An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data

Yu-Xin Tian and Chuan Zhang

International Journal of Production Economics, 2023, vol. 265, issue C

Abstract: We investigate a data-driven single-period inventory management problem with uncertain demand, where large amounts of textual online reviews and historical data are accessible. Unlike two-step frameworks (i.e., predict-then-optimization), we propose an end-to-end (E2E) framework that directly suggests the order quantity by leveraging a deep learning model that inputs textual online reviews and other demand-related feature data, without any intermediate steps such as text sentiment analysis. The E2E model does not require any prior assumptions about the demand distribution and can automatically determine the order quantity that minimizes the newsvendor cost by employing the information from real-world data. Our experiments, using publicly available real-world data, demonstrate that our method can significantly reduce the sum of overage and underage costs, outperforming other data-driven models proposed in recent years. Specifically, the inclusion of textual online review data improves ordering decisions by a 28.7% cost reduction.

Keywords: Data-driven; End-to-End; Newsvendor problem; Textual online reviews; Deep learning; Forecasting (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:265:y:2023:i:c:s0925527323002487

DOI: 10.1016/j.ijpe.2023.109016

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