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, Italy
bDepartment 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: sergio.terzi@polimi.it (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 the
supply 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-operative
Simulation
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.
6 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16
� 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.
S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 7
Table 1
Literature survey—local simulation paradigm
P
a
p
e
r
s
A
l
f
i
e
r
i
a
n
d
B
r
a
n
d
i
m
a
r
t
e
[
3
2
]
A
r
c
h
i
b
a
l
d
e
t
a
l
.
[
2
8
]
B
a
g
c
h
i
e
t
a
l
.
[
1
9
]
B
e
l
h
a
u
e
t
a
l
.
[
2
4
]
B
e
r
r
y
a
n
d
N
a
i
m
[
5
0
]
B
o
t
t
e
r
e
t
a
l
.
[
2
5
]
B
u
r
n
e
t
t
a
n
d
L
e
B
a
r
o
n
[
5
1
]
C
a
v
a
l
i
e
r
i
e
t
a
l
.
[
5
2
]
C
h
e
n
e
t
a
l
.
[
4
0
]
H
a
f
e
e
z
e
t
a
l
.
[
5
4
]
H
i
r
s
c
h
e
t
a
l
.
[
2
3
]
I
n
g
a
l
l
s
e
t
a
l
.
[
5
5
]
J
a
i
n
e
t
a
l
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