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Competitiveness metrics for small and medium-sized enterprises through multi-criteria decision making methods and neural networks

Author

Listed:
  • Jones Luís Schaefer
  • Elpidio Oscar Benitez Nara
  • Julio Cezar Mairesse Siluk
  • Ismael Cristofer Baierle
  • Matheus Becker Da Costa
  • João Carlos Furtado

Abstract

This paper aims to present a way to obtain competitiveness metrics for small and medium-sized enterprises (SMEs) in a country with emerging characteristics. Key performance indicators (KPIs) were selected through a bibliographical research and fuzzy-Delphi method. Competitiveness rates were obtained modelling these KPIs through a hybrid approach between VIKOR and TODIM methods, and artificial neural networks (ANNs). A set of 18 KPIs to evaluate, monitor and control SMEs competitiveness was defined. Individual competitiveness rates (ICRs) were obtained for SMEs and an average of 78.33 with the ANN × VIKOR hybridisation and 81.61 with the ANN × TODIM hybridisation (on a scale from 0 to 100). This paper can serve as parameter for other studies related to competitiveness evaluation. Through the KPIs set, it is possible to define measurement parameters, translating into better control and optimising possibilities for SMEs competitiveness, being used for comparisons and benchmarks for other similar Brazilian or global SMEs.

Suggested Citation

  • Jones Luís Schaefer & Elpidio Oscar Benitez Nara & Julio Cezar Mairesse Siluk & Ismael Cristofer Baierle & Matheus Becker Da Costa & João Carlos Furtado, 2022. "Competitiveness metrics for small and medium-sized enterprises through multi-criteria decision making methods and neural networks," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 12(2), pages 184-207.
    • Handle: RePEc:ids:ijpmbe:v:12:y:2022:i:2:p:184-207
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    Cited by:

    1. Cruz, Yarens J. & Villalonga, Alberto & Castaño, Fernando & Rivas, Marcelino & Haber, Rodolfo E., 2024. "Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises," Operations Research Perspectives, Elsevier, vol. 12(C).

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