts Ts Ts Ts Cn

a Na means information not available.b Cn: conceptual; Sw: software; Ex: experience; Ts: testing.

S.

Terzi,S

.C

ava

lieri/Co

mp

uters

inIn

du

stry5

3(2

00

4)

3–

16

9

The survey makes use of a chart classification and its

results are summarised in Tables 1 and 2. Before

detailing the content of the tables, it is necessary to

introduce the classification criteria adopted.

3.1. Classification criteria

Three classification criteria have been adopted for

categorising the reviewed articles.

� Scope and objectives: It is related to the specific

context, the objectives and the scale of the problem

(strategic, tactic, operative) the simulation techni-

que was addressed to.

� Simulation paradigm and technology: It states the

simulation paradigm (e.g. local versus distributed

simulation) and the simulation tools and languages

adopted.

� Development stage: It refers to the different levels

of development of the simulation application

reported in the articles (from the conceptual level

to testing activities or commercial applications).

3.1.1. Scope and objectives

This classification driver is further structured in

three sub-criteria: (1) objectives, (2) processes, (3)

morphology.

(1) Objectives: It is possible to highlight two macro

objectives.

(a) Network SC design: Simulation can be used

as a decision support system within the

design phases (e.g. design of a logistics

network, design of production nodes). Two

sub-levels are defined.

(i) Design: It stands for logical modelling and

industrial nodes configuration. It is possi-

ble to notice that all papers illustrating

specific simulation tools stress this objec-

tive. For example, in Hirsch et al. [23], a

specific supply chain simulation tool,

named LOCOMOTIVE, is adopted to

verify and test more solutions into a

logistic network for packing eco-reusing

and recovery.

(ii) Node localisation: It relates to the

activity of placing a supply chain node

in a determined geographic site. Only a

few simulation models and tools, among

those reviewed, deal with the problem of

geographic disposition of industrial

nodes. For example, in Belhau et al.

[24], a simulation model is conceived in

order to identify the right geographic

disposition for distribution centres, aim-

ing to minimise transport costs through

the use of proper cost functions.

(b) SC strategic decision support: Simulation is

applied over a supply chain to evaluate more

strategic alternatives, as strategies based on

quick response, collaborative planning and

forecasting or outsourcing to third-parties. As

an example, in Botter et al. [25] simulation is

applied on a Brazilian beer logistics network

in order to evaluate the possibility of entirely

outsourcing the logistic process to an external

provider.

(2) Processes: The survey investigates which pro-

cesses are addressed and which decision levels

(strategic, tactic, operative [7]) are pondered in the

simulation applications under scrutiny. The clas-

sification makes use of the same categorisation of

most APS systems [4].

(a) Demand and sales planning: Simulation

processes dealing with stochastic demand

generation (e.g. customer process generation)

and forecasting planning definition.

(b) Supply chain planning: Simulation processes

supporting production planning and distribu-

tion resources allocation, under supply and

capacity constraints; as an example, Schunk

[26] describes a simulation tool, Supply

Solver, which is interfaced with an external

module, which optimises the solution for

distribution and production allocation pro-

blem.

(c) Inventory planning: Simulation processes

supporting multi-inventory planning; the

commercial simulation tool programmed by

Promodel, SCGuru, proposes, a specific

module for inventory management and opti-

misation [27].

(d) Distribution and transportation planning:

Simulation of distribution centres, sites loca-

lisation and transport planning, in terms of

resources, times and costs; it is one of the

most recurrent simulation processes reported


in literature; for example, as described in

Bagchi et al. [19] and Archibald et al. [28],

IBM supply chain analyser has two separated

modules (distribution and transportation plan-

ning) to simulate distribution centres, trans-

port type (train, truck, etc.) and relative

management processes (material handling,

loading and unloading, etc.).

(e) Production planning and scheduling: Simula-

tion processes related to production manage-

ment. Each logistics node is simulated at its

manufacturing layer, as a specific set of

machines, cells and lines. Manufacturing

planning is implemented by simulation mod-

els (and tools), which integrate different

model layers, from single production lines to

the entire factory and to the whole logistics

chain. SDI Industry Pro [29] is one of the most

important examples of manufacturing plan-

ning implementation; SDI is a simulation tool

specifically developed for logistics chains,

which allows the development of models from

single production machines to more complex

distribution centres.

