Manufacturing Productivity with Worker Turnover

Ken Moon (), Patrick Bergemann (), Daniel Brown (), Andrew Chen (), James Chu (), Ellen A. Eisen (), Gregory M. Fischer (), Prashant Loyalka (), Sungmin Rho () and Joshua Cohen ()
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Ken Moon: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Patrick Bergemann: The Paul Merage School of Business, University of California–Irvine, Irvine, California 92607
Daniel Brown: Elevance Health, Palo Alto, California
Andrew Chen: Apple, Inc., Cupertino, California 95014
James Chu: Department of Sociology, Columbia University, New York 10027
Ellen A. Eisen: Elevance Health, Palo Alto, California
Gregory M. Fischer: School of Public Health, University of California–Berkeley, Berkeley, California 94720
Prashant Loyalka: Department of Sociology, Columbia University, New York 10027
Sungmin Rho: Boston Consulting Group, London, United Kingdom
Joshua Cohen: Stanford Graduate School of Education, Stanford, California 94305

Management Science, 2023, vol. 69, issue 4, 1995-2015

Abstract: To maximize productivity, manufacturers must organize and equip their workforces to efficiently handle variable workloads. Their success depends on their ability to assign experienced and skilled workers to specialized tasks and coordinate work on production lines. Worker turnover may disrupt such efforts. We use staffing, productivity, and pay data from within a major consumer electronics manufacturer’s supply chain to study how firms should manage worker turnover and its effects using production decisions, wages, and inventory. We find that worker turnover impedes coordination between assembly line coworkers by weakening knowledge sharing and relationships. Publicly available unit-cost estimates imply that worker turnover accounts for $206–274 million in added direct expenses alone from defectively assembled units failing the firm’s stringent quality control. To evaluate managerial alternatives, we structurally estimate a dynamic equilibrium model (an Experience-Based Equilibrium) encompassing (1) workers’ endogenous turnover decisions and (2) the firm’s weekly planning of its production scheduling and staffing in response. In counterfactual analyses, a less turnover-prone, hence more productive, workforce significantly benefits the firm, reducing its variable production costs by 4.5%, or an estimated $928 million for the studied product. Such benefits justify paying higher efficiency wages even to less skilled workforces; furthermore, interestingly, rational inventory management policies incentivize self-interested firms to reduce rather than tolerate turnover.

Keywords: data-driven workforce planning; empirical operations management; employee turnover; Experience-Based Equilibrium; productivity; reinforcement learning; structural estimation (search for similar items in EconPapers)
Date: 2023
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