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An Empirical Study on Supply Chain Demand Forecasting Using Adaptive Exponential Smoothing  

Kim, Jung-Il (Graduate School of Management, KAIST)
Cha, Kyoung-Cheon (Forbizone Inc.)
Jun, Duk-Bin (Graduate School of Management, KAIST)
Park, Dae- Keun (Graduate School of Management, KAIST)
Park, Sung-Ho (Graduate School of Management, KAIST)
Park, Myoung-Whan (Department of Industrial and Mechanical System Engineering, Hansung University)
Publication Information
IE interfaces / v.18, no.3, 2005 , pp. 343-349 More about this Journal
Abstract
This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.
Keywords
SCM forecasting; adaptive exponential smoothing;
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