(3) Morphology: The morphology of the supply

chains addressed by simulation models can be

further refined as follows.

(a) Supply chain ownership: Which distinguishes

two possible conditions.

(i) Single ownership: This is the typical case

of multinational companies, whose in-

dustrial nodes (manufacturers, distribu-

tors, financial sites, etc.) are distributed

all over the world; an example is the case

of IBM and its supply chain analyser [19]

simulation tool specifically developed as

a decision support system for solving

company’s supply chain issues.

(ii) Multi-ownership: In this case, there is a

fair balance of power among more

autonomous enterprises joining a logistics

network. In the LOGSME-ESPRIT 22633

European project, a simulation tool has

been developed in order to support the

decision-making process of a logistics

network made up by SMEs [30].

(b) Supply chain levels: With regards to the

number of tiers along a supply chain, from

the survey, it came out that most of the articles

reviewed do not provide clear information

about the physical dimension of the simulated

systems.

3.1.2. Simulation paradigm and technology

As reported in Section 2.1, there are in literature two

main alternative approaches adopted, with different

choices in terms of tools and languages adopted.

� Local simulation paradigm: With: (i) specific com-

mercial simulation tools developed by software ven-

dors only for simulation purposes within a supply

chain context (e.g. SDI Industry Pro in [29], IBM

SCA in [19], SCGuru in [27], LOCOMOTIVE in

[23], Supply Solver in [26]). (ii) General-purpose

simulation tools or languages, as Arena [25], Create!

[24], CPLEX [31], ModSim [32].

� Parallel and distributed simulation paradigm: With

(i) a network logic approach (e.g. CMB-DIST in

[22], MPI-ASP in [33], GRIDS in [17]); (ii) a

centric logic approach (e.g. HLA in [34,35],

DEVS/CORBA in [36]).

3.1.3. Development stage

From the literature review, it is possible to argue

that the reported simulation studies are at a different

level of development, ranging from as follows.

� Conceptual level: Papers which still denote a con-

ceptual content, since simulation models appear not

yet implemented and tested [36–38], or are mainly

proposals for new descriptive methodologies sup-

porting the adoption of simulation in supply chain

environments [24,31,39,40], or reporting the appli-

cation of novel simulation paradigms, as web-based

simulation [41].

� Software description: Papers which explain features

of tools specifically created for design and devel-

opment of simulation models. Examples of this

category of articles are the two papers presented

at the 1998 and 2000 Winter Simulation Conference

(WSC) by two simulation software development

teams, IBM supply chain analyser [19] and SDI

Industry Pro [42].

� Experience description: Papers which describe real

applications of supply chain simulation. For exam-

ple, Archibald et al. [28] describe a simulation

of a food logistics network aiming to verify the


effectiveness of alternative logistics management

strategies, in particular, the adoption of continuous

replenishment policies.

� Testing activity: Papers which verify simulation

technology portability in a supply chain context.

In particular, in these papers IT platforms and

software solutions are tested. The stability of dis-

tributed simulation paradigms is the most experi-

mented problem, as it appears by the papers

presented by the research groups of the University

of Singapore [16,21,22,33,43,44] and by the Uni-

versity of Magdeburg [34,45,46].

4. Survey analysis

From the literature survey, it is possible to draw

some useful indications for recognising the future

trends of simulation applications in a supply chain

context.

At first, it is important to notice the clear difference

that exists between local simulation and PDS para-

digms. In fact, after this evidence, authors decided to

divide Tables 1 and 2 in local simulation and PDS

experiences. Next considerations are reported having

in mind this first sharp separation.

4.1. Local simulation paradigm

The local simulation paradigm is still the most

applied approach in literature. It is mainly applied

for supply chain network design, but also for verifying

strategic models and management archetypes. The

most implemented simulation processes are related

to distribution, transportation and inventory planning.

