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Bayesian Determination of the Reorder Point of a Slow Moving Item

Author

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  • Edward A. Silver

    (Arthur D. Little, Inc., Cambridge, Massachusetts)

Abstract

Consider an inventory item for which the procurement (or production setup) lead time is nonzero. One of the commonly used methods of determining the reorder point of such an item is to specify the probability of a stockout or a desired service level (fraction of demand that is to be instantaneously satisfied) and then select the reorder level that will provide such service. However, such an approach has assumed that the probability distribution of demand during the lead time is exactly known. In many inventory situations this assumption is not justified, particularly in the case of a slow moving item with limited available sales history. For such a situation this paper explores the use of a Bayesian approach to selecting a reorder point.

Suggested Citation

  • Edward A. Silver, 1965. "Bayesian Determination of the Reorder Point of a Slow Moving Item," Operations Research, INFORMS, vol. 13(6), pages 989-997, December.
    • Handle: RePEc:inm:oropre:v:13:y:1965:i:6:p:989-997
    DOI: 10.1287/opre.13.6.989
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    Cited by:

    1. Boender, C. G. E. & Rinnooy Kan, A. H. G., 1983. "Nonparametric Bayesian Estimation Of A Discrete Probability Distribution With Unknown Domain," Econometric Institute Archives 272279, Erasmus University Rotterdam.
    2. Silver, Edward A. & Fiechter, Claude-Nicolas, 1995. "Preventive maintenance with limited historical data," European Journal of Operational Research, Elsevier, vol. 82(1), pages 125-144, April.
    3. Juan Manuel Izar Landeta & Carmen Berenice Ynzunza Cortés & Orlando Guarneros García, 2016. "Lead time demand variability, safety stock and the inventory cost," Contaduría y Administración, Accounting and Management, vol. 61(3), pages 499-513, Julio-Sep.
    4. Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
    5. Babai, M.Z. & Chen, H. & Syntetos, A.A. & Lengu, D., 2021. "A compound-Poisson Bayesian approach for spare parts inventory forecasting," International Journal of Production Economics, Elsevier, vol. 232(C).
    6. Sinan Apak, 2015. "A Bayesian Approach Proposal For Inventory Cost and Demand Forecasting," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(2), pages 41-48, December.
    7. 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.
    8. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    9. Dolgui, Alexandre & Pashkevich, Maksim, 2008. "Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance," International Journal of Production Economics, Elsevier, vol. 112(2), pages 885-894, April.
    10. Hill, Roger M., 1997. "Applying Bayesian methodology with a uniform prior to the single period inventory model," European Journal of Operational Research, Elsevier, vol. 98(3), pages 555-562, May.

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