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Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance

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  • Dolgui, Alexandre
  • Pashkevich, Maksim
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    Abstract

    Modeling the lead-time demand for the multiple slow-moving inventory items in the case when the available requests history is very short is a challenge for inventory management. The classical forecasting technique, which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity, is known to lead to very poor performance in many cases important for industrial applications. An alternative approach to the demand forecasting for the considered problem is based on the Bayesian paradigm, when the initially developed population-averaged demand probability distribution is modified for each item using its specific requests history. This paper follows this approach and presents a new model, which relies on the beta distribution as a prior for the request probability, and allows to account for disparity in variance of demand between different stock keeping units. To estimate the model parameters, a special computationally effective technique based on the generalized method of moments is developed. Simulation results indicate the superiority of the proposed model over the known ones, while the computational burden does not increase.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 112 (2008)
    Issue (Month): 2 (April)
    Pages: 885-894

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    Handle: RePEc:eee:proeco:v:112:y:2008:i:2:p:885-894

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

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    References

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    1. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
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    3. Aronis, Kostas-Platon & Magou, Ioulia & Dekker, Rommert & Tagaras, George, 2004. "Inventory control of spare parts using a Bayesian approach: A case study," European Journal of Operational Research, Elsevier, vol. 154(3), pages 730-739, May.
    4. 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.
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    6. S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
    7. Ram Tripathi & Ramesh Gupta & John Gurland, 1994. "Estimation of parameters in the beta binomial model," Annals of the Institute of Statistical Mathematics, Springer, vol. 46(2), pages 317-331, June.
    8. Petrovic, R. & Senborn, A. & Vujosevic, M., 1989. "A new adaptive algorithm for determination of stocks in spare parts inventory systems," Engineering Costs and Production Economics, Elsevier, vol. 15(1), pages 405-410, May.
    9. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
    10. Dolgui, Alexandre & Ould-Louly, Mohamed-Aly, 2002. "A model for supply planning under lead time uncertainty," International Journal of Production Economics, Elsevier, vol. 78(2), pages 145-152, July.
    11. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
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    Cited by:
    1. Lindsey, Matthew & Pavur, Robert, 2009. "Prediction intervals for future demand of existing products with an observed demand of zero," International Journal of Production Economics, Elsevier, vol. 119(1), pages 75-89, May.
    2. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.

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