两全其美:运营管理中的机器学习和行为科学

Andrew M. Davis, Shawn Mankad, Charles J. Corbett, Elena Katok
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摘要

问题定义:机器学习(ML)和行为科学(BSci)这两门学科越来越多地应用于运营管理(OM)领域。我们并没有将这两个学科视为相互排斥的领域,而是讨论了它们如何互为补充,共同解决重要的运营管理问题。方法/结果:我们首先说明了智能语言和智能科学如何在非 OM 领域相互促进,然后详细介绍了它们各自研究过程中的每一步如何在 OM 环境中为对方带来益处。最后,我们提出了一个框架,以帮助确定 ML 和 BSci 如何共同解决 OM 问题。管理意义:总之,我们旨在探索如何将 ML 和 BSci 结合起来,使研究人员能够解决 OM 中的各种问题,从而使未来的研究能够为管理者、公司和社会提供有价值的见解。
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The Best of Both Worlds: Machine Learning and Behavioral Science in Operations Management
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.
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