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Advanced Analytics Drives Reengineering of Field Operations for the 2020 U.S. Census

Author

Listed:
  • Tamara Adams

    (U.S. Census Bureau, Washington, DC 20233)

  • Alessandro Ferrucci

    (U.S. Census Bureau, Washington, DC 20233)

  • Pedro Carvalho

    (U.S. Census Bureau, Washington, DC 20233)

  • Sothiara Em

    (U.S. Census Bureau, Washington, DC 20233)

  • Benjamin Whitley

    (U.S. Census Bureau, Washington, DC 20233)

  • Ryan Cecchi

    (U.S. Census Bureau, Washington, DC 20233)

  • Teresa Hicks

    (U.S. Census Bureau, Washington, DC 20233)

  • Alexander Wooten

    (U.S. Census Bureau, Washington, DC 20233)

  • John Cuffe

    (U.S. Census Bureau, Washington, DC 20233)

  • Stephanie Studds

    (U.S. Census Bureau, Washington, DC 20233)

  • Irvin Lustig

    (Princeton Consultants, Princeton, New Jersey 08540)

  • Steve Sashihara

    (Princeton Consultants, Princeton, New Jersey 08540)

Abstract

The U.S. Census Bureau conducts a census of population and housing every 10 years as mandated in the U.S. Constitution. Following up in person with households that do not respond online, by phone, or by mail, which is known as nonresponse follow-up (NRFU), represents a major component of this effort. For the 2010 Census, the Census Bureau equipped enumerators with paper maps and notebooks filled with questionnaires and required enumerators to go door to door and collect decennial census data. The enumerators met daily with their supervisors to return completed questionnaires and update payroll information. For the 2020 Census, an advanced analytics solution, utilizing machine learning and optimization techniques, drove a reengineering of the entire field operations process, leading to substantially reduced costs and improved productivity. These reengineering efforts included business processes and technology centered around the development of this solution, the MOJO Optimizer, and resulted in an 80% increase in the number of cases completed per hour (from 1.05 to 1.92) and a 27% decrease in the number of miles reimbursed per case (from 5.05 to 3.68) compared with the 2010 Census NRFU. Capitalizing on the massive innovations realized during decennial census operations, the Census Bureau intends to use this technology to revolutionize its over 90 active surveys.

Suggested Citation

  • Tamara Adams & Alessandro Ferrucci & Pedro Carvalho & Sothiara Em & Benjamin Whitley & Ryan Cecchi & Teresa Hicks & Alexander Wooten & John Cuffe & Stephanie Studds & Irvin Lustig & Steve Sashihara, 2023. "Advanced Analytics Drives Reengineering of Field Operations for the 2020 U.S. Census," Interfaces, INFORMS, vol. 53(1), pages 47-58, January.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:1:p:47-58
    DOI: 10.1287/inte.2022.1146
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    References listed on IDEAS

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    1. Chuck Holland & Jack Levis & Ranganath Nuggehalli & Bob Santilli & Jeff Winters, 2017. "UPS Optimizes Delivery Routes," Interfaces, INFORMS, vol. 47(1), pages 8-23, February.
    2. Gunawan, Aldy & Lau, Hoong Chuin & Vansteenwegen, Pieter, 2016. "Orienteering Problem: A survey of recent variants, solution approaches and applications," European Journal of Operational Research, Elsevier, vol. 255(2), pages 315-332.
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