Supply chain simulation tools and techniques: a survey <s> Overview <s> Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the well-known NETLIB collection. We then apply the Robust Optimization methodology (Ben-Tal and Nemirovski [1–3]; El Ghaoui et al. [5, 6]) to produce “robust” solutions of the above LPs which are in a sense immuned against uncertainty. Surprisingly, for the NETLIB problems these robust solutions nearly lose nothing in optimality. <s> BIB001 </s> Supply chain simulation tools and techniques: a survey <s> Overview <s> Abstract The focus on supply chain management has increased in recent years; among academics as well as among practitioners. In this paper, we present a supply chain simulation study for a real case, concerned with the manufacturing of mobile communication systems. The purpose of the study is twofold. One objective is to evaluate alternative supply chain designs with respect to quality, lead-times and costs as the key performance parameters. A second objective is to increase the understanding of the interrelationships among these and other parameters, relevant for the design of the supply chain structure. The design alternatives differ in terms of the level of integration and synchronisation between supply chain stages. Modelling and validation issues are highlighted since these are fundamental to a supply chain simulation study. The results are discussed and a model capturing the relationships among cost, quality, and lead-times, based on the simulation study, is presented. <s> BIB002 </s> Supply chain simulation tools and techniques: a survey <s> Overview <s> Abstract This paper proposes a new paradigm for tactical demand chain planning (DCP), called robust planning, based on risk assessment of the supply and demand chain. The concepts of supply chain management (SCM), and its extension demand chain management (DCM), have been at the center of much recent research. One of the reasons for this is that, over the last years, a significant number of information systems have emerged, which claim to support the concept. The paper argues that these systems mostly adopt a myopic view of planning, based on pure deterministic planning methods. It demonstrates that such an approach fails to coop with the considerable uncertainty of the planning information. The proposed robust planning paradigm is then introduced and its impact explained, using the well-known example of the beer game. It holds the promise of reducing the number of re-planning cycles, through a better characterization of the expected service level performance over a medium planning horizon. Finally, a case study will show the value of robust planning in a European chemical enterprise. <s> BIB003 </s> Supply chain simulation tools and techniques: a survey <s> Overview <s> Abstract Fast and dramatic changes in customer expectations, competition, and technology are creating an increasingly uncertain environment. To respond, manufacturers are seeking to enhance flexibility across the value chain. Manufacturing flexibility, a critical dimension of value chain flexibility, is the ability to produce a variety of products in the quantities that customers demand while maintaining high performance. It is strategically important for enhancing competitive position and winning customer orders. This research organizes literature on manufacturing flexibility and classifies it according to competence and capability theory. It describes a framework to explore the relationships among flexible competence (machine, labor, material handling, and routing flexibilities), flexible capability (volume flexibility and mix flexibility), and customer satisfaction. It develops valid and reliable instruments to measure the sub-dimensions of manufacturing flexibility, and it applies structural equation modeling to a large-scale sample ( n =273). The results indicate strong, positive, and direct relationships between flexible manufacturing competence and volume flexibility and between flexible manufacturing competence and mix flexibility. Volume flexibility and mix flexibility have strong, positive, and direct relationships with customer satisfaction. <s> BIB004 </s> Supply chain simulation tools and techniques: a survey <s> Overview <s> Abstract Facing a dynamic and complex environment, networked companies often require the coordination of many plants, which produce and deliver goods to customers located in different places, and suppliers, which provide each plant with the required components. A way to optimize the product flows in supply chains (SCs) is to adopt the concept of limited flexibility , that is a particular configuration of product assignments to plants and components to suppliers which can yield many benefits without dramatically increasing the flexibility costs. In this paper, a simulation model is proposed to evaluate the performance of different configurations of a SC. In particular, based on a work-in-process and time performance analysis, the different configurations are analyzed in order to support the selection of suitable flexibility degrees of the operations network. <s> BIB005
The case study consists of three simulation models that represent three alternative designs for a supply chain. Figure 1 illustrates that a newer configuration has fewer operations and tests. Figure 2 shows one of the simulation models-buffers (inventories) are located before and after every test station and operation; products are transported between all operations and test stations. The output is the steady-state mean costs of the total supply chain. Details are given by BIB002 .  derive a shortlist with the most important factors; this process is also called screening. They apply a method called sequential bifurcation (SB). I shall summarise this study in Section 3.3. Next, Kleijnen, Bettonvil and Persson (2004b) derive a robust solution; i.e. they find appropriate values for the factors that management can control, while accounting for the randomness of the environmental factors. Their solution is inspired by Taguchi's approach for designing robust physical products. This means that the important factors are divided into controllable and environmental factors.  systematically investigate these controllable factors (using a reduced so-called central composite design). They randomly combine the environmental factors into scenarios using latin hypercube sampling, LHS. Then they estimate the controllable factor values that minimise the output's expected value and variance, respectively. A confidence region for these optima is derived through bootstrapping. This confidence region can be used to select a (compromise) robust solution. I shall summarise that study in Section 3.4. Note: The SCM literature distinguishes between robustness and flexibility. A flexible supply chain can react to a changing environment by adapting its operations. A robust supply chain keeps its design fixed, and can still accommodate many changes in its environment. So the two Figure 1 The three supply chain structures: (a) the old, (b) the current and (c) the next generation concepts focus on operational and strategic decisions respectively. Also see BIB003 and BIB004 . The effects of flexibility on supply chain performance are evaluated through simulation by BIB005 . Note: Robustness is also important outside DEDS simulation. For example, deterministic simulation-via non-linear difference equations-for computer-aided engineering (CAE) often aims at the optimal design of products such as automobiles, airplanes, etc.; see  . The coefficients of these equations are not exactly known, and the resulting solution cannot be exactly implemented, so the optimal solution should be 'robust'. In mathematical programming, the search for robust solutions has made most progress and reached maturity, I think; see BIB001 .