Private Optimal Inventory Policy Learning for Feature-Based Newsvendor with Unknown Demand

Tuoyi Zhao (), Wen-Xin Zhou () and Lan Wang ()
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Tuoyi Zhao: Department of Management Science, Miami Herbert Business School, University of Miami, Coral Gables, Florida 33146
Wen-Xin Zhou: Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, Illinois 60607
Lan Wang: Department of Management Science, Miami Herbert Business School, University of Miami, Coral Gables, Florida 33146

Management Science, 2025, vol. 71, issue 7, 6092-6111

Abstract: The data-driven newsvendor problem with features has recently emerged as a significant area of research, driven by the proliferation of data across various sectors such as retail, supply chains, e-commerce, and healthcare. Given the sensitive nature of customer or organizational data often used in feature-based analysis, it is crucial to ensure individual privacy to uphold trust and confidence. Despite its importance, privacy preservation in the context of inventory planning remains unexplored. A key challenge is the nonsmoothness of the newsvendor loss function, which sets it apart from existing work on privacy-preserving algorithms in other settings. This paper introduces a novel approach to estimating a privacy-preserving optimal inventory policy within the f -differential privacy framework, an extension of the classical ( ϵ , δ ) -differential privacy with several appealing properties. We develop a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation to simultaneously address three main challenges: (i) unknown demand distribution and nonsmooth loss function, (ii) provable privacy guarantees for individual-level data, and (iii) desirable statistical precision. We derive finite-sample high-probability bounds for optimal policy parameter estimation and regret analysis. By leveraging the structure of the newsvendor problem, we attain a faster excess population risk bound compared with that obtained from an indiscriminate application of existing results for general nonsmooth convex loss. Our bound aligns with that for strongly convex and smooth loss function. Our numerical experiments demonstrate that the proposed new method can achieve desirable privacy protection with a marginal increase in cost.

Keywords: newsvendor; differential privacy; data-driven decision-making; convolution smoothing; regret analysis (search for similar items in EconPapers)
Date: 2025
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