적응적 지수평활법을 이용한 공급망 수요예측의 실증분석

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 Leachs adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Juns 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.