Manufacturing Productivity with Worker Turnover

70 Pages Posted: 25 Sep 2018 Last revised: 22 Nov 2019

See all articles by Ken Moon

Ken Moon

University of Pennsylvania - The Wharton School

Patrick Bergemann

University of Chicago - Booth School of Business

Daniel Brown

University of California, Berkeley

Andrew Chen

Apple Inc.

James Chu

Stanford University, Department of Sociology, Students; Stanford University - Freeman Spogli Institute for International Studies

Ellen Eisen

University of California, Berkeley

Gregory Fischer

London School of Economics & Political Science (LSE)

Prashant Kumar Loyalka

Stanford University - Freeman Spogli Institute for International Studies

Sungmin Rho

Graduate Institute of International and Development Studies (IHEID)

Joshua Cohen

Apple University

Date Written: November 18, 2019

Abstract

We find that rapid worker turnover significantly disrupts the productivity of responsive manufacturers. Our study uses a uniquely rich dataset drawn from China-based FATP (final assembly, testing, and packaging) facilities that produce millions of units of consumer electronic goods weekly yet exhibit high worker turnover exceeding 300% annually. The data cover the firm's weekly production plans, 52,214 workers' compensations and assignments, and assembly station productivity. To study managerial prescriptions, we extend the classical production planning problem to include endogenous worker turnover as an Experience-Based Equilibrium and use advances in reinforcement learning and approximate dynamic programming to estimate and simulate our model. Our empirical analyses exploit instrumental variables, including the firm's demand forecasts as ``demand shifters''. We find that turnover's impact on yield waste is conservatively $146-178M, and that a well-calibrated wage increase reduces the manufacturer's variable production costs (including wages) by up to 21%, or $594M for the product we study. The wage increase reduces the firm's reliance on a larger workforce and overtime to hedge against yield disruptions from turnover; it stabilizes a leaner workforce and improves both production reliability and flexibility. In settings where performance depends on workers repeating known tasks in coordinated groups, our results suggest that firms responsively matching supply to demand can pay a steep price for a disruptively turnover-prone workforce.

Keywords: Data-driven workforce planning, Empirical operations management, Employee turnover, Experience-Based Equilibrium, Production planning, Productivity, Stochastic optimization, Structural estimation

Suggested Citation

Moon, Ken and Bergemann, Patrick and Brown, Daniel and Chen, Andrew and Chu, James and Eisen, Ellen and Fischer, Gregory and Loyalka, Prashant and Rho, Sungmin and Cohen, Joshua, Manufacturing Productivity with Worker Turnover (November 18, 2019). Available at SSRN: https://ssrn.com/abstract=3248075 or http://dx.doi.org/10.2139/ssrn.3248075

Ken Moon (Contact Author)

University of Pennsylvania - The Wharton School ( email )

Jon M. Huntsman Hall
3730 Walnut St.
Philadelphia, PA 19104-6365
United States

Patrick Bergemann

University of Chicago - Booth School of Business

Daniel Brown

University of California, Berkeley

Andrew Chen

Apple Inc.

James Chu

Stanford University, Department of Sociology, Students ( email )

Stanford
United States

Stanford University - Freeman Spogli Institute for International Studies ( email )

Stanford, CA 94305
United States

Ellen Eisen

University of California, Berkeley

Gregory Fischer

London School of Economics & Political Science (LSE)

Houghton Street
London, WC2A 2AE
United Kingdom

Prashant Loyalka

Stanford University - Freeman Spogli Institute for International Studies ( email )

Stanford, CA 94305
United States

Sungmin Rho

Graduate Institute of International and Development Studies (IHEID) ( email )

PO Box 136
Geneva, CH-1211
Switzerland

Joshua Cohen

Apple University ( email )

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