OM Forum—The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management

Andrew M. Davis (), Shawn Mankad (), Charles J. Corbett () and Elena Katok ()
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Andrew M. Davis: Samuel Curtis Johnson Graduate School of Management, SC Johnson College of Business, Cornell University, Ithaca, New York 14853
Shawn Mankad: Poole College of Management, North Carolina State University, Raleigh, North Carolina 27695
Charles J. Corbett: Anderson School of Management, University of California, Los Angeles, California 90095
Elena Katok: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Manufacturing & Service Operations Management, 2024, vol. 26, issue 5, 1605-1621

Abstract: Problem definition : Two disciplines increasingly applied in operations management (OM) are machine learning (ML) and behavioral science (BSci). Rather than treating these as mutually exclusive fields, we discuss how they can work as complements to solve important OM problems. Methodology/results : We illustrate how ML and BSci enhance one another in non-OM domains before detailing how each step of their respective research processes can benefit the other in OM settings. We then conclude by proposing a framework to help identify how ML and BSci can jointly contribute to OM problems. Managerial implications : Overall, we aim to explore how the integration of ML and BSci can enable researchers to solve a wide range of problems within OM, allowing future research to generate valuable insights for managers, companies, and society.

Keywords: operations management; machine learning; behavioral science (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:5:p:1605-1621

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