With regards to the simulation tools adopted, with

more powerful simulation tools (e.g. IBM SCA and

SDI), based upon modular construction, it is possible

to describe detailed industry models and more com-

plex supply chain processes; on the other hand, gen-

eral-purpose simulation languages guarantee more

programs flexibility, but with more complexity, so

that they appear not suitable for simulation of

multi-tier logistics networks.

In synthesis, the local simulation paradigm:

� is used in many experiences, with heterogeneous

objectives, from supply chain design to strategic

decisions, within several industrial sectors and with

different company scales;

� is often realised, within the industry environment,

with specific simulation tools, whilst academic

users mostly apply general simulation tools;

� is usually applied to a single-ownership supply

chain (e.g. as in the IBM case), while only for

some experiences is applied to a multi-ownership

supply chain, for the main reason that each com-

pany normally is not willing to share its own

simulation models and data with the other tiers of

the network.

4.2. PDS paradigms

The literature survey on PDS applications points out

clearly that PDS paradigm has not become a steady

applied approach and probably, at this time, the critical

research mass for advancing development and user-

friendly employment has not been yet reached. Cer-

tainly, this is due to the major IT complexity that PDS

paradigm causes.

Among the studies reporting the use of PDS para-

digm, it is worthwhile to report two particular experi-

ences.

� The Web Integrated Logistics Designer (WILD)

project [47], conducted by the authors, which makes

use of heterogeneous simulation models, each

reproducing an industrial node of an aeronautical

multi-ownership logistics chain, written in different

languages and intertwined through the use of the

HLA framework; the main objective of the project

was to integrate the local production planning and

scheduling activities at each node by means of

interaction among distributed simulation models;

in each simulation model, local production systems,

production management and scheduling activities

were simulated.

� The Osim project [48], conducted by the University

of Karlsruhe, which aims to create a hierarchical

simulation where more interconnected simulation

models reproduce different ‘‘industrial’’ processes

and layers (production physical cells, production

management, customers, business control, etc.) in

order to model a single supply chain node. These

coupled models could be (not at the present ver-

sion) interconnected in a more extensive supply


chain simulation with models of other industrial

nodes.

Both experiences highlight the increasing attention

of the scientific and industrial community for parallel

and distributed supply chain simulation, which is

being developed in different ways:

� in the research world, it is in a testing phase, above

all for solving IT stability problems;

� there is not yet a sufficient critical research mass for

expanding PDS application;

� it is applied mainly to multi-ownership supply

chains, for their main property to solve any infor-

mation-sharing issue among nodes, thanks to the

provision of a common information bus where each

simulation model, even if written with proprietary

language, can be plugged in and synchronised;

� at IT implementation level, it is possible to observe

an evolutionary trend from a network structure,

based on distributed protocols approach, to a centric

structure, especially based on the HLA standard

framework.

5. Conclusions

According to Chang and Makatsoris [49]: ‘‘dis-

crete-event simulation allows the evaluation of oper-

ating performance prior to the implementation of a

system since: (a) it enables companies to perform

powerful what-if analyses leading them to better

planning decisions; (b) it permits the comparison of

various operational alternatives without interrupting

the real system and (c) it permits time compression so

that timely policy decisions can be made’’.

These features are the common background coming

out from the survey reported in this paper, which

shows how simulation is successfully adopted in

different studies related to logistics network.

In particular, the local simulation paradigm is

preferably used within intra-company supply chain

projects (typical of large multinational logistics net-

works) for evaluating and quantitatively ranking dif-

ferent project solutions or for verifying more strategic

policies.

On the contrary, if the supply chain is composed

by independent enterprises, sharing information

becomes a critical obstacle, since each independent

actor typically is not willing to share with the other

nodes its own production data (as production capacity,

internal lead times, production costs, etc.).

This problem is further exacerbated in geographi-

cally distributed networks. Each simulation model of a

local production site of a company needs be locally

resident on each plant. In fact, the maintenance of the

simulation model cannot be carried out centrally, since

only the technical personnel directly working on the

plant is able to maintain and update it whenever the

plant is subjected to any reconfiguration (like instal-

ling new machines or lay-out modifications).

