Strategic Data Analytics Under Supplier Encroachment

Chen, Yonghui ORCID: 0009-0003-2836-4519, Ma, Guangrui, He, Qiao-Chu, Chen, Ying-Ju and Shen, Zuo-Jun Max (2026) Strategic Data Analytics Under Supplier Encroachment M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT, 28 (4). pp. 1324-1338. ISSN 1523-4614, 1526-5498

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Abstract

Problem definition: To enhance supply chain collaboration, major retailers increasingly grant suppliers access to their data platforms, leveraging data analytics to deliver valuable demand insights. However, such access raises the risk of channel encroachment by suppliers. This paper examines the retailer’s strategy for deploying data analytics in the presence of supplier encroachment. Methodology/results: Under the Bayesian persuasion framework, the retailer’s data analytics strategy is formulated as a signaling rule that maps market states into predictions with partial informational granularity. We further compare two organizational structures of data governance: a shared data platform, which delivers identical demand predictions to both the retailer and the supplier, and a separate data platform, which provides accurate predictions for the retailer, offering less precise insights to the supplier. On a shared data platform, the retailer adopts perfectly accurate analytics when the supplier’s encroachment cost is either very high or very low. At moderate encroachment costs, the shared platform generates informative yet biased forecasts, which surprisingly benefit both firms because of the ease of double marginalization. By contrast, under a separate platform, no informative predictions are generated for the supplier at moderate encroachment costs. Overall, comparing the two organizational structures, the retailer prefers a shared platform when the supplier’s encroachment cost is relatively high. Managerial implications: Our findings reveal a nonmonotonic relationship between the accuracy of data analytics and suppliers’ encroachment efficiency, offering practical guidance for retailers in implementing effective learning algorithms. Moreover, we highlight the benefits of transparent and integrated data analytics within vertical distribution.

Item Type:	Article
Uncontrolled Keywords:	supplier encroachment, information design, strategic data analytics, organizational structures
Divisions:	Faculty of Humanities & Social Sciences Faculty of Humanities & Social Sciences > School of Management Faculty of Humanities & Social Sciences > Faculty of Humanities & Social Sci (All T&R Staff) Faculty of Humanities & Social Sciences > School of Management > School of Management (T&R Staff) Faculty of Humanities & Social Sciences > School of Management > Operations and Supply Chain Management
Depositing User:	Symplectic Admin
Date Deposited:	06 Mar 2026 17:05
Last Modified:	11 Aug 2026 12:02
DOI:	10.1287/msom.2023.0412
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3197395
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