IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지

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

적응적 지수평활법을 이용한 공급망 수요예측의 실증분석

  • 김정일 (한국과학기술원 테크노경영대학원) ;
  • 차경천 ;
  • 전덕빈 (한국과학기술원 테크노경영대학원) ;
  • 박대근 (한국과학기술원 테크노경영대학원) ;
  • 박성호 (한국과학기술원 테크노경영대학원) ;
  • 박명환 (한성대학교 산업시스템공학과)
  • Received : 2005.03.17
  • Accepted : 2005.06.30
  • Published : 2005.09.30
Download PDF
⟨ Previous Next ⟩

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

References

  1. Chen, C. and L. K. Liu (1993), Forecasting Time Series with Outlier, Journal of Forecasting, 12, 13-35 https://doi.org/10.1002/for.3980120103
  2. Chernoff, H. and S. Zacks (1964), Estimation the Current Mean of a Normal Distribution Which is Subjected to Changes Over Time, Annals of Mathematical Statistics, 35, 999-1089 https://doi.org/10.1214/aoms/1177700517
  3. Gardner, L. A. Jr. (1969), On Detecting Changes in the Mean of Normal Variates, Annals of Mathematical Statistics, 40, 116-126 https://doi.org/10.1214/aoms/1177697808
  4. Hartison, P. J. and C. F. Stevens (1976), Bayesian Forecasting, journal of the Royal Statistical Society, Series B, 38, 205-247
  5. Jun, D. B. and R M. Oliver (1985), Bayesian Forecasts Following a Major Level Changes in Exponential Smoothing, Journal of Forecasting, 4, 293-302 https://doi.org/10.1002/for.3980040306
  6. Jun, D. B. (1989), On Deteering and Estimating a Major Level of Slope Change in General Exponential Smoothing, Journal of Forecasting, 8(1), 55-64 https://doi.org/10.1002/for.3980080105
  7. Jun, D. B. (1992), A Design of Adaptive Exponential Smoothing Using A Change Detection Statistic, Journal of the Operatiom Research Society of Japan, 35(2), June
  8. Trigg, D. W. and A. G. Leach (1967), Exponential Smoothing with an Adaptive Response Rate, Operations Research Quarterly, 18, 132-141
  9. Tsay, R S. (1986), Time Series Model Specification in the Presence of Outlier, Journal of the American Statistical Association, 81, 132-141 https://doi.org/10.2307/2287980
  10. Zhao, X., Xie, J., and Leung, J. (2002), The Impact of Forecasting Model Selection on the Value of Information Sharing in a Supply Chain, European Journal of Operational Research, 142,321-344 https://doi.org/10.1016/S0377-2217(01)00300-9