RIEB Seminar

3:00pm-5:00pm

Topics

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

Speaker

Mark H. LANG (Kenan-Flagler Business School, University of North Carolina)

Abstruct

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 decisionmaking 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 crosstrained 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.