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A dynamic inventory model with random replenishment quantities

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  • Baker, H.
  • Ehrhardt, R.

Abstract

A periodic-review, random-demand inventory model is analyzed under the assumption that replenishment quantities are random fractions of the amounts ordered. Results of a previous study of a single-period model are generalized to form an easily computed heuristic adaptation of the (s, S) policy for use in this environment. The heuristic is based on the simple practice of scaling down pipeline inventories to estimate the inventory position, and scaling up the order quantity in anticipation of an average replenishment yield. Simulation experiments are used to estimate the most cost-efficient (s, S) policies and to estimate the performance of heuristic policies in environments where replenishment randomness ranges from mild (0-20% defectives) to moderate (0-50% defectives). The heuristic is shown to perform quite well, with expected total costs typically within a few percent of the best (s, S) costs. The results tend to support common practice in industry which is similar to the approach studied here. Although the heuristic is naive in the sense that it ignores the degree of randomness in the replenishment quantity, the simulation results support the speculation that unless the target service level is extremely high, the replenishment process must be extremely random for its variability to be a significant explicit factor in the selection of a practical, cost-effective policy.

Suggested Citation

  • Baker, H. & Ehrhardt, R., 1995. "A dynamic inventory model with random replenishment quantities," Omega, Elsevier, vol. 23(1), pages 109-116, February.
  • Handle: RePEc:eee:jomega:v:23:y:1995:i:1:p:109-116
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    References listed on IDEAS

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    1. Evan L. Porteus, 1985. "Numerical Comparisons of Inventory Policies for Periodic Review Systems," Operations Research, INFORMS, vol. 33(1), pages 134-152, February.
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    Cited by:

    1. Awi Federgruen & Nan Yang, 2014. "Infinite Horizon Strategies for Replenishment Systems with a General Pool of Suppliers," Operations Research, INFORMS, vol. 62(1), pages 141-159, February.
    2. T W Sloan, 2004. "A periodic review production and maintenance model with random demand, deteriorating equipment, and binomial yield," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(6), pages 647-656, June.
    3. Xiao, Li & Wang, Ce, 2023. "Multi-location newsvendor problem with random yield: Centralization versus decentralization," Omega, Elsevier, vol. 116(C).
    4. Saxena, Neha & Sarkar, Mitali & Sarkar, Biswajit, 2024. "Which retail strategy for shelf life products can satisfy consumers under game policy?," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).

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