An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor

An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor In “An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor,” Chen and Chua examine an emerging challenge in data-driven operations: inventory decisions can unintentionally reveal sensitive demand information. Even when raw demand data are never shared, observers may reverse-engineer the data from small changes in order quantities. This risk is particularly acute in supply chains, where decisions are frequent, data-intensive, and easily observable. To mitigate this threat, the paper develops a suite of differentially private algorithms for the contextual newsvendor problem. These methods introduce carefully calibrated randomness into the newsvendor model, ensuring strong privacy protection under differential privacy while maintaining near-optimal operational performance. The authors also identify key drivers of the cost of privacy—data set size, contextual richness, and product variety—offering actionable guidance for firms seeking secure yet efficient data-driven operations. Finally, they demonstrate that privacy-preserving decisions can distort demand signals and reduce upstream supplier profits, highlighting important supply-chain-wide implications of data protection.