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Applied Machine Learning in Operations Management

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Innovative Technology at the Interface of Finance and Operations

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 11))

Abstract

The field of operations management has witnessed a fast-growing trend of data analytics in recent years. In particular, spurred by the increasing availability of data and methodological advancement in machine learning, a large body of recent literature in this field takes advantage of machine learning techniques for analyzing how firms should operate. In this chapter, we review applications of different machine learning methods, including supervised learning, unsupervised learning, and reinforcement learning, in various areas of operations management. We highlight how both supervised and unsupervised learning shape operations management research in both descriptive and prescriptive analyses. We also emphasize how different variants of reinforcement learning are applied in diverse operational decision problems. We then identify several exciting future directions at the intersection of machine learning and operations management.

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Authors and Affiliations

  1. Wharton Business School, University of Pennsylvania, Philadelphia, PA, USA

    Hamsa Bastani

  2. Olin Business School, Washington University in St. Louis, St. Louis, MO, USA

    Dennis J. Zhang

  3. W. P. Carey School of Business, Arizona State University, Tempe, AZ, USA

    Heng Zhang

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  1. Hamsa Bastani
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  2. Dennis J. Zhang
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  3. Heng Zhang
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Correspondence to Heng Zhang .

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  1. McDonough School of Business, Georgetown University, Washington, DC, USA

    Volodymyr Babich

  2. University of Chicago Booth, School of Business, Chicago, IL, USA

    John R. Birge

  3. McDonough School of Business, Georgetown University, Washington, DC, USA

    Gilles Hilary

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Bastani, H., Zhang, D.J., Zhang, H. (2022). Applied Machine Learning in Operations Management. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-75729-8_7

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