Abstract
This article outlines a four-step approach in analysing a complex supply chain using optimization and simulation software tools. The first step consists of Multi-Echelon Optimization to determine the best supply chain structures. The second step involves a Discrete-Event Simulation to determine the appropriate supply chain configuration. The third step, Simulation-Optimization, is then used to improve the supply chain's design established in the first two steps by optimizing the policies used to govern the network's behaviour. The final step, Design for Robustness, ensures that the final selection of the supply chain's network structure and policies will operate well under a wide variety of situations by minimizing the risk of undesirable outcomes. Using a four-step methodology, supply chain modelling provides an efficient supply chain design operating under effective inventory, sourcing and transportation policies. A case study from a Fortune 500 manufacturing company is evaluated using the four-step methodology. Future studies are outlined.
Additional information
Notes on contributors
Sameer Kumar
Sameer Kumar is a Professor of Decision Sciences and Qwest Endowed Chair in Global Communications and Technology Management in the Opus College of Business, University of St. Thomas. His major research interests include optimization concepts applied to design and operational management of production and service systems, where issues relating to various aspects of global supply chain management, international operations, technology management, product and process innovation, and capital investment justification decisions are also considered.
Daniel A. Nottestad
Daniel A. Nottestad is a Supply Chain Specialist at 3M Manufacturing & Supply Chain Services, 3M Company. He has been at 3M for 21 years. He has research interests in systems simulation, modeling and optimization, specifically, manufacturing systems. He has an MS in Manufacturing Systems Engineering from the University of St. Thomas and a BS in Mechanical Engineering, University of Wisconsin-Madison.
