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Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection

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  • Gülşen Aydın Keskin

Abstract

Decision-making techniques are used to help evaluate the current suppliers’ aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.

Suggested Citation

  • Gülşen Aydın Keskin, 2015. "Using integrated fuzzy DEMATEL and fuzzy C: means algorithm for supplier evaluation and selection," International Journal of Production Research, Taylor & Francis Journals, vol. 53(12), pages 3586-3602, June.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:12:p:3586-3602
    DOI: 10.1080/00207543.2014.980461
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    Cited by:

    1. José Roberto Mendoza-Fong & Jorge Luis García-Alcaraz & José Roberto Díaz-Reza & Juan Carlos Sáenz Diez Muro & Julio Blanco Fernández, 2017. "The Role of Green and Traditional Supplier Attributes on Business Performance," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    2. Srđan Ljubojević & Dragan Pamučar & Dragutin Jovanović & Vujadin Vešović, 2019. "Outsourcing transport service: a fuzzy multi-criteria methodology for provider selection based on comparison of the real and ideal parameters of providers," Operational Research, Springer, vol. 19(2), pages 399-433, June.
    3. Amjad Hussain & Muhammad Umar Farooq & Muhammad Salman Habib & Tariq Masood & Catalin I. Pruncu, 2021. "COVID-19 Challenges: Can Industry 4.0 Technologies Help with Business Continuity?," Sustainability, MDPI, vol. 13(21), pages 1-25, October.
    4. Liu, Hu-Chen & Li, Zhaojun & Zhang, Jian-Qing & You, Xiao-Yue, 2018. "A large group decision making approach for dependence assessment in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 135-144.
    5. Xinping Wang & Cheng Zhang & Jun Deng & Chang Su & Zhenzhe Gao, 2022. "Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model," IJERPH, MDPI, vol. 19(12), pages 1-30, June.
    6. Kao-Yi Shen & Gwo-Hshiung Tzeng, 2018. "Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications," Sustainability, MDPI, vol. 10(5), pages 1-7, May.
    7. Vinay Surendra Yadav & A. R. Singh & Rakesh D. Raut & Naoufel Cheikhrouhou, 2023. "Blockchain drivers to achieve sustainable food security in the Indian context," Annals of Operations Research, Springer, vol. 327(1), pages 211-249, August.
    8. Miguel Ortiz-Barrios & Juan Cabarcas-Reyes & Alessio Ishizaka & Maria Barbati & Natalia Jaramillo-Rueda & Giovani Jesús Carrascal-Zambrano, 2021. "A hybrid fuzzy multi-criteria decision making model for selecting a sustainable supplier of forklift filters: a case study from the mining industry," Annals of Operations Research, Springer, vol. 307(1), pages 443-481, December.
    9. Seongtae Kim & M. Ramkumar & Nachiappan Subramanian, 2019. "Logistics service provider selection for disaster preparation: a socio-technical systems perspective," Annals of Operations Research, Springer, vol. 283(1), pages 1259-1282, December.

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