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Simulation in the supply chain context - a survey_266106186 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...

Simulation in the supply chain context - a survey_266106186
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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