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Forecasting sales of slow and fast moving inventories

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  • Snyder, Ralph

Abstract

Adaptations of simple exponential smoothing are presented that aim to unify the task of forecasting demand for both slow and fast moving inventories. A feature of the adaptations is that they are designed to ensure that the resulting prediction distributions have only a nonnegative domain. A parametric bootstrap approach is proposed for generating empirical approximations for the so-called lead-time demand distribution, something required for inventory control calculations. The proposed methods are illustrated and their performance compared on real demand data for car parts.

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 140 (2002)
Issue (Month): 3 (August)
Pages: 684-699

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Handle: RePEc:eee:ejores:v:140:y:2002:i:3:p:684-699

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References

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  1. Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
  2. Snyder, R. D., 1984. "Inventory control with the gamma probability distribution," European Journal of Operational Research, Elsevier, vol. 17(3), pages 373-381, September.
  3. Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.
  4. Johnston, F. R. & Boylan, J. E., 1996. "Forecasting intermittent demand: A comparative evaluation of croston's method. Comment," International Journal of Forecasting, Elsevier, vol. 12(2), pages 297-298, June.
  5. Snyder, R.D. & Koehler, A.B. & Ord, J.K., 1998. "Lead Time demand for Simple Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 13/98, Monash University, Department of Econometrics and Business Statistics.
  6. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
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Cited by:
  1. Tratar, Liljana Ferbar, 2010. "Joint optimisation of demand forecasting and stock control parameters," International Journal of Production Economics, Elsevier, vol. 127(1), pages 173-179, September.
  2. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
  3. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
  4. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
  5. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics.
  6. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.

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