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Forecasting the intermittent demand for slow-moving inventories: A modelling approach

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Author Info

  • Snyder, Ralph D.
  • Ord, J. Keith
  • Beaumont, Adrian

Abstract

Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine various different approaches to demand 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. This emphasis leads to the 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 the occurrence of active demand periods). We also develop performance measures which are related to the entire prediction distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1046 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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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 28 (2012)
Issue (Month): 2 ()
Pages: 485-496

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Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:485-496

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

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

References

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  1. 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.
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  3. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
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  6. Heinen, Andreas, 2003. "Modelling Time Series Count Data: An Autoregressive Conditional Poisson Model," MPRA Paper 8113, University Library of Munich, Germany.
  7. Snyder, R.D. & Koehler, A.B. & Ord, J.K., 1998. "Lead Time demand for Simple Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 13/98, Monash University, Department of Econometrics and Business Statistics.
  8. 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.
  9. HEINEN, Andreas & RENGIFO, Erick, 2003. "Multivariate modelling of time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  10. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.
  11. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
  12. 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.
  13. Neil Shephard, 1995. "Generalized linear autoregressions," Economics Papers 8., Economics Group, Nuffield College, University of Oxford.
  14. 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.
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  17. Baki Billah & Maxwell L King & Ralph D Snyder & Anne B Koehler, 2005. "Exponential Smoothing Model Selection for Forecasting," Monash Econometrics and Business Statistics Working Papers 6/05, Monash University, Department of Econometrics and Business Statistics.
  18. 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.
  19. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
  20. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
  21. Gardner, Everette Jr. & Koehler, Anne B., 2005. "Comments on a patented bootstrapping method for forecasting intermittent demand," International Journal of Forecasting, Elsevier, vol. 21(3), pages 617-618.
  22. Snyder, Ralph, 2002. "Forecasting sales of slow and fast moving inventories," European Journal of Operational Research, Elsevier, vol. 140(3), pages 684-699, August.
  23. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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Citations

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Cited by:
  1. Roman Frigg & Seamus Bradley & Hailiang Du & Leonard A. Smith, . "Laplace’s Demon and Climate Change," Grantham Research Institute on Climate Change and the Environment Working Papers 103, Grantham Research Institute on Climate Change and the Environment.
  2. 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.

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