Manufacturing Technology 2024, 24(3):440-447 | DOI: 10.21062/mft.2024.050

Proposal for Evaluating the Efficiency of Production Processes Using External and Internal Key Performance Indicators

Erika Sujová ORCID...1, Daniela Vysloužilová ORCID...2, Ivan Babic1
1 Faculty of Technology, Technical University in Zvolen, Studentska 26, 960 01 Zvolen, Slovak Republic
2 Faculty of Mechanical Engineering, Jan Evangelista Purkyně University in Ústí nad Labem, Pasteurova 3334/7, 400 96 Ústí nad Labem, Czech Republic

The paper focuses on proposing a method for implementing key performance indicators (KPIs) to assess the effectiveness of manufacturing processes. For the evaluated processes of precision parts machining, the share of non-conforming products was proposed as a KPI, evaluated as both an exter-nal and an internal indicator. The external indicator EXTppm expressed the quantity of faulty prod-ucts to the volume of production. Its monthly development during 2022 was evaluated. The internal KPI represented the internal share of non-conforming products INTppm during 2022 which was re-lated to the order of part A. Towards the conclusion causes for not attaining the targeted KPI values are pinpointed, and recommendations are put forth to enhance the productivity of manufacturing processes.

Keywords: Key Performance Indicator (KPI), Process Performance, Faulty Product, Non-conforming Product, Maintenance
Grants and funding:

The authors would like to acknowledge the Agency APPV for supporting the project APVV-20-0403 “FMA analysis of potential signals suitable for adaptive control of nesting strategies for milling wood-based agglomerates“
The paper was created as a part of the KEGA project 007TU Z-4/2023 "Innovation and Educational Support of Subjects in the Field of Technical Diagnostics of Agricultural and Forestry Machinery with a Focus on Practice"
This publication is the result of the implementation of the project “Progressive Research into Utility Properties of Materials and Products Based on Wood (LignoPro)”, ITMS 313011T720 supported by the Operational Programme Integrated Infrastructure (OPII) funded by the ERDF

Received: January 5, 2024; Revised: May 20, 2024; Accepted: May 21, 2024; Prepublished online: May 21, 2024; Published: July 1, 2024  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Sujová E, Vysloužilová D, Babic I. Proposal for Evaluating the Efficiency of Production Processes Using External and Internal Key Performance Indicators. Manufacturing Technology. 2024;24(3):440-447. doi: 10.21062/mft.2024.050.
Download citation

