Strategic Data Analytics Under Supplier Encroachment

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 channels, providing managerial insights into the organizational choices underpinning retailers’ data governance. Funding: This work was partially supported by the National Natural Science Foundation of China [Grant 72571122] and the Research Grants Council of Hong Kong [Grants C6020-21GF, GRF 16501722]. The authors also acknowledge support from the Southern Key Laboratory of Technology Finance (Guangdong) at the Southern University of Science and Technology (Shenzhen, China). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0412 .