Predictive Analytics and the Changing Manufacturing Employment Relationship: Plant Level Evidence from Census Data

59 Pages Posted: 1 Jan 2019 Last revised: 14 Mar 2019

See all articles by Eva Labro

Eva Labro

Kenan-Flagler Business School University of North Carolina

Mark H. Lang

University of North Carolina at Chapel Hill

James D. Omartian

University of Michigan - Ross School of Business

Date Written: March 2019

Abstract

We examine trends in the use of predictive analytics for a sample of more than 25,000 manufacturing plants using proprietary data from the US Census. Comparing 2010 and 2015, we find that use of predictive analytics has increased markedly, with the greatest use in younger plants, professionally-managed firms, more educated workforces, and stable industries. Decisions on data to be gathered originate from headquarters and are associated with less delegation of decision- making and more widespread awareness of quantitative targets among plant employees. Performance targets become more accurate, long-term oriented, and linked to company-wide performance, and management incentives strengthen, both in terms of monetary bonuses and career outcomes. Plants increasing predictive analytics change the demographics of their workforce by reducing management payroll and increasing use of flexible, temporary and cross- trained rank-and-file employees. With increased usage of predictive analytics, plants become more efficient, with lower inventory, increased volume of shipments, and narrower product mix. Results are robust to a specification based on increased government demand for data.

Keywords: Predictive Analytics, Big Data, Organizational Architecture, Manufacturing, Incentives, Targets

JEL Classification: L22, L25, M11, M12, M15, M41, M51, M52

Suggested Citation

Labro, Eva and Lang, Mark H. and Omartian, James D., Predictive Analytics and the Changing Manufacturing Employment Relationship: Plant Level Evidence from Census Data (March 2019). Available at SSRN: https://ssrn.com/abstract=3300927 or http://dx.doi.org/10.2139/ssrn.3300927

Eva Labro

Kenan-Flagler Business School University of North Carolina ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States
(919) 962-5747 (Phone)

Mark H. Lang

University of North Carolina at Chapel Hill ( email )

Kenan-Flagler Business School
McColl Building
Chapel Hill, NC 27599-3490
United States
919-962-1644 (Phone)
919-962-4727 (Fax)

James D. Omartian (Contact Author)

University of Michigan - Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI 48109-1234
United States

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