Forecasting the Intermittent Demand for Slow-Moving Items
AbstractOrganizations 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2010-003.
Length: 38 pages
Date of creation: May 2010
Date of revision: Mar 2011
Contact details of provider:
Postal: Monroe Hall #340, 2115 G Street, NW, Washington, DC 20052
Phone: (202) 994-6150
Fax: (202) 994-6147
Web page: http://www.gwu.edu/~forcpgm
More information through EDIRC
Croston's method; Exponential smoothing; Hurdle shifted Poisson distribution; Intermittent demand; Inventory control; Prediction likelihood; State space models;
Other versions of this item:
- Keith Ord & Ralph Snyder & Adrian Beaumont, 2010. "Forecasting the Intermittent Demand for Slow-Moving Items," Monash Econometrics and Business Statistics Working Papers 12/10, Monash University, Department of Econometrics and Business Statistics.
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- M21 - Business Administration and Business Economics; Marketing; Accounting - - Business Economics - - - Business Economics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-03-19 (All new papers)
- NEP-ECM-2011-03-19 (Econometrics)
- NEP-ETS-2011-03-19 (Econometric Time Series)
- NEP-FOR-2011-03-19 (Forecasting)
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.:
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tara M. Sinclair).
If references are entirely missing, you can add them using this form.