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The Effect of Collaborative Forecasting on Supply Chain Performance

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  • Yossi Aviv

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    (Olin School of Business, Washington University, St. Louis, Missouri 63130)

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    Abstract

    We consider a cooperative, two-stage supply chain consisting of two members: a retailer and a supplier. In our first model, called local forecasting, each member updates the forecasts of future demands periodically, and is able to integrate the adjusted forecasts into his replenishment process. Forecast adjustments made at both levels of the supply chain can be correlated. The supply chain has a decentralized information structure, so that day-to-day inventory and forecast information are known locally only. In our second model, named collaborative forecasting, the supply chain members jointly maintain and update a single forecasting process in the system. Hence, forecasting information becomes centralized. Finally, we consider as a benchmark the special case in which forecasts are not integrated into the replenishment processes at all. We propose a unified framework that allows us to study and compare the three types of settings. This study comes at a time when various types of collaborative forecasting partnerships are being experimented within industry, and when the drivers for success or failure of such initiatives are not yet fully understood. In addition to providing some managerial insights into questions that arise in this context, our set of models is tailored to serve as building blocks for future work in this emerging area of research.

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    File URL: http://dx.doi.org/10.1287/mnsc.47.10.1326.10260
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 47 (2001)
    Issue (Month): 10 (October)
    Pages: 1326-1343

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    Handle: RePEc:inm:ormnsc:v:47:y:2001:i:10:p:1326-1343

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    Related research

    Keywords: Collaborative Forecasting; CFAR; CPFR; Supply Chain Management;

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    Cited by:
    1. Li, Gang & Yang, Hongjiao & Sun, Linyan & Sohal, Amrik S., 2009. "The impact of IT implementation on supply chain integration and performance," International Journal of Production Economics, Elsevier, vol. 120(1), pages 125-138, July.
    2. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    3. Kalchschmidt, Matteo, 2012. "Best practices in demand forecasting: Tests of universalistic, contingency and configurational theories," International Journal of Production Economics, Elsevier, vol. 140(2), pages 782-793.
    4. Ryu, Seung-Jin & Tsukishima, Takahiro & Onari, Hisashi, 2009. "A study on evaluation of demand information-sharing methods in supply chain," International Journal of Production Economics, Elsevier, vol. 120(1), pages 162-175, July.
    5. Nam, Seong-Hyun & Vitton, John & Kurata, Hisashi, 2011. "Robust supply base management: Determining the optimal number of suppliers utilized by contractors," International Journal of Production Economics, Elsevier, vol. 134(2), pages 333-343, December.
    6. Choi, Tsan-Ming & Sethi, Suresh, 2010. "Innovative quick response programs: A review," International Journal of Production Economics, Elsevier, vol. 127(1), pages 1-12, September.
    7. Sari, Kazim, 2008. "On the benefits of CPFR and VMI: A comparative simulation study," International Journal of Production Economics, Elsevier, vol. 113(2), pages 575-586, June.
    8. Khan, Muhammad Hamza & Khan, Muhammad Hassan & Maqsood, Muhammad Nawaz & Rehman, Khaliq Ur, 2012. "The Relationship between Supply Chain Fit and Return on Assets of the Firm," MPRA Paper 53195, University Library of Munich, Germany.
    9. Hayat, Khizer & Abbas, Aamir & Siddique, M. & Cheema, Khaliq Ur Rehman, 2012. "A Study of the Different Factors That Affecting the Supply Chain Responsiveness," MPRA Paper 53193, University Library of Munich, Germany.
    10. Akkermans, Henk & Bogerd, Paul & van Doremalen, Jan, 2004. "Travail, transparency and trust: A case study of computer-supported collaborative supply chain planning in high-tech electronics," European Journal of Operational Research, Elsevier, vol. 153(2), pages 445-456, March.
    11. Glenn, David & Bisi, Arnab & Puterman, Martin L., 2004. "The Bayesian Newsvendors in Supply Chains with Unobserved Lost Sales," Working Papers 04-0110, University of Illinois at Urbana-Champaign, College of Business.
    12. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    13. Pibernik, Richard & Sucky, Eric, 2007. "An approach to inter-domain master planning in supply chains," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 200-212, July.
    14. Williams, Brent D. & Waller, Matthew A. & Ahire, Sanjay & Ferrier, Gary D., 2014. "Predicting retailer orders with POS and order data: The inventory balance effect," European Journal of Operational Research, Elsevier, vol. 232(3), pages 593-600.
    15. Mukhopadhyay, Samar K. & Yue, Xiaohang & Zhu, Xiaowei, 2011. "A Stackelberg model of pricing of complementary goods under information asymmetry," International Journal of Production Economics, Elsevier, vol. 134(2), pages 424-433, December.
    16. Kim, Sung Min & Mahoney, Joseph T., 2006. "Collaborative Planning, Forecasting, and Replenishment (CPFR) as a Relational Contract: An Incomplete Contracting Perspective," Working Papers 06-0102, University of Illinois at Urbana-Champaign, College of Business.
    17. Altug, Mehmet Sekip & Muharremoglu, Alp, 2011. "Inventory management with advance supply information," International Journal of Production Economics, Elsevier, vol. 129(2), pages 302-313, February.
    18. Kamath, Narasimha B. & Roy, Rahul, 2007. "Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics framework," European Journal of Operational Research, Elsevier, vol. 179(2), pages 334-351, June.
    19. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The impact of forecasting on companies' performance: Analysis in a multivariate setting," International Journal of Production Economics, Elsevier, vol. 133(1), pages 458-469, September.
    20. Zhang, Xiaolong & Burke, Gerard J., 2011. "Analysis of compound bullwhip effect causes," European Journal of Operational Research, Elsevier, vol. 210(3), pages 514-526, May.
    21. Arshinder & Kanda, Arun & Deshmukh, S.G., 2008. "Supply chain coordination: Perspectives, empirical studies and research directions," International Journal of Production Economics, Elsevier, vol. 115(2), pages 316-335, October.
    22. Gunasekaran, Angappa & Ngai, Eric W.T., 2009. "Modeling and analysis of build-to-order supply chains," European Journal of Operational Research, Elsevier, vol. 195(2), pages 319-334, June.

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