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Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

Author

Listed:
  • Ruomeng Cui

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Gad Allon

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Achal Bassamboo

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Jan A. Van Mieghem

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

Abstract

We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a consumer packaged goods company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. Whereas the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These “decision deviations” lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge—the value of knowing the downstream replenishment policy. This paper was accepted by Serguei Netessine, operations management.

Suggested Citation

  • Ruomeng Cui & Gad Allon & Achal Bassamboo & Jan A. Van Mieghem, 2015. "Information Sharing in Supply Chains: An Empirical and Theoretical Valuation," Management Science, INFORMS, vol. 61(11), pages 2803-2824, November.
  • Handle: RePEc:inm:ormnsc:v:61:y:2015:i:11:p:2803-2824
    DOI: 10.1287/mnsc.2014.2132
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    References listed on IDEAS

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    11. Ketzenberg, Michael & Oliva, Rogelio & Wang, Yimin & Webster, Scott, 2023. "Retailer inventory data sharing in a fresh product supply chain," European Journal of Operational Research, Elsevier, vol. 307(2), pages 680-693.
    12. Tarikere T. Niranjan & Narendra K. Ghosalya & Srinagesh Gavirneni, 2022. "Crying Wolf and a Knowing Wink: A Behavioral Study of Order Inflation and Discounting in Supply Chains," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1071-1088, March.
    13. Altendorfer, Klaus, 2017. "Relation between lead time dependent demand and capacity flexibility in a two-stage supply chain with lost sales," International Journal of Production Economics, Elsevier, vol. 194(C), pages 13-24.
    14. Kyung Sun (Melissa) Rhee & Jinyang Zheng & Youwei Wang & Yong Tan, 2023. "Value of Information Sharing via Ride-Hailing Apps: An Empirical Analysis," Information Systems Research, INFORMS, vol. 34(3), pages 1228-1244, September.
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    16. Lu, Jizhou & Feng, Gengzhong & Shum, Stephen & Lai, Kin Keung, 2021. "On the value of information sharing in the presence of information errors," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1139-1152.
    17. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.
    18. Park, Arim & Chen, Roger & Cho, Soohyun & Zhao, Yao, 2023. "The determinants of online matching platforms for freight services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    19. Tom F. Tan & Bradley R. Staats, 2020. "Behavioral Drivers of Routing Decisions: Evidence from Restaurant Table Assignment," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 1050-1070, April.
    20. Peng Liang & Hasan Cavusoglu & Nan Hu, 2023. "Customers’ managerial expectations and suppliers’ asymmetric cost management," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1975-1993, June.
    21. Klaus Altendorfer & Thomas Felberbauer & Herbert Jodlbauer, 2018. "Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand," Papers 1812.00773, arXiv.org.
    22. Yu, Yugang & Luo, Yifei & Shi, Ye, 2022. "Adoption of blockchain technology in a two-stage supply chain: Spillover effect on workforce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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