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Inventory rotation policies for slow moving items

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  • John W. Bradford
  • Paul K. Sugrue

Abstract

In this article we present a stochastic model for determining inventory rotation policies for a retail firm which must stock many hundreds of distinctive items having uncertain heterogeneous sales patterns. The model develops explicit decision rules for determining (1) the length of time that an item should remain in inventory before the decision is made on whether or not to rotate the item out of inventory and (2) the minimum sales level necessary for retaining the item in inventory. Two inventory rotation policies are developed, the first of which maximizes cumulative expected sales over a finite planning horizon and the second of which maximizes cumulative expected profit. We also consider the statistical behavior of items having uncertain, discrete, and heterogeneous sales patterns using a two‐period prediction methodology where period 1 is used to accumulate information on individual sales rates and this knowledge is then used, in a Bayesian context, to make sales predictions for period 2. This methodology assumes that over an arbitrary time interval sales for each item are Poisson with unknown but stationary mean sales rates and the mean sales rates are distributed gamma across all items. We also report the application of the model to a retail firm which stocks many hundreds of distinctive unframed poster art titles. The application provides some useful insights into the behavior of the model as well as some interesting aspects pertaining to the implementation of the results in a “real‐world” situation.

Suggested Citation

  • John W. Bradford & Paul K. Sugrue, 1991. "Inventory rotation policies for slow moving items," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(1), pages 87-105, February.
  • Handle: RePEc:wly:navres:v:38:y:1991:i:1:p:87-105
    DOI: 10.1002/1520-6750(199102)38:13.0.CO;2-V
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    References listed on IDEAS

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    1. Vincent P. Carroll & Hau L. Lee & Ambar G. Rao, 1986. "Implications of Salesforce Productivity Heterogeneity and Demotivation: A Navy Recruiter Case Study," Management Science, INFORMS, vol. 32(11), pages 1371-1388, November.
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    3. Popovic, Jovan B., 1987. "Decision making on stock levels in cases of uncertain demand rate," European Journal of Operational Research, Elsevier, vol. 32(2), pages 276-290, November.
    4. Donald G. Morrison & David C. Schmittlein, 1981. "Predicting Future Random Events Based on Past Performance," Management Science, INFORMS, vol. 27(9), pages 1006-1023, September.
    5. Everette S. Gardner, 1983. "Approximate decision rules for continuous review inventory systems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 30(1), pages 59-68, March.
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    Cited by:

    1. Zhou, Quan Spring & Olsen, Tava Lennon, 2017. "Inventory rotation of medical supplies for emergency response," European Journal of Operational Research, Elsevier, vol. 257(3), pages 810-821.

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