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An improvement selection methodology for key performance indicators

Author

Listed:
  • Andrew J. Collins

    (Old Dominion University)

  • Patrick Hester

    (Old Dominion University)

  • Barry Ezell

    (Old Dominion University)

  • John Horst

    (National Institute for Standards and Technology (NIST))

Abstract

Key performance indicators (KPIs) are critical measures for determining the health of a manufacturing plant in relationship to the plant’s goals. In today’s competitive environment, manufacturers cannot be careless about their business; in fact, they must ensure that their KPIs are effective and use them to make improvements when necessary. This paper describes a method for suggesting improvements to a manufacturer’s KPIs, based on the results achieved from a workshop to score the KPI on a number of predefined criteria. The approach uses a prospect theory approach to weight the scoring. Different problem formulations were derived that allow for both recommendations for improvements and the recommendations for disinvestments to over-performing KPIs. The authors applied the developed approach to two workshop outputs, each from independent manufacturers, and the results highlighted the significant difference between the two manufacturers in terms of improvement priorities and KPI assessment. The optimal improvement suggestions were compared to those found through a fast heuristic. It was determined that given the underlying assumptions of the approach that the heuristic solutions were just as adequate as the optimal ones.

Suggested Citation

  • Andrew J. Collins & Patrick Hester & Barry Ezell & John Horst, 2016. "An improvement selection methodology for key performance indicators," Environment Systems and Decisions, Springer, vol. 36(2), pages 196-208, June.
    • Handle: RePEc:spr:envsyd:v:36:y:2016:i:2:d:10.1007_s10669-016-9591-8
    DOI: 10.1007/s10669-016-9591-8
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    Cited by:

    1. Mária Ďurišová & Emese Tokarčíková & Florina Oana Virlanuta & Zuzana Chodasová, 2019. "The Corporate Performance Measurement and Its Importance for the Pricing in a Transport Enterprise," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    2. Man Ying (Annie) Ho & Joseph H. K. Lai & Huiying (Cynthia) Hou & Dadi Zhang, 2021. "Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey," Energies, MDPI, vol. 14(21), pages 1-30, November.
    3. Angelika Lepold & Norbert Tanzer & Paulino Jiménez, 2018. "Expectations of Bank Employees on the Influence of Key Performance Indicators and the Relationship with Job Satisfaction and Work Engagement," Social Sciences, MDPI, vol. 7(6), pages 1-13, June.
    4. Alexandre André Feil & Iane Brito Reiter & Renata Oberherr & Virgílio José Strasburg & Dusan Schreiber, 2022. "Analysis and measurement of the sustainability level in the furniture industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 13657-13682, December.
    5. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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