Supply-Chain Analysis at Volkswagen of America

Abstract

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.

A simulation based approach for supply network control

“…Ding et al (2003) also developed a model to simulate various designs of a two-stage distribution network of a company of the automotive sector producing on stock and on order. Karabakal et al (2000) consider the evaluation of different location scenarios of Volkswagen of America's distribution sites.…”

Incorporating uncertainty in optimal decision making: Integrating mixed integer programming and simulation to solve combinatorial problems

“…If service coverage or service reliability is unacceptable, or if the utilization and cost estimates differ significantly from those used in the optimization model, the input parameters are updated and the optimization model is solved again. Karabakal et al (2000) developed a hybrid solution approach that iterates between a simulation and MIP model with an objective to find the optimum configuration for the Volkswagen of America's vehicle distribution system. Two major input parameters to the MIP are demand and truck load factors, both of which depend on the location policy.…”

“…Considering the advantages and disadvantages of these two techniques, some researchers have adopted hybrid approaches incorporating both mathematical optimization and simulation techniques (Acar, 2007;Butler, Karwan, & Sweigart, 1992;Dijk & Sluis, 2006;Lee et al, 2006;Lin et al, 2000;Moore, Warmke, & Gorban, 1991;Muriel, Anand, & Yongmei, 2006;Qi & Bard, 2006;Smith et al, 2007;Tang & Liu, 2007). A few researchers have used these two techniques iteratively, by returning values from simulation for re-optimization to solve specific problems, exchanging problem-specific parameters between the two techniques (Byrne & Hossain, 2005;Carlson, Hershey, & Kropp, 1979;De Angelis, Felici, & Impelluso, 2003;Karabakal, Gunal, & Ritchie, 2000;Ko, Ko, & Kim, 2006;Lee, Kim, & Moon, 2002;Leung & Cheung, 2000;Leung, Maheshwari, & Miller, 1993;Nolan & Sovereign, 1972). This paper introduces a novel solution methodology that integrates optimization and simulation and is easily adaptable to various combinatorial problems such as those of facility location, facility layout, and scheduling.…”

Concurrent Design of Product Modules Structure and Global Supply Chain Configuration

“…In 1998, Van Hoek, introduced a framework to analyze the configuration of supply chain in the context of global strategy and showed that implementation of postponed manufacturing not only requires the reconfiguration of the logistic systems but also other operations in the supply chain. Karabakal et al, (2000), studied the American Volkswagen's vehicle distribution system and presented a combination of simulation and discrete optimization models to analyze the alterative designs in terms of costs and customer service level. Hahn et al, (2000), addressed the supply chain synchronization problem in Hyundai Motor…”