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Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data

Author

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  • Eva Labro
  • Mark Lang
  • Jim Omartian

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 Bureau. 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 become more efficient, with lower inventory, increased volume of shipments, narrower product mix, reduced management payroll and increased use of flexible and temporary employees. Results are robust to a specification based on increased government demand for data.

Suggested Citation

  • Eva Labro & Mark Lang & Jim Omartian, 2019. "Predictive Analytics and Organizational Architecture: Plant-Level Evidence from Census Data," Working Papers 19-02, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:19-02
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    File URL: https://www2.census.gov/ces/wp/2019/CES-WP-19-02.pdf
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    References listed on IDEAS

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