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Forecasting the Intermittent Demand for Slow-Moving Items

Ralph Snyder (), Keith Ord () and Adrian Beaumont
Additional contact information
Adrian Beaumont: Department of Econometrics and Business Statistics, Monash University

No 2010-003, Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting

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.

Keywords: Croston's method; Exponential smoothing; Hurdle shifted Poisson distribution; Intermittent demand; Inventory control; Prediction likelihood; State space models (search for similar items in EconPapers)
JEL-codes: C25 C53 M21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Date: 2010-05, Revised 2011-03
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https://www2.gwu.edu/~forcpgm/2010-003.pdf Second version, 2011 (application/pdf)

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