Forecasting the Intermittent Demand for Slow-Moving Items
Keith Ord (),
Ralph Snyder () and
Adrian Beaumont ()
No 12/10, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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 non-stationary. We develop a forecasting framework based upon the zero-inflated Poisson distribution (ZIP), which enables the explicit evaluation of the multi-period lead-time demand distribution in special cases and an effective simulation scheme more generally. We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The ZIP model is compared to a number of existing methods using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that the ZIP scheme compares favorably to other approaches, including variations of Croston's method as well as providing a straightforward basis for inventory planning.
Keywords: Croston's method; Exponential smoothing; Intermittent demand; Inventory control; Prediction likelihood; State space models; Zero-inflated Poisson distribution (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Working Paper: Forecasting the Intermittent Demand for Slow-Moving Items (2011)
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