Data-Driven Research in Supply Chain Operations - A Review

36 Pages Posted: 7 Jul 2020

See all articles by Meng Qi

Meng Qi

University of California, Berkeley - Department of Industrial Engineering and Operations Research

Ho-Yin Mak

University of Oxford - Said Business School

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)

Date Written: June 11, 2020

Abstract

We review the operations research/management science literature on data-driven methods in supply chain operations. This line of work has grown rapidly in recent years, thanks to 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 supply chain 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

Qi, Meng and Mak, Ho-Yin and Shen, Zuo-Jun Max, Data-Driven Research in Supply Chain Operations - A Review (June 11, 2020). Available at SSRN: https://ssrn.com/abstract=3625059 or http://dx.doi.org/10.2139/ssrn.3625059

Meng Qi

University of California, Berkeley - Department of Industrial Engineering and Operations Research ( email )

4141 Etcheverry Hall
Berkeley, CA 94720-1777
United States

Ho-Yin Mak (Contact Author)

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

Zuo-Jun Max Shen

University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR) ( email )

IEOR Department
4135 Etcheverry Hall
Berkeley, CA 94720
United States

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