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A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

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  • Ningxuan Kang
  • Cong Zhao
  • Jingshan Li
  • John A. Horst

Abstract

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.

Suggested Citation

  • Ningxuan Kang & Cong Zhao & Jingshan Li & John A. Horst, 2016. "A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6333-6350, November.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:21:p:6333-6350
    DOI: 10.1080/00207543.2015.1136082
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    Cited by:

    1. Jones Luís Schaefer & Paulo Roberto Tardio & Ismael Cristofer Baierle & Elpidio Oscar Benitez Nara, 2023. "GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises," Administrative Sciences, MDPI, vol. 13(2), pages 1-16, February.
    2. Bustinza, Oscar F. & Opazo-Basaez, Marco & Tarba, Shlomo, 2022. "Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance," Technovation, Elsevier, vol. 118(C).
    3. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    4. Ciprian Cristea & Maria Cristea, 2021. "KPIs for Operational Performance Assessment in Flexible Packaging Industry," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    5. Cagno, Enrico & Accordini, Davide & Trianni, Andrea & Katic, Mile & Ferrari, Nicolò & Gambaro, Federico, 2022. "Understanding the impacts of energy efficiency measures on a Company’s operational performance: A new framework," Applied Energy, Elsevier, vol. 328(C).
    6. Wen, Xuanhao & Cao, Huajun & Hon, Bernard & Chen, Erheng & Li, Hongcheng, 2021. "Energy value mapping: A novel lean method to integrate energy efficiency into production management," Energy, Elsevier, vol. 217(C).
    7. Zhang, Haili & Song, Michael & Wang, Yufan, 2023. "Does AI-infused operations capability enhance or impede the relationship between information technology capability and firm performance?," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. 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.
    9. J. Vicente Tébar-Rubio & F. Javier Ramírez & M. José Ruiz-Ortega, 2023. "Conducting Action Research to Improve Operational Efficiency in Manufacturing: The Case of a First-Tier Automotive Supplier," Systemic Practice and Action Research, Springer, vol. 36(3), pages 427-459, June.

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