An Improved Multi-Criteria Method for Supplier Selection in Indian Automotive Manufacturing

International Journal of Mechanical Engineering

© 2025 by SSRG - IJME Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : Shankha Ghosh, Partha Sarathi Chakraborty, S. Nallusamy, M. Rajaram Narayanan

10.14445/23488360/IJME-V12I12P104

How to Cite?

Shankha Ghosh, Partha Sarathi Chakraborty, S. Nallusamy, M. Rajaram Narayanan, "An Improved Multi-Criteria Method for Supplier Selection in Indian Automotive Manufacturing," SSRG International Journal of Mechanical Engineering, vol. 12,  no. 12, pp. 31-44, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I12P104

Abstract:

In the medium-sized automotive sector, supplier selection is the most important factor in ensuring constant quality, cost management, and delivery performance. These sectors must deal with tight operational restrictions, interconnected criteria, and ambiguous information. As a result, choosing a supplier is a difficult choice. This article proposes an inventive strategy to address this issue over an integrated framework. 3.2. Fuzzy Analytic Hierarchy Process (FAHP) is used to calculate the criteria weights due to uncertainty, Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) is used to map cause and effect relationships, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS) are used to assess supplier performance. Sensitivity analysis evaluates the stability of the results, whereas the Borda and Copeland Count methods combine the rankings. The FAHP score, the important factors are cost 0.128, quality 0.279, and delivery reliability 0.130. According to FDEMATEL, lower-weighted criteria indicate cause, and higher-weighted criteria indicate effect. TOPSIS and EDAS considered S1 and S2 to be the best performers, whereas S4 frequently scored lowest. These opinions were validated by Borda and Copeland aggregation. S1 and S2 maintained their top positions in every scenario, according to a sensitivity study with a weight variation of ±20%. Based on the results of this study, the integrated model offers a robust and clear base for the selection of suppliers.

Keywords:

Supplier selection, MCDM, Rank Aggregation, Sensitivity analysis.

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