MODELING UNCERTAIN FORECAST ACCURACY IN SUPPLY CHAINS WITH POSTPONEMENT |
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| Authors: | Larry J. LeBlanc James A. Hill Jerry Harder Gregory W. Greenwell |
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| Affiliation: | 1. Vanderbilt University;2. (Ph.D. Northwestern University) is a professor of operations management at Vanderbilt University. His research interests include supply chain analysis and application of spreadsheet optimization models. He has approximately 60 publications in refereed journals and 50 presentations at universities and organizations abroad. He has served as Secretary and Council Member of the Telecommunications Section of INFORMS. Prior to that, he served as Chairman, Vice‐Chairman, and Council Member of the Transportation Science Section of INFORMS.;3. The Ohio State University;4. (Ph.D. The Ohio State University) is an assistant professor at The Ohio State University. He conducts research on lead time uncertainty in supply chains, the value of cross‐docking in supply chains, and the value of forecast accuracy in supply chains. Professor Hill previously worked as the regional supplier development manager for Pepsi Co. His research has appeared in the Journal of Business Logistics, Decision Sciences, European Journal of Operational Research, and the International Journal of Production Research and Interfaces.;5. (B.A. Vanderbilt University) is employed by the Office of Cancer Surveillance, Tennessee Department of Health. His research interests include queuing theory and applications, telecommunications network design, and decision making under uncertainty. His publications include a coauthored book, the Complete Traffic Engineering Handbook. His research has appeared in Locational Analysis among other journals.;6. Total Print;7. (MBA Vanderbilt University) is President of Total Print. He was previously Vice President of Business Development at Nu‐kote International. Mr. Greenwell has domestic and international experience in operations, finance, information technology and business development. He has a B.A. in Accounting from Bellarmine University and an MBA from the Owen Graduate School of Management at Vanderbilt University. |
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| Abstract: | We examine a situation where a manufacturer operates in a two‐mode production environment. The first mode could involve overseas vendors and manufacturing facilities. If additional units are later required, the company must use its second mode—more expensive last‐minute domestic vendors and manufacturing sites. We develop a new methodology for analyzing the impact of forecast accuracy on the decision to postpone production. We examine the interaction of forecast accuracy, shortage vs. holding costs, transportation costs and the cost of postponing production in the supply chain of a single product facing uncertain demand. Our model can be used to analyze the cost of important changes, such as increasing forecast accuracy, reducing the cost of backorders, lowering the cost of delaying production, or lowering transportation costs. Our model allows a firm to understand its overall cost structure so that it can accurately evaluate the impact of improved forecast accuracy and lowered costs in the context of postponement. |
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| Keywords: | Forecasting Probabilistic models Production planning Supply chain management |
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