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A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system

Author

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  • Ehsan Pourjavad

    (University of Regina)

  • Rene V. Mayorga

    (University of Regina)

Abstract

In today’s competitive environment, measuring companies’ performance properly has become a vital subject not only for investors but also for the companies that are working in the same sector. The achieved results of performance measurement can help managers to identify means of improvement, measure progress and find unknown problems in the company. There are many efficiency frontier analysis methods to evaluate performance; but, each of these methods has its strength as well as major limitations. In this article, a fuzzy approach based on Mamdani fuzzy inference system is presented for performance measurement of manufacturing systems. The generation of fuzzy rules is the biggest consideration in designing the proposed model. In fact, fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multi-criteria decision-making methods. A fuzzy inference system is constructed and applied to measure the performance or efficiency of manufacturing systems. Implementation of the proposed model is analyzed and discussed using a real case. The results reveal the usefulness of the proposed model in evaluating the performance of manufacturing companies.

Suggested Citation

  • Ehsan Pourjavad & Rene V. Mayorga, 2019. "A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1085-1097, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1307-5
    DOI: 10.1007/s10845-017-1307-5
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    Cited by:

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    2. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    3. Amir Hossein Azadnia & Simon Stephens & Pezhman Ghadimi & George Onofrei, 2022. "A comprehensive performance measurement framework for business incubation centres: Empirical evidence in an Irish context," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2437-2455, July.

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