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A New Extension to a Multi-Criteria Decision-Making Model for Sustainable Supplier Selection under an Intuitionistic Fuzzy Environment

Author

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  • Patchara Phochanikorn

    (School of Business, Central South University, Changsha 410083, China)

  • Chunqiao Tan

    (School of Business, Nanjing Audit University, Nanjing 211815, China)

Abstract

The increase of environmental pollution has led to the rise of sustainable awareness in recent years. This trend has motivated various industries to recognize the importance of implementing sustainable supply chain practices to seek economic, environmental and social advantages. From a sustainability perspective, selecting a suitable supplier is the main component of modern enterprises. It is also a challenging problem since several criteria concerning supplier selection are interdependent with a complex character. Therefore, the contribution of this paper is a new extension to multi-criteria decision-making model (MCDM) under an intuitionistic fuzzy environment for sustainable supplier selection (SSS) based on sustainable supply chain management SSCM practices. It consists of intuitionistic fuzzy set theory (IFS) with a decision making trial and evaluation laboratory (DEMATEL) combined with an analytic network process (ANP) to identify uncertainties and interdependencies among criteria as well as analyzing the criteria weights. We modified Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to evaluate and rank the desired level of sustainable supplier performance. The suggested approach is conducted by a case study from the Thailand palm oil industry. Results show that the proposed model not only can find the most suitable sustainable supplier, but also the enterprises can aid their suppliers in improving sustainability by using the proposed method and can improve enterprises’ socio-environmental performance, which is key to achieving sustainable development.

Suggested Citation

  • Patchara Phochanikorn & Chunqiao Tan, 2019. "A New Extension to a Multi-Criteria Decision-Making Model for Sustainable Supplier Selection under an Intuitionistic Fuzzy Environment," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5413-:d:272267
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    References listed on IDEAS

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    Cited by:

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    3. Ananna Paul & Nagesh Shukla & Sanjoy Kumar Paul & Andrea Trianni, 2021. "Sustainable Supply Chain Management and Multi-Criteria Decision-Making Methods: A Systematic Review," Sustainability, MDPI, vol. 13(13), pages 1-28, June.
    4. Koray Altintas & Ozalp Vayvay & Sinan Apak & Emine Cobanoglu, 2020. "An Extended GRA Method Integrated with Fuzzy AHP to Construct a Multidimensional Index for Ranking Overall Energy Sustainability Performances," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    5. Alina Alb Lupaş, 2023. "Fuzzy Differential Subordination and Superordination Results for Fractional Integral Associated with Dziok-Srivastava Operator," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    6. Fatih Ecer, 2022. "Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer," Operational Research, Springer, vol. 22(1), pages 199-233, March.
    7. Sophia Xiaoxia Duan & Santoso Wibowo & Josephine Chong, 2021. "A Multicriteria Analysis Approach for Evaluating the Performance of Agriculture Decision Support Systems for Sustainable Agribusiness," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
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    9. Qaisar Khan & Jeonghwan Gwak & Muhammad Shahzad & Muhammad Kamran Alam, 2021. "A Novel Approached Based on T-Spherical Fuzzy Schweizer-Sklar Power Heronian Mean Operator for Evaluating Water Reuse Applications under Uncertainty," Sustainability, MDPI, vol. 13(13), pages 1-35, June.
    10. 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.
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