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Forecasting Sales of Slow and Fast Moving Inventories

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Author Info
Snyder, R.

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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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File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/1999/wp7-99.pdf
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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/99.

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Length: 18 pages
Date of creation: Jun 1999
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Handle: RePEc:msh:ebswps:1999-7

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Related research
Keywords: demand forecasting; inventory control; simulation; parametric bootstrapping; time series analysis.;

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Find related papers by JEL classification:
E22 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Capital; Investment; Capacity
D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
D20 - Microeconomics - - Production and Organizations - - - General

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References listed on IDEAS
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.:
  1. 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. [Downloadable!] (restricted)
  2. 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.
  3. 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. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, 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.)

  1. 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. [Downloadable!]
  2. Lydia Shenstone & Rob J. Hyndman, 2003. "Stochastic models underlying Croston's method for intermittent demand forecasting," Monash Econometrics and Business Statistics Working Papers 1/03, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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