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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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    5. 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.
    6. Yuliang Yao & Martin Dresner & Kevin Xiaoguo Zhu, 2019. "“Monday Effect” on Performance Variations in Supply Chain Fulfillment: How Information Technology–Enabled Procurement May Help," Information Systems Research, INFORMS, vol. 30(4), pages 1402-1423, December.
    7. 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.
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    9. Nikolai Stein & Jan Meller & Christoph M. Flath, 2018. "Big data on the shop-floor: sensor-based decision-support for manual processes," Journal of Business Economics, Springer, vol. 88(5), pages 593-616, July.
    10. 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).
    11. 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.
    12. 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.
    13. Jiankun Sun & Dennis J. Zhang & Haoyuan Hu & Jan A. Van Mieghem, 2022. "Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations," Management Science, INFORMS, vol. 68(2), pages 846-865, February.
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    15. 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.
    16. Kovtun, Vladimir & Giloni, Avi & Hurvich, Clifford, 2019. "The value of sharing disaggregated information in supply chains," European Journal of Operational Research, Elsevier, vol. 277(2), pages 469-478.
    17. Lisa M. Scheele & Ulrich W. Thonemann & Marco Slikker, 2018. "Designing Incentive Systems for Truthful Forecast Information Sharing Within a Firm," Management Science, INFORMS, vol. 64(8), pages 3690-3713, August.
    18. 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.
    19. Ke Rong & Di Zhou & Xinwei Shi & Wei Huang, 2022. "Social Information Disclosure of Friends in Common in an E‐commerce Platform Ecosystem: An Online Experiment," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 984-1005, March.
    20. Robert N. Boute & Stephen M. Disney & Joren Gijsbrechts & Jan A. Van Mieghem, 2022. "Dual Sourcing and Smoothing Under Nonstationary Demand Time Series: Reshoring with SpeedFactories," Management Science, INFORMS, vol. 68(2), pages 1039-1057, February.
    21. Kostas Bimpikis & Davide Crapis & Alireza Tahbaz-Salehi, 2019. "Information Sale and Competition," Management Science, INFORMS, vol. 67(6), pages 2646-2664, June.
    22. 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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