Evaluating Supplier Supply Chain Performance Using a Multi-Criteria Decision-Making Approach: Case Study in the Automotive Industry

Authors

  • Nabil Kayouh Abdelmalek Essaadi University, National School of Applied Sciences Tetouan, Morocco
  • Btissam Dkhissi Abdelmalek Essaadi University, National School of Applied Sciences Tetouan, Morocco

DOI:

https://doi.org/10.24425/mper.2025.154924

Abstract

Effective supply chain management is essential for business success and continuity. A key component of this management is supplier evaluation, which plays a vital role in mitigating logistical risks, optimizing value, and fostering long-term, mutually beneficial relationships within the supply chain. Although extensive research has been conducted, significant gaps re main in addressing sustainable supply chain risks and integrating them into supplier assessment frameworks. This study addresses this gap by proposing an integrated approach for evaluating and managing supplier-related logistics risks. The approach combines the Best-Worst Method (BWM) to assign relative weights to various sustainable supply chain risks with the fuzzy TOPSIS method to rank suppliers based on their risk profiles. A focus group is used to identify appropriate strategies to mitigate the identified risks. To demonstrate the practicality and effectiveness of the proposed framework, a real-world case study involving a multinational automotive company is presented. The results indicate that two specific suppliers require immediate attention and targeted risk mitigation strategies. This research provides supply chain managers with a robust evaluation methodology and actionable insights for improving supplier risk management in the automotive sector.

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Published

2025-06-30

How to Cite

Kayouh, Nabil, and Btissam Dkhissi. “Evaluating Supplier Supply Chain Performance Using a Multi-Criteria Decision-Making Approach: Case Study in the Automotive Industry”. Management and Production Engineering Review, vol. 16, no. 2, June 2025, pp. [nr art. 2], s. 1-17, doi:10.24425/mper.2025.154924.

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