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When platform exploits data analysis advantage: change of OEM-led supply chain structure

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Abstract

The development of digital technology, such as data mining and analysis techniques, has enabled e-commerce platforms to use the data generated in their ecosystems and forecast the online demand more accurately. By sharing the forecast information, platforms help their partners reduce the demand uncertainty. To examine the effects of the shared information, this study considers a two-echelon supply chain consisting of one original equipment manufacturer (OEM), one brand store, and one platform, and investigates the relations between the forecast information and firms’ channel strategies. Our analysis reveals some interesting implications. First, the platform’s forecast information encourages the OEM to develop online business unless the brand store adopts the dual-channel strategy and the platform sets the revenue commission as zero, while it always increases the brand store’s willingness to adopt the dual-channel strategy. Moreover, the OEM’s online business hinders the platform to share the information if consumers’ acceptance for the brand in online channel is low. Interestingly, the brand store’s dual-channel strategy hinders the platform to share the information if consumers’ acceptance for the brand in online channel is high. Further, we find that when the brand store adopts the dual-channel strategy, the OEM’s online business always decreases the benefit of the forecast information for the brand store. However, when the OEM develops online business, the brand store’s dual-channel strategy sometimes increases the benefit of the forecast information for the OEM. In addition, we also conduct some numerical experiments to examine the impacts of the platform commission rate on these firms’ benefits from the information and find that the higher commission rate can increase the OEM’s benefit under certain conditions. Our study aims to provide managerial insights for the OEM, brand store, and platform to capture the value of forecast information.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grants. 71922009, 71871080, 71690235, 71501058, 72071060], Innovative Research Groups of the National Natural Science Foundation of China [Grant 71521001], Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product [111 project, Grant B17014], and National Key Research and Development Program of China [2019YFB1705300, 2019YFE0110300].

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, China

    Ping Yan, Jun Pei & Ya Zhou

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, China

    Ping Yan, Jun Pei & Ya Zhou

  3. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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  1. Ping Yan
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Correspondence to Jun Pei.

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Yan, P., Pei, J., Zhou, Y. et al. When platform exploits data analysis advantage: change of OEM-led supply chain structure. Ann Oper Res 339, 1405–1431 (2024). https://doi.org/10.1007/s10479-021-04335-2

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