The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers

Ken Moon (), Prashant Loyalka (), Patrick Bergemann () and Joshua Cohen ()
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Ken Moon: The Wharton School, Philadelphia, Pennsylvania 19104
Prashant Loyalka: Stanford Graduate School of Education, Stanford, California 94305
Patrick Bergemann: The Paul Merage School of Business, University of California Irvine, Irvine, California 92697
Joshua Cohen: Apple University, Cupertino, California 95014

Management Science, 2022, vol. 68, issue 5, 3755-3767

Abstract: Product reliability is a key concern for manufacturers. We examine worker turnover as a significant but underrecognized determinant of product reliability. Our study collects and integrates (1) data reporting factory worker staffing and turnover from within a major consumer electronics producer’s supply chain and (2) traceable data reporting the component quality and field failures—that is, replacements and repairs—of nearly 50 million consumer mobile devices over four years of customer usage. Devices are individually traced back to the factory conditions and staffing, down to the assembly line–week, under which they were produced. Despite the manufacturer’s extensive quality control efforts, including stringent testing, each percentage point increase in the weekly rate of workers quitting from an assembly line (its weekly worker turnover) is found to increase field failures by 0.74%–0.79%. In the high-turnover weeks following paydays, eventual field failures are strikingly 10.2% more common than for devices produced during the lowest turnover weeks immediately before paydays. In other weeks, the assembly lines experiencing higher turnover produce an estimated 2%–3% more field failures on average. The associated costs amount to hundreds of millions of U.S. dollars. We demonstrate that staffing and retaining a stable factory workforce critically underlies product reliability and showcase the value of traceability coupled with connected workplace and product data in supply chain operations.

Keywords: data-driven workforce planning; empirical operations management; employee turnover; people operations; product quality; productivity; quality management; supply chain management (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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