Unlike local simulation, PDS paradigm fulfils

powerfully these requirements. Within the PDS

approach, each simulation model can run in its own

local environment; the data exchange and, above all,

the synchronisation with the other distributed simula-

tion models are ensured by a shared protocol. Thus, in

a supply chain context, collaborating nodes need only

to define at the beginning which information will be

shared and the time steps or the production events

which will trigger the data exchange.

In addition, each model can be developed with

different simulation tools or languages and executed

on heterogeneous platforms, since the establishment of

the shared network is rather similar to a plug-in tool.

This sounds quite important whenever simulation mod-

els already exist: no substantial revisions on the simula-

tion code need to be produced in order to scale it up from

a local running to a distributed experimentation.

PDS can be implemented with the two frameworks

described in Section 2.1, which propose different

solutions for the two most important PDS problems:

(i) data exchanging and (ii) simulation time synchro-

nisation. From literature survey, it is possible to argue

that the centric logic is becoming the most used

framework. In particular, HLA can be considered

the reference, adopted in several simulation projects

within different domains (military, civil, scientific).

This HLA supremacy derives certainly from the

free distribution policy decided by the USA DoD

(developer of the HLA framework), but it also comes

out from evidence: HLA promises a relative simple

approach to PDS and it guarantees all necessary

devices and support.

Once available the proper IT tools, it is possible to

assert that in the future, simulation models developed

with the PDS approach could better enlarge their


current scope of application as a support to the deci-

sion-making processes of SCM.

Their intensive use will certainly contribute to the

elimination of the current barriers in the accomplish-

ment of a real integration of logistics networks. By

providing a systematic quantitative and objective eva-

luation of the outcomes resulting from different pos-

sible planning scenarios, from demand planning to

transportation and distribution planning, simulation

techniques can make companies more aware of the

benefits coming out from an integrated and co-oper-

ating strategy with their upstream/downstream nodes

rather than following myopically an antagonistic

behaviour with them.

Acknowledgements

The paper reports some of the results achieved by

the authors within the WILD project (refer to its web-

site http://st.itim.unige.it/wild/ for detailed informa-

tion), a project involving seven Italian universities and

funded by the Italian Ministry of Universities

Research & Scientific Technologies. The authors wish

to thank all the collaborating researchers within the

WILD project, in particular, researchers working at

the Dipartimento di Ingegneria Gestionale of Politec-

nico di Milano, namely Prof. Marco Garetti, Prof.

Alessandro Pozzetti, Dr. Alessandro Brun, Dr. Maria

Caridi, Roberto Cigolini and Marco Macchi.

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39–51.

Sergio Terzi is a PhD student of Poli-

tecnico di Milano, Department of Eco-

nomics, Industrial and Management

Engineering, Laboratory of Produc-

tion Systems Design and Management.

He is also taking PhD in conjunction

with CRAN laboratories, University of

Nancy I, France. He received his MSc in

management engineering degrees from

the University of Castellanza in 1999

and from the same university he recei-

ved his BS degrees in economics in 2002. His current research

interests are parallel and distributed simulation applied to industry

and supply chain context, technologies enabling product life-

cycle management within SME and modelling of production

systems.

Sergio Cavalieri is currently associate

professor at the Department of Indus-

trial Engineering of the University of

Bergamo. Graduated in July 1994 in

management and production engineer-

ing, in 1998 he got the PhD title in

management engineering at the Uni-

versity of Padua. His main fields of

interest are modelling and simulation

of manufacturing systems, application

of multi-agent systems and soft-com-

puting techniques (genetic algorithms, ANNs, expert systems)

for operations and supply chain management. He has been

participating to various research projects at national and inter-

national level. He has published two books and about 40 papers

on national and international journals and conference proceed-

ings. He is currently co-ordinator of the IMS Network of Ex-

cellence Special Interest Group on Benchmarking of Production

Scheduling Systems and member of the IFAC-TC on Advanced

Manufacturing Technology.


simulation in the supply chain context: a survey

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