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Article . 2018 . Peer-reviewed
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Article . 2023 . Peer-reviewed
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Article . 2023
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Manufacturing Productivity with Worker Turnover

Authors: Ken Moon; Patrick Bergemann; Daniel Brown; Andrew Chen; James Chu; Ellen A. Eisen; Gregory M. Fischer; +3 Authors

Manufacturing Productivity with Worker Turnover

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. This paper was accepted by Vishal Gaur, operations management. Funding: The authors gratefully acknowledge support from the data sponsor, and K. Moon gratefully acknowledges support from the Wharton Dean’s Research Fund and the Claude Marion Endowed Faculty Scholar Award of the Wharton School. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4476 .

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
41
Top 10%
Top 10%
Top 10%
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