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• W4280613770 abstract "In this study, it is aimed to contribute to the development of companies in order to reach the desired values utilizing the cluster features of the suppliers in a sustainable supplier evaluation process and to guide the supplier in this regard. The clustering features acquired at the end of the study will guide the determination of sustainable supplier criteria and potential new suppliers according to the sector and business line in future studies. The first step of the three-stage model is to apply the best–worst method to determine the weights of the criteria. At the second stage, suppliers are clustered together by the k-means algorithm and silhouette index method. At the third stage, suppliers are divided into categories, and suggestion policies for each supplier category are included. Then, sensitivity analysis has been performed to analyze the effects of variations of criteria weights on the result of the proposed model. Finally, to express the integrated method, a real-life sustainable supplier selection problem is solved and suppliers are categorized according to their similarities. Following evaluation, it was seen that the environmental management system and waste management criteria were the most critical criteria in the categorization of suppliers. When its reasons are investigated, sectoral characteristics come into prominence. The results indicate that the proposed model fits well as a multicriteria decision-making tool for sustainable supplier selection and evaluation decisions. Finally, it is predicted that this model will evaluate new suppliers or new products/services to be procured and selected from the set of features." @default.
• W4280613770 created "2022-05-22" @default.
• W4280613770 creator A5013881526 @default.
• W4280613770 date "2022-05-13" @default.
• W4280613770 modified "2023-10-16" @default.
• W4280613770 title "A Novel Perspective to Sustainable Supplier Performance Evaluation Problem: A Case Study Based on Determining the Optimum Number of Clusters" @default.
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• W4280613770 doi "https://doi.org/10.1142/s0219622022500195" @default.
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