JOURNAL ARTICLE

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

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2024, v. 26, n. 5. P. 1605 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Davis, Andrew M.; Mankad, Shawn; Corbett, Charles J.; Katok, Elena 3 of 3

Abstract

The article focuses on the complementary integration of machine learning (ML) and behavioral science (BSci) in operations management (OM) to address complex problems more effectively. It outlines how ML, which excels in prediction using large data sets, and BSci, which provides insights into human decision-making through experiments and behavioral modeling, can jointly enhance data collection, algorithm training, deployment, hypothesis development, experimental design, and data analysis in OM research. The authors present examples from OM domains such as order fulfillment, forecasting, and worker performance to illustrate successful ML-BSci collaborations and propose a framework categorizing research approaches based on data availability and objectives (causal inference versus prediction). The article also emphasizes the need for interdisciplinary training and resource sharing to foster future ML-BSci partnerships that benefit managers, companies, and society.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2024/09, Vol. 26, Issue 5, p1605
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1523-4614
  • DOI:10.1287/msom.2022.0553
  • Accession Number:179561475
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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