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Forecasting the Intermittent Demand for Slow-Moving Items

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Author Info

  • Ralph D. Snyder

    (Department of Econometrics and Business Statistics, Monash University)

  • J. Keith Ord

    (McDonough School of Business, Georgetown University)

  • Adrian Beaumont

    (Department of Econometrics and Business Statistics, Monash University)

Abstract

Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be nonstationary. This emphasis leads to consideration of prediction distributions for processes with time-dependent parameters. A wide range of possible distributions could be considered but we focus upon the Poisson (as a widely used benchmark), the negative binomial (as a popular extension of the Poisson) and a hurdle shifted Poisson (which retains Croston’s notion of a Bernoulli process for times between orders). We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that inventory planning should be based upon dynamic models using distributions that are more flexible than the traditional Poisson scheme.

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File URL: http://www.gwu.edu/~forcpgm/2010-003.pdf
File Function: Second version, 2011
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Bibliographic Info

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2010-003.

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Length: 38 pages
Date of creation: May 2010
Date of revision: Mar 2011
Handle: RePEc:gwc:wpaper:2010-003

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Keywords: Croston's method; Exponential smoothing; Hurdle shifted Poisson distribution; Intermittent demand; Inventory control; Prediction likelihood; State space models;

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  1. Muhammad Akram & Rob J Hyndman & J. Keith Ord, 2008. "Exponential smoothing and non-negative data," Working Papers 2008-003, The George Washington University, Department of Economics, Research Program on Forecasting.
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Cited by:
  1. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
  2. 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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