Supply chain simulation tools and techniques: a survey <s> INTRODUCTION <s> Numerous algorithms and tools have been deployed in supply chain modeling and problem solving. These are based on stochastic models, mathematical programming models, heuristic techniques, and simulation. Since different decision problems in supply chains entail different approaches to be used for modeling and problem solving, there is a need for a unified approach to modeling supply chains so that any required representation can be created in a rapid and flexible way. In this paper, we develop a decision support system DESSCOM (decision support for supply chains through object modeling) which enables strategic, tactical, and operational decision making in supply chains. DESSCOM has two major components: (1) DESSCOM-MODEL, a modeling infrastructure comprising a library of carefully designed generic objects for modeling supply chain elements and dynamic interactions among these elements, and (2) DESSCOM-WORKBENCH, a decision workbench that can potentially include powerful algorithmic and simulation-based solution methods for supply chain decision-making. Through DESSCOM-MODEL, faithful models of any given supply chain can be created rapidly at any desired level of abstraction. Given a supply chain decision problem to be solved, the object oriented models created at the right level of detail can be transformed into problem formulations that can then be solved using an appropriate strategy from DESSCOM-WORKBENCH. We have designed and implemented a prototype of DESSCOM. We provide a real-world case study of a liquid petroleum gas supply chain to demonstrate the use of DESSCOM to model supply chains and enable decision-making at various levels. � 2002 Elsevier B.V. All rights reserved. <s> BIB001 </s> Supply chain simulation tools and techniques: a survey <s> INTRODUCTION <s> This survey paper starts with a critical analysis of various performance metrics for supply chain management (SCM), used by a specific manufacturing company. Then it summarizes how economic theory treats multiple performance metrics. Actually, the paper proposes to deal with multiple metrics in SCM via the balanced scorecard — which measures customers, internal processes, innovations, and finance. To forecast how the values of these metrics will change — once a supply chain is redesigned — simulation may be used. This paper distinguishes four simulation types for SCM: (i) spreadsheet simulation, (ii) system dynamics, (iii) discrete-event simulation, and (iv) business games. These simulation types may explain the bullwhip effect, predict fill rate values, and educate and train users. Validation of simulation models requires sensitivity analysis; a statistical methodology is proposed. The paper concludes with suggestions for a possible research agenda in SCM. A list with 50 references for further study is included. <s> BIB002 </s> Supply chain simulation tools and techniques: a survey <s> INTRODUCTION <s> Abstract Supply chain management (SCM) has been a major component of competitive strategy to enhance organizational productivity and profitability. The literature on SCM that deals with strategies and technologies for effectively managing a supply chain is quite vast. In recent years, organizational performance measurement and metrics have received much attention from researchers and practitioners. The role of these measures and metrics in the success of an organization cannot be overstated because they affect strategic, tactical and operational planning and control. Performance measurement and metrics have an important role to play in setting objectives, evaluating performance, and determining future courses of actions. Performance measurement and metrics pertaining to SCM have not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of SCM performance measurement and metrics. Using the current literature and the results of an empirical study of selected British companies, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area. <s> BIB003 </s> Supply chain simulation tools and techniques: a survey <s> INTRODUCTION <s> This paper describes the integration of two commercially available, highly specialized, off-the-shelf software packages--CPLEX and ARENA. We present the need for such integration and the ways in which it differs from the more traditional methods of problem solving. The two products are described in detail followed by an example that requires this integration. A detailed account of the integration is also presented together with drawbacks of this approach. <s> BIB004
Simulation analysts may want to quantify the benefits resulting from supply chain management (SCM), in order to support decision making at two levels: • the strategic level, including (re)designing a supply chain • the operational level, including setting the values of control policies. BIB002 distinguish four simulation types for SCM: • spreadsheet simulation • system dynamics (SD) • discrete-event dynamic systems (DEDS) simulation • business games. Spreadsheets may be part of production control software. SD simulation may explain the bullwhip effect. DEDS simulation may predict fill rate values. Business games may educate and train users ('games' must be distinguished from application). In Section 2, I summarise these four simulation types and their role in SCM. From the viewpoint of methodology, I distinguish four types of issues in simulation (in SCM and in other application domains): • validation and verification (V & V) • sensitivity or 'what-if' analysis • optimisation • robustness, risk or uncertainty analysis. To address these four methodological issues, a variety of techniques may be used. I, however, focus on the use of statistical methods for the design of experiments (DOE). In Section 3, I describe these methods and illustrate their application through a case study detailed in Persson (2004a, 2004b) . DOE is important in simulation, because, by definition, simulation is an experimental method; i.e., the analysts experiment with different input values and different model structures (representing different policies, etc.) of the simulation model, treated as a black box (by definition, a black box means that only the inputs and outputs of that box are observed, not its internal operation). A point to note is that some methods do not treat the simulation model as a black box; examples are perturbation analysis and score function methods; see  . Unfortunately, these methods require that more mathematical conditions are satisfied, and that analysts are mathematically sophisticated. In practice, simulation is a method that is relatively often used as compared with other quantitative models. Several reasons may explain this popularity: no mathematical sophistication is needed (see the preceding paragraph), multiple responses are natural in simulation (in SCM, these responses may be the fill rate or service percentage, stocks including work in progress or WIP, sales, etc.). These various responses are discussed by BIB003 and BIB002 . Simulation may give insight into the causes and effects of the supply chain performance: which inputs (or factors) significantly affect which outputs? Indeed, simulation can help in understanding causality, as simulation is a methodology that does not treat a system (for example, a supply chain) as a black box (DOE treats the simulation as a black box, as we saw above). For example, modern simulation software may model individual events such as order arrivals and machine breakdowns in great detail; see  manual for simulation in the Arena software. BIB004 compare Arena and other 'commercial off the shelf' (COTS) software; BIB001 present object-oriented software for simulation models and other model types of supply chains.) The main contribution of this paper is twofold: • it surveys major tools and techniques for the simulation of supply chains • it discusses several methodological issues, including a novel methodology for the robust design of supply chains. The remainder of this paper is organised as follows. The four simulation types that BIB002 distinguish, are discussed in the four separate subsections of Section 2. Section 3 summarises a case study that is detailed in Persson (2004a, 2004b) ; this study illustrates sensitivity analysis, used to derive a shortlist with the most important factors, and robustness analysis, inspired by Taguchi, and bootstrapping to derive a confidence region for the best input values. Section 4 gives the conclusions. A list with 44 references for further study is included.