Forecasting for inventory control with exponential smoothing
AbstractExponential smoothing, often used for sales forecasting in inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains the appropriate approach under more general conditions where the variances are allowed to grow and contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular the problem of finding the prediction distribution of aggregate lead- time demand for use in inventory control calculations is considered. It is found that unless a drift term is added to simple exponential smoothing, the prediction distribution is largely unaffected by the variance assumption. A method for establishing order-up-to levels and reorder levels directly from the simulated prediction distributions is also proposed.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 18 (2002)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/ijforecast
Other versions of this item:
- Snyder, R.D. & Koehler, A. & Ord, K., 1999. "Forecasting for Inventory Control with Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 10/99, Monash University, Department of Econometrics and Business Statistics.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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"Forecasting Models and Prediction Intervals for the Multiplicative Holt-Winters Method,"
Monash Econometrics and Business Statistics Working Papers
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