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Newsvendor Bounds and Heuristic for Optimal Policies in Serial Supply Chains

  • Kevin H. Shang

    ()

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Jing-Sheng Song

    ()

    (Graduate School of Management, University of California, Irvine, California 92697)

Registered author(s):

    We consider the classic N-stage serial supply systems with linear costs and stationary random demands. There are deterministic transportation leadtimes between stages, and unsatisfied demands are backlogged. The optimal inventory policy for this system is known to be an echelon base-stock policy, which can be computed through minimizing N nested convex functions recursively. To identify the key determinants of the optimal policy, we develop a simple and surprisingly good heuristic. This method minimizes 2Nseparate newsvendor-type cost functions, each of which uses the original problem data only. These functions are lower and upper bounds for the echelon cost functions; their minimizers form bounds for the optimal echelon base-stock levels. The heuristic is the simple average of the solution bounds. In extensive numerical experiments, the average relative error of the heuristic is 0.24%, with the maximum error less than 1.5%. The bounds and the heuristic, which can be easily obtained by simple spreadsheet calculations, enhance the accessibility and implementability of the multiechelon inventory theory. More importantly, the closed-form expressions provide an analytical tool for us to gain insights into issues such as system bottlenecks, effects of system parameters, and coordination mechanisms in decentralized systems.

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    File URL: http://dx.doi.org/10.1287/mnsc.49.5.618.15147
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 49 (2003)
    Issue (Month): 5 (May)
    Pages: 618-638

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    Handle: RePEc:inm:ormnsc:v:49:y:2003:i:5:p:618-638
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    1. Fangruo Chen, 1999. "Decentralized Supply Chains Subject to Information Delays," Management Science, INFORMS, vol. 45(8), pages 1076-1090, August.
    2. Gérard P. Cachon & Paul H. Zipkin, 1999. "Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain," Management Science, INFORMS, vol. 45(7), pages 936-953, July.
    3. Guillermo Gallego, 1998. "New Bounds and Heuristics for (Q, r) Policies," Management Science, INFORMS, vol. 44(2), pages 219-233, February.
    4. 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.
    5. Sven Axsäter & Kaj Rosling, 1993. "Notes: Installation vs. Echelon Stock Policies for Multilevel Inventory Control," Management Science, INFORMS, vol. 39(10), pages 1274-1280, October.
    6. Fangruo Chen & Yu-Sheng Zheng, 1994. "Lower Bounds for Multi-Echelon Stochastic Inventory Systems," Management Science, INFORMS, vol. 40(11), pages 1426-1443, November.
    7. van Houtum, G. J. & Inderfurth, K. & Zijm, W. H. M., 1996. "Materials coordination in stochastic multi-echelon systems," European Journal of Operational Research, Elsevier, vol. 95(1), pages 1-23, November.
    8. Evan L. Porteus, 2000. "Responsibility Tokens in Supply Chain Management," Manufacturing & Service Operations Management, INFORMS, vol. 2(2), pages 203-219, October.
    9. Hau Lee & Seungjin Whang, 1999. "Decentralized Multi-Echelon Supply Chains: Incentives and Information," Management Science, INFORMS, vol. 45(5), pages 633-640, May.
    10. Guillermo Gallego & Paul Zipkin, 1999. "Stock Positioning and Performance Estimation in Serial Production-Transportation Systems," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 77-88.
    11. Jing-Sheng Song, 1994. "The Effect of Leadtime Uncertainty in a Simple Stochastic Inventory Model," Management Science, INFORMS, vol. 40(5), pages 603-613, May.
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