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Information Sharing and Order Variability Control Under a Generalized Demand Model

Author

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  • Li Chen

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

  • Hau L. Lee

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

The value of information sharing and how it could address the bullwhip effect have been the subject of studies in the literature. Most of these studies used different forms of demand models, assuming that no order smoothing was used by the retailer and that the supplier has full knowledge of the retailer's demand model and order policy. In this paper, we contribute to the literature by starting with a most general demand model, coupled with a smoothing policy for order variability control. In addition, we do not require that the supplier has full knowledge of the retailer's demand model and order policy, but instead let the retailer share its projected future orders (and freely revise them as the retailer sees fit). Under such a setting, we first obtain a unifying formula for the magnitude of the bullwhip effect. The formula indicates that it is the forecast correlation over the exposure period as a whole that determines the magnitude of the bullwhip effect. We then quantify the value of information sharing and generalize the existing results in the literature. Finally, we explore the optimal smoothing parameters that could benefit the total supply chain. The resulting optimal policy resembles the postponement strategy. We find that information sharing together with order postponement improves the supply chain performance, even though the order variability may amplify in some cases.

Suggested Citation

  • Li Chen & Hau L. Lee, 2009. "Information Sharing and Order Variability Control Under a Generalized Demand Model," Management Science, INFORMS, vol. 55(5), pages 781-797, May.
  • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:5:p:781-797
    DOI: 10.1287/mnsc.1080.0983
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    References listed on IDEAS

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    1. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    2. Bourland, Karla E. & Powell, Stephen G. & Pyke, David F., 1996. "Exploiting timely demand information to reduce inventories," European Journal of Operational Research, Elsevier, vol. 92(2), pages 239-253, July.
    3. Vishal Gaur & Avi Giloni & Sridhar Seshadri, 2005. "Information Sharing in a Supply Chain Under ARMA Demand," Management Science, INFORMS, vol. 51(6), pages 961-969, June.
    4. Yossi Aviv, 2003. "A Time-Series Framework for Supply-Chain Inventory Management," Operations Research, INFORMS, vol. 51(2), pages 210-227, April.
    5. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    6. Yossi Aviv, 2002. "Gaining Benefits from Joint Forecasting and Replenishment Processes: The Case of Auto-Correlated Demand," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 55-74, December.
    7. Anantaram Balakrishnan & Joseph Geunes & Michael S. Pangburn, 2004. "Coordinating Supply Chains by Controlling Upstream Variability Propagation," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 163-183, July.
    8. Warren H. Hausman, 1969. "Sequential Decision Problems: A Model to Exploit Existing Forecasters," Management Science, INFORMS, vol. 16(2), pages 93-111, October.
    9. Gérard P. Cachon & Taylor Randall & Glen M. Schmidt, 2007. "In Search of the Bullwhip Effect," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 457-479, April.
    10. 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.
    11. Guillermo Gallego & Özalp Özer, 2001. "Integrating Replenishment Decisions with Advance Demand Information," Management Science, INFORMS, vol. 47(10), pages 1344-1360, October.
    12. Stephen C. Graves, 1999. "Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process"," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 174-174.
    13. Stephen C. Graves & David B. Kletter & William B. Hetzel, 1998. "A Dynamic Model for Requirements Planning with Application to Supply Chain Optimization," Operations Research, INFORMS, vol. 46(3-supplem), pages 35-49, June.
    14. Gérard P. Cachon & Martin A. Lariviere, 2001. "Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain," Management Science, INFORMS, vol. 47(5), pages 629-646, May.
    15. Fangruo Chen, 1999. "Decentralized Supply Chains Subject to Information Delays," Management Science, INFORMS, vol. 45(8), pages 1076-1090, August.
    16. Srinivasan Raghunathan, 2001. "Information Sharing in a Supply Chain: A Note on its Value when Demand Is Nonstationary," Management Science, INFORMS, vol. 47(4), pages 605-610, April.
    17. Hau Lee & Seungjin Whang, 1999. "Decentralized Multi-Echelon Supply Chains: Incentives and Information," Management Science, INFORMS, vol. 45(5), pages 633-640, May.
    18. G. D. Johnson & H. E. Thompson, 1975. "Optimality of Myopic Inventory Policies for Certain Dependent Demand Processes," Management Science, INFORMS, vol. 21(11), pages 1303-1307, July.
    19. L. Beril Toktay & Lawrence M. Wein, 2001. "Analysis of a Forecasting-Production-Inventory System with Stationary Demand," Management Science, INFORMS, vol. 47(9), pages 1268-1281, September.
    20. Gullu, Refik, 1997. "A two-echelon allocation model and the value of information under correlated forecasts and demands," European Journal of Operational Research, Elsevier, vol. 99(2), pages 386-400, June.
    21. Kahn, James A, 1987. "Inventories and the Volatility of Production," American Economic Review, American Economic Association, vol. 77(4), pages 667-679, September.
    22. Stephen C. Graves, 1999. "A Single-Item Inventory Model for a Nonstationary Demand Process," Manufacturing & Service Operations Management, INFORMS, vol. 1(1), pages 50-61.
    23. Gérard P. Cachon & Marshall Fisher, 2000. "Supply Chain Inventory Management and the Value of Shared Information," Management Science, INFORMS, vol. 46(8), pages 1032-1048, August.
    24. Xiangwen Lu & Jing-Sheng Song & Amelia Regan, 2006. "Inventory Planning with Forecast Updates: Approximate Solutions and Cost Error Bounds," Operations Research, INFORMS, vol. 54(6), pages 1079-1097, December.
    25. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    26. Yossi Aviv, 2001. "The Effect of Collaborative Forecasting on Supply Chain Performance," Management Science, INFORMS, vol. 47(10), pages 1326-1343, October.
    27. Yossi Aviv, 2007. "On the Benefits of Collaborative Forecasting Partnerships Between Retailers and Manufacturers," Management Science, INFORMS, vol. 53(5), pages 777-794, May.
    28. Srinagesh Gavirneni & Roman Kapuscinski & Sridhar Tayur, 1999. "Value of Information in Capacitated Supply Chains," Management Science, INFORMS, vol. 45(1), pages 16-24, January.
    29. Theodore H. Clark & Janice H. Hammond, 1997. "Reengineering Channel Reordering Processes To Improve Total Supply‐Chain Performance," Production and Operations Management, Production and Operations Management Society, vol. 6(3), pages 248-265, September.
    30. Tetsuo Iida & Paul H. Zipkin, 2006. "Approximate Solutions of a Dynamic Forecast-Inventory Model," Manufacturing & Service Operations Management, INFORMS, vol. 8(4), pages 407-425, October.
    31. Christian Terwiesch & Z. Justin Ren & Teck H. Ho & Morris A. Cohen, 2005. "An Empirical Analysis of Forecast Sharing in the Semiconductor Equipment Supply Chain," Management Science, INFORMS, vol. 51(2), pages 208-220, February.
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