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A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models


  • Katy S. Azoury

    (School of Business Administration and Economics, California State University, Northridge, California 91330)

  • Bruce L. Miller

    (System Science Department, University of California, Los Angeles, California 90024)


Although it is often the case that the parameters of the distribution of demand are not known with certainty and that a Bayesian formulation would be appropriate, such an approach is generally not used in inventory calculations for computational reasons. Since one often resorts to a non-Bayesian formulation, it is of interest to compare Bayesian policies with a comparable non-Bayesian policy. Using the concept of flexibility it was anticipated that the quantity ordered under the non-Bayesian policy would be greater than or equal to that under a Bayesian policy. This result is established for the n-period nondepletive inventory model. However, a two-period counterexample is given for the standard (depletive) inventory model.

Suggested Citation

  • Katy S. Azoury & Bruce L. Miller, 1984. "A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models," Management Science, INFORMS, vol. 30(8), pages 993-1003, August.
  • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:8:p:993-1003

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    Cited by:

    1. Larson, C. Erik & Olson, Lars J. & Sharma, Sunil, 2001. "Optimal Inventory Policies when the Demand Distribution Is Not Known," Journal of Economic Theory, Elsevier, vol. 101(1), pages 281-300, November.
    2. Choi, Tsan-Ming, 2007. "Pre-season stocking and pricing decisions for fashion retailers with multiple information updating," International Journal of Production Economics, Elsevier, vol. 106(1), pages 146-170, March.
    3. Janssen, Elleke & Strijbosch, Leo & Brekelmans, Ruud, 2009. "Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function," International Journal of Production Economics, Elsevier, vol. 118(1), pages 34-42, March.
    4. Choi, Tsan-Ming (Jason) & Li, Duan & Yan, Houmin, 2006. "Quick response policy with Bayesian information updates," European Journal of Operational Research, Elsevier, vol. 170(3), pages 788-808, May.
    5. Choi, Tsan-Ming & Chow, Pui-Sze, 2008. "Mean-variance analysis of Quick Response Program," International Journal of Production Economics, Elsevier, vol. 114(2), pages 456-475, August.
    6. Sen, Alper & Zhang, Alex X., 2009. "Style goods pricing with demand learning," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1058-1075, August.
    7. Katy S. Azoury & Julia Miyaoka, 2009. "Optimal Policies and Approximations for a Bayesian Linear Regression Inventory Model," Management Science, INFORMS, vol. 55(5), pages 813-826, May.
    8. Guillermo Gallego & Özalp Özer, 2001. "Integrating Replenishment Decisions with Advance Demand Information," Management Science, INFORMS, vol. 47(10), pages 1344-1360, October.
    9. repec:pal:jorsoc:v:54:y:2003:i:8:d:10.1057_palgrave.jors.2601584 is not listed on IDEAS
    10. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2006. "Assessing the Effects of using Demand Parameters Estimates in Inventory Control," Discussion Paper 2006-90, Tilburg University, Center for Economic Research.
    11. Joseph M. Milner & Panos Kouvelis, 2002. "On the Complementary Value of Accurate Demand Information and Production and Supplier Flexibility," Manufacturing & Service Operations Management, INFORMS, vol. 4(2), pages 99-113, December.
    12. Joseph M. Milner & Panos Kouvelis, 2005. "Order Quantity and Timing Flexibility in Supply Chains: The Role of Demand Characteristics," Management Science, INFORMS, vol. 51(6), pages 970-985, June.

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    spare parts; Bayesian; dynamic programming;


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