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Echelon Reorder Points, Installation Reorder Points, and the Value of Centralized Demand Information

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  • Fangruo Chen

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

We consider a serial inventory system with N stages. The material flows from an outside supplier to stage N, then to stage N - 1, etc., and finally to stage 1 where random customer demand arises. Each stage replenishes a stage-specific inventory position according to a stage-specific reorder point/order quantity policy. Two variations of this policy are considered. One is based on echelon stock, and the other installation stock. The former requires centralized demand information, while the latter does not. The relative cost difference between the two policies is called the value of centralized demand information. For fixed order quantities, we develop efficient algorithms for computing both the optimal echelon reorder points and the optimal installation reorder points. These algorithms enable us to conduct an extensive computational study to assess the value of centralized demand information and to understand how this value depends on several key system parameters, i.e., the number of stages, leadtimes, batch sizes, demand variability, and the desired level of customer service.

Suggested Citation

  • Fangruo Chen, 1998. "Echelon Reorder Points, Installation Reorder Points, and the Value of Centralized Demand Information," Management Science, INFORMS, vol. 44(12-Part-2), pages 221-234, December.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:12-part-2:p:s221-s234
    DOI: 10.1287/mnsc.44.12.S221
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    References listed on IDEAS

    as
    1. Fangruo Chen & Yu-Sheng Zheng, 1994. "Lower Bounds for Multi-Echelon Stochastic Inventory Systems," Management Science, INFORMS, vol. 40(11), pages 1426-1443, November.
    2. G. Hadley & T. M. Whitin, 1961. "A Family of Inventory Models," Management Science, INFORMS, vol. 7(4), pages 351-371, July.
    3. Andrew J. Clark & Herbert Scarf, 2004. "Optimal Policies for a Multi-Echelon Inventory Problem," Management Science, INFORMS, vol. 50(12_supple), pages 1782-1790, December.
    4. Awi Federgruen & Yu-Sheng Zheng, 1992. "An Efficient Algorithm for Computing an Optimal (r, Q) Policy in Continuous Review Stochastic Inventory Systems," Operations Research, INFORMS, vol. 40(4), pages 808-813, August.
    5. Marc A. De Bodt & Stephen C. Graves, 1985. "Continuous-Review Policies for a Multi-Echelon Inventory Problem with Stochastic Demand," Management Science, INFORMS, vol. 31(10), pages 1286-1299, October.
    6. repec:bla:scandj:v:90:y:1988:i:3:p:275-89 is not listed on IDEAS
    7. Fangruo Chen, 1998. "Stationary Policies in Multiechelon Inventory Systems with Deterministic Demand and Backlogging," Operations Research, INFORMS, vol. 46(3-supplem), pages 26-34, June.
    8. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    9. Fangruo Chen & Yu-Sheng Zheng, 1998. "Near-Optimal Echelon-Stock (R, nQ) Policies in Multistage Serial Systems," Operations Research, INFORMS, vol. 46(4), pages 592-602, August.
    10. Rema Hariharan & Paul Zipkin, 1995. "Customer-Order Information, Leadtimes, and Inventories," Management Science, INFORMS, vol. 41(10), pages 1599-1607, October.
    11. Fangruo Chen & Yu-Sheng Zheng, 1994. "Evaluating Echelon Stock (R, nQ) Policies in Serial Production/Inventory Systems with Stochastic Demand," Management Science, INFORMS, vol. 40(10), pages 1262-1275, October.
    12. Yu-Sheng Zheng, 1992. "On Properties of Stochastic Inventory Systems," Management Science, INFORMS, vol. 38(1), pages 87-103, January.
    Full references (including those not matched with items on IDEAS)

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