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Data‐driven research in retail operations—A review

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  • Meng Qi
  • Ho‐Yin Mak
  • Zuo‐Jun Max Shen

Abstract

We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

Suggested Citation

  • Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
    • Handle: RePEc:wly:navres:v:67:y:2020:i:8:p:595-616
    DOI: 10.1002/nav.21949
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    2. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
    3. Olivares-Nadal, Alba V., 2024. "Constructing decision rules for multiproduct newsvendors: An integrated estimation-and-optimization framework," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1021-1037.
    4. Niu, Baozhuang & Liu, Jiayun & Zhang, Jianhua & Chen, Kanglin, 2024. "Promised-delivery-time-driven reselling facing global platform's private label competition: Game analysis and data validation," Omega, Elsevier, vol. 123(C).

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