System Dynamics

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 competition, managing world-wide procurement and unforeseeable markets, supervising geographically distributed production plants, striving for the provision of outstanding products and high quality customer 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, identifying and enhancing the core activities pertaining to the product value chain, and invested massively in new intra-company information and communication platforms, 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 organisational autonomy of each single unit. This business vision is applied to different industrial processes (e.g. procurement, logistics, marketing, etc.) and implementing different policies (e.g. continuous replenishment, 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 programming techniques and genetic algorithms for solving multi-site or transportation planning problems, or timeseries 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 decision 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 particular, 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 alternatives 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 activities) 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 morphological scheme in corporate logistics, a logistics network requires, among others, alignment of network strategies and interests, mutual trust and openness among tiers, high intensity of information sharing, collaborative 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 scheduling as for example inventory and capacity levels. Hence, from the IT point of view there is the strong requirement to adopt distributed collaborative solutions, which could preserve at the same time the local autonomies and privacy of logistics data. Moreover, these solutions must necessarily be platform independent 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 carried 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 cooperating 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 multiprocessor computing platforms, while distributed simulation (DS) is concerned with execution of simulations on geographically distributed computers interconnected 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 computing 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-participates 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 simulation 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. Therefore, reducing them into a single simulation model is a rough approximation. By preserving the geographic 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, without 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 environment 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 synchronicity 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 interconnected (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 simulation 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 structure 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 standard. A PDS in HLA is named ‘‘federation’’ while participant models are termed ‘‘federates’’. One HLAPDS 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 services for federation execution; the Object Modelling Template (OMT) language, for the specification of communications amongst federates. Within the HLA framework, a distributed simulation is accomplished through a ‘‘federation’’ of concurrent ‘‘federates’’, interacting between themselves by means of a shared data model and federation services (basically time and data distribution management 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 different local models within one overall complex simulation 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 separated 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 objectives 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. Introductive 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 modelling 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 Papers Alfieri and Brandimarte [32] Archibald etal. [28] Bagchi etal. [19] Belhau etal. [24] Berry and Naim [50] Botter etal. [25] Burnett and Le Baron [51] Cavalieri etal. [52] Chen etal. [40] Hafeez etal. [54] Hirsch etal. [23] Ingalls etal. [55] Jain etal. [56] Luo etal. [57] Mielke [58] Persson and Olhager [59] Petrovic [60] Phelps etal. [42] Phelps etal. [61] Promodel [27] Ritchie Dunham and Anderson [37] Siprelle etal. [29] Schunk [26] Van der Vorst etal. [31] Zhang etal. [30] Zhang etal. [38] 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) ModSim IBM SCA IBM SCA Create! Dynamo Arena Automod Java IBM SCA Dynamo LOCOMOTIVE Arena Arena Arena Arena Taylor II General purpose SDI SDI SCGuru SDI Supply Solver General purpose Arena 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. 8 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3 –16 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/CORBA 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. Cavalieri / Computers in Industry 53 (2004) 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 technique 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 possible to notice that all papers illustrating specific simulation tools stress this objective. 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, aiming 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 processes are addressed and which decision levels (strategic, tactic, operative [7]) are pondered in the simulation applications under scrutiny. The classification 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 distribution 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 problem. (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 optimisation [27]. (d) Distribution and transportation planning: Simulation of distribution centres, sites localisation and transport planning, in terms of resources, times and costs; it is one of the most recurrent simulation processes reported 10 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 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 planning) to simulate distribution centres, transport type (train, truck, etc.) and relative management processes (material handling, loading and unloading, etc.). (e) Production planning and scheduling: Simulation processes related to production management. Each logistics node is simulated at its manufacturing layer, as a specific set of machines, cells and lines. Manufacturing planning is implemented by simulation models (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 planning 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 industrial nodes (manufacturers, distributors, 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 commercial simulation tools developed by software vendors 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 conceptual content, since simulation models appear not yet implemented and tested [36–38], or are mainly proposals for new descriptive methodologies supporting the adoption of simulation in supply chain environments [24,31,39,40], or reporting the application of novel simulation paradigms, as web-based simulation [41]. Software description: Papers which explain features of tools specifically created for design and development 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 example, Archibald et al. [28] describe a simulation of a food logistics network aiming to verify the S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 11 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 distributed simulation paradigms is the most experimented 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 University 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 paradigms. 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 complex supply chain processes; on the other hand, general-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 company 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 userfriendly employment has not been yet reached. Certainly, this is due to the major IT complexity that PDS paradigm causes. Among the studies reporting the use of PDS paradigm, it is worthwhile to report two particular experiences. 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 version) interconnected in a more extensive supply 12 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 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 information-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]: ‘‘discrete-event simulation allows the evaluation of operating 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 networks) for evaluating and quantitatively ranking different 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 geographically 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 installing 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 simulation 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 models already exist: no substantial revisions on the simulation 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 synchronisation. 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 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16 13 current scope of application as a support to the decision-making processes of SCM. Their intensive use will certainly contribute to the elimination of the current barriers in the accomplishment of a real integration of logistics networks. By providing a systematic quantitative and objective evaluation of the outcomes resulting from different possible 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-operating strategy with their upstream/downstream nodes rather than following myopically an antagonistic behaviour with them. 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Son, Distributed simulation: an enabling technology for the evaluation of virtual enterprises, in: Proceedings of the 2001 Winter Simulation Conference, 2001. [63] G. Zulch, U. Jonsson, J. Fischer, Hierarchical simulation of complex production systems by coupling of models, International Journal of Production Economics 77 (2002) 39–51. Sergio Terzi is a PhD student of Politecnico di Milano, Department of Economics, Industrial and Management Engineering, Laboratory of Production 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 received 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 lifecycle management within SME and modelling of production systems. Sergio Cavalieri is currently associate professor at the Department of Industrial Engineering of the University of Bergamo. Graduated in July 1994 in management and production engineering, in 1998 he got the PhD title in management engineering at the University of Padua. His main fields of interest are modelling and simulation of manufacturing systems, application of multi-agent systems and soft-computing techniques (genetic algorithms, ANNs, expert systems) for operations and supply chain management. He has been participating to various research projects at national and international level. He has published two books and about 40 papers on national and international journals and conference proceedings. He is currently co-ordinator of the IMS Network of Excellence Special Interest Group on Benchmarking of Production Scheduling Systems and member of the IFAC-TC on Advanced Manufacturing Technology. 16 S. Terzi, S. Cavalieri / Computers in Industry 53 (2004) 3–16