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Forecasting Sales of Slow and Fast Moving Inventories

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  • Snyder, R.

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.

Suggested Citation

  • Snyder, R., 1999. "Forecasting Sales of Slow and Fast Moving Inventories," Monash Econometrics and Business Statistics Working Papers 7/99, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:1999-7
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/1999/wp7-99.pdf
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    References listed on IDEAS

    as
    1. R D Snyder & A B Koehler & J K Ord, 1999. "Lead time demand for simple exponential smoothing: an adjustment factor for the standard deviation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1079-1082, October.
    2. 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.
    3. 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.
    4. 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.
    5. Snyder, R. D., 1984. "Inventory control with the gamma probability distribution," European Journal of Operational Research, Elsevier, vol. 17(3), pages 373-381, September.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    demand forecasting; inventory control; simulation; parametric bootstrapping; time series analysis.;
    All these keywords.

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D20 - Microeconomics - - Production and Organizations - - - General

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