References

  1. FARELL, M. J. (1957). The Measurement of Productive Efficiency. In: Journal of the Royal Statistical Society. Series A (general), Year 120, Number 3, pp. 253-290. https://doi.org/10.2307/2343100. ISSN 0964-1998. Go to original source...
  2. STN EN ISO 9004:2018 Manažérstvo trvalého úspechu organizácie. Prístup na základe manažérstva kvality.
  3. HELMOLD, M., TERRY, B. (2021). Operations and Supply as Integral Part of the Corporate Strategy. Operations and Supply Management 4.0: Industry Insights, Case Studies and Best Practices; Future of Business and Finance; Springer International Publishing: Cham, Switzerland, pp. 85-95. ISBN 978-3-030-68696-3. Go to original source...
  4. MIDOR, K. et al. (2020). Key Performance Indicators (KPIs) as a Tool to Improve Product Quality. In: New Trends in Production Engineering, 2020, 3(1) 347-354. https://doi.org/10.2478/ntpe-2020-0029. ISSN 2545-2843 Go to original source...
  5. LINDBERG, C. F., TAN, S., YAN, J., STARFELT, F. (2015). Key performance indicators improve industrial performance. In: Energy procedia, 75, 1785-1790. ISSN 1876-6102. Go to original source...
  6. RODRIGUES, D., GODINA, R., DA CRUZ, P. E. (2021). Key performance indicators selection through an analytic network process model for tooling and die industry. In: Sustainability, 13(24), 13777. ISSN 2071-1050. Go to original source...
  7. RAMIS FERRER, B., MUHAMMAD, U., MOHAMMED, W. M., MARTÍNEZ LASTRA, J. L. (2018). Implementing and visualizing ISO 22400 key performance indicators for monitoring discrete manufacturing systems. In: Machines, 6(3), 39. ISSN 2075-1702. Go to original source...
  8. LAMBÁN, M. P., MORELLA, P., ROYO, J., SÁNCHEZ, J. C. (2022). Using industry 4.0 to face the challenges of predictive maintenance: A key performance indicators development in a cyber physical system. In: Computers & Industrial Engineering, 171, 108400. ISSN 0360-8352. Go to original source...
  9. NOLAN, D.P., ANDERSON, E.T. (2015). OE/SHE Key Performance Indicators (KPIs). Applied Operational Excellence for the Oil, Gas, and Process In-dustries; Gulf Professional Publishing: Houston, TX, USA, 2015; pp. 147-163. ISBN 978-0-12-802788-2. Go to original source...
  10. BIALY, W., (2020). Improvement of Production System Reliability Using Selected KPIs. In: Conference Quality Production Improvement - CQPI, 2(1) pp. 204-213. https://doi.org/10.2478/cqpi-2020-0023. ISSN 2657-8603. Go to original source...
  11. FERREIRA, S., SILVA, F.J.G., CASAIS, R.B., PEREIRA, M.T., FERREIRA, L.P. (2019). KPI Development and Obsolescence Management in Industrial Maintenance. IN: Procedia Manuf. , 38, pp1427-1435. https://doi.org/10.1016/j.promfg.2020.01.145. ISSN 2351-9789. Go to original source...
  12. KARIMI, J. et all. (2001). Impact of information technology management practices on customer service. In: Journal of Management Information Systems. Year 2001, Number 17.4, pp. 125-158. ISSN 0742-1222. Go to original source...
  13. REN, Ch., WANG, W., DONG, J., DING, H., SHAO, B., WANG, Q. (2008). Towards a flexible business process modeling and simulation environment. In: Proceedings - WinterSimulationConference. 1694-1701. https://doi.org/10.1109/WSC.2008.4736255. Go to original source...
  14. GARCIA, C. A., CAIZA, G., GUIZADO, D., NARANJO, J. E., ORTIZ, A., AYALA, P., GARCIA, M. V. (2023). Visualization of Key Performance Indicators in the Production System in the Context of Industry 4.0. In: IFAC-PapersOnLine, Volume 56, Issue 2, 2023, pp 6582-6587. https://doi.org/10.1016/j.ifacol.2023.10.310 Go to original source...
  15. KAGANSKI, S., MAJAK, J., KARJUST, K., TOOMPALU, S. (2017) Implementation of Key Performance Indicators Selection Model as Part of the Enterprise Analysis Model. In: Procedia CIRP, Volume 63, 2017, pp 283-288. https://doi.org/10.1016/j.procir.2017.03.143 Go to original source...
  16. DIAN, M. (2013). The Methodology of Quality Matrix in Manufacturing Quality Process Improvements. In: Manufacturing Technology, December 2013, Vol. 13, No. 4. https://doi.org/ 10.21062/ujep/x.2013/a/1213-2489/MT/13/4/431. ISSN 1213-2489. Go to original source...
  17. LEGÁT, V., ALEŠ, Z., HLADÍK, T. (2017) Maintenance Audit: The Tool for Maintenance Management Quality of Manufacturing Equipment. In: Manufacturing Technology, February 2017, Vol. 17, No. 1. https://doi.org/ 10.21062/ujep/x.2017/a/1213-2489/MT/17/1/53. ISSN 1213-2489. Go to original source...
  18. CONTINI, G., PERUZZINI, M. (2022) Sustainability and Industry 4.0: Definition of a Set of Key Performance Indicators for Manufacturing Companies. In: Sustainability, 2022, 14(17), 11004 https://doi.org/10.3390/su141711004. ISSN 2071-1050. Go to original source...
  19. BABIC, I. (2023). Implementácia kľúčových ukazovateľov hodnotenia výkonnosti výrobných procesov. Bakalárska práca. Technická univerzita vo Zvolene, Slowakia FT - 104081 - 17834.
  20. MUKHERJEE, I., RAY, P. K. (2006). A review of optimisation techniques in metal cutting process. In: Computers& Industrial Engineering, pp 15-34. https://doi.org/10.1016/j.cie.2005.10.001. ISSN 0360-8352. Go to original source...
  21. RUIYU, Y. (2011). Metallurgical Process Engineering. Berlin: Metallurgical Industry Press, Berlin. 400 pp. ISBN 978-3-642-13956-7.
  22. SUJOVÁ, E., VYSLOUŽILOVÁ, D., KOLEDA, P., GAJDZIK, B.: Research on the Evaluation of the Efficiency of Production Processes Through the Implementation of Key Performance Indicators. In: Management Systems in Production Engineering. 2023, Volume 31, Issue 4, 404-410, ISSN 2450-5781, OPEN ACCESS. Go to original source...

This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.