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Selecting key performance indicators for production with a linear programming approach

Author

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  • Nicole Stricker
  • Fabio Echsler Minguillon
  • Gisela Lanza

Abstract

Modern production systems are prone to disruptions due to shorter product life cycles, growing variant diversity and progressively distributed production. At the same time, reduced time and capacity buffers diminish mitigation opportunities, requiring better tools for production control. Performance measurement with key performance indicators (KPIs) is a widely used instrument to detect changes in production system performance in order to coordinate appropriate countermeasures. The main challenge in planning KPI systems consists in determining relevant KPIs. On the one hand, enough KPIs must be selected for a sufficiently high information content. On the other hand, the cognitive abilities of users are not to be overstrained by selecting too many KPIs. This tradeoff is addressed in a proposed selection process using an integer linear programme for objective KPI selection. In order to achieve this goal, crucial facets of the information content requirement are formalised mathematically. The developed method is validated using a practical application example, showing the influence of model parameter selection on optimisation results. The formalisation of the information content is shown to be a novel and promising approach.

Suggested Citation

  • Nicole Stricker & Fabio Echsler Minguillon & Gisela Lanza, 2017. "Selecting key performance indicators for production with a linear programming approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5537-5549, October.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:19:p:5537-5549
    DOI: 10.1080/00207543.2017.1287444
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    Cited by:

    1. Ivo Hristov & Antonio Chirico & Riccardo Camilli, 2022. "The role of Key Performance Indicators as a performance management tool in implementing corporate strategies: A critical review of the literature," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2022(1), pages 117-151.
    2. Diogo Rodrigues & Radu Godina & Pedro Espadinha da Cruz, 2021. "Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    3. Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 2021. "Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Artur Dmowski & Jakub Bis, 2021. "An Optimal Algorithm of Material Reserves Management based on Probabilistic Model," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 179-188.

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