In 1995, Volkswagen of America began a review of its vehicle-distribution system looking for opportunities to improve customer responsiveness and simultaneously reduce system costs. An analytical tool was required to evaluate alternative designs in terms of cost and customer service level, both of which are functions of probabilistic and dynamic elements. These elements include inventory policies, demand seasonality and volume, customer-choice patterns, and transportation delays. By using an innovative combination of simulation and discrete optimization models, we addressed the problem of analyzing a large number of alternatives efficiently. Our analysis indicated opportunities for significant savings in estimated annual transportation costs, and it provided insights on how to implement the proposed system.

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Manufacturingautomotive powertrain components (engines and transmissions) is a complex task involving the integration of hundreds of components. Simulation is commonly applied in the design and implementation of such production systems. Examples of such systems are the crankshaft machining line, engine final assembly and transmission final assembly, to name a few. Invariably, different engine and transmission subassemblies are machined and assembled on separate systems.The completed sub-assemblies are then assembled to the engine or transmission main assembly. There are many areas within a powertrain assembly plant that show complicated behavior due to the varying nature of manufacturing processes. Not only the variation in process, but the schedules, availability of workers, and the performance of material handling equipment are only few of the factors contributing to the randomness in operation. Test areas where the final assembly is inspected for functionality present an example of such highly random operation. Simulation is a very useful tool for investigating the behavior of such complicated systems. This paper discusses the need for and uses of discrete event simulation in the design of manufacturing systems for powertrain assemblies. The benefits of such applications of simulation are illustrated by using a sample study of the final engine test and repair area.

Automotive manufacturing is a complex task involving several steps of machining and assembly. Typically, larger components of an automobile such as the body, engine etc. are assembled over multiple systems. These large assemblies are transferred from one assembly system to another using conveyors.The conveyor/transfer system serves as a buffer and also serves to sort and re-sequence the components in a form that is required by the downstream operation. This requires the transfer system to be able to 'look ahead' at the requirements for the downstream operation and resequence the assemblies, if necessary. The sortation and re-sequencing part of the conveyor system is called a selectivity bank. The capacity requirement calculation and configuration design of these selectivity banks is difficult due to the randomness in the operation of, and the differences in schedules between the two systems it is connecting. Simulation is a valuable tool that is increasingly being used in the design, testing and upgrading of these systems. This paper presents the typical design issues of such selectivity banks, that are addressed using simulation. A case study is presented to elucidate the concepts and applications. The paper concentrates on automotive manufacturing systems but the concepts presented here are applicable to sortation systems used in several industries.

Chain conveyors are a speeitlc type of conveyor often used in a variety of manufacturing and production applications, such as body and paint shops. These conveyors must typically interface with other types of conveyors such as cross-transfer conveyors, and also Electronics Engineers [IEEE], the Society for Computer Simulation [SCS], and the Society of Manufacturing Engineers [SME]. He serves on the editorial board of the International Journal of Industrial Engineering -Applications and Practice.