The current state of sustainability is promoting the status of the supply chain from traditional economic objectives related to the cost, quality, and time to the multidimensional opportunities in terms of economic, social and environmental fronts. The paper deals with the development of a decision support framework for the prioritization of suppliers on sustainability factors. The framework is based on the combined approach of the analytical hierarchical process (AHP) and the fuzzy inference system (FIS) to evaluate the supplier for the benefit of the manufacturer. The role of AHP is to select the significant factors as criteria, while the FIS mechanism works to measure the sustainability index of each supplier for prioritization from the combined effect of the selected factors. In the ranking process, experts’ opinions on the importance of deciding the criteria (developed by the AHP) are considered in linguistic terms. To handle the subjectivity of decision makers assessments, fuzzy logic has been applied using FIS. In addition, uncertainties in the decision making support system are overcome by considering the fuzzy set theory for the selected sustainable factors. A numerical experiment is carried out to consider seven suppliers working with the goalkeeping gloves manufacturing firm for the pragmatic application of the proposed framework. The methodology of the integrated AHP–FIS approach is utilized to rank the suppliers by calculating the sustainability index value. The proposed approach provides a platform for the manufacturer to better understand the capability, sustainable suppliers must possess to continue working with them for the sustainable supply chain management.
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Acknowledgements. We thank the volunteer designers that participated in our study. Clarisse de Souza and Luciana Salgado thank the National Council for Scientific and Technological Development (CNPq) and the Research Foundation of the State of Rio de Janeiro (FAPERJ) for financial support at different stages of this research project.
© 2021, Taiwan Fuzzy Systems Association.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence