Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 2322-1305 14 4 2025 02 19 Modeling a sustainable and resilient supply chain in the automotive industry Modeling a sustainable and resilient supply chain in the automotive industry 379 406 220319 10.48313/jqem.2025.514088.1508 FA Seyedeh Mahboubeh Saeidifar Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran. Iraj Mahdavi Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran. 0000-0001-5123-6169 Ali Tajdin Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran. Nikbakhsh Javadian Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran. Journal Article 2024 06 26 Purpose: This work addresses the automotive industry with two important, evolving concepts: sustainability and resiliency. The proposed model is designed to balance economic, environmental, and social objectives while maintaining adaptability to potential supply chain changes and disruptions. A real automotive company is then investigated as the case study to assess the applicability, validity, and performance of the developed model and, eventually, render useful managerial and decision aids. Methodology: To achieve this objective, a comprehensive decision-making model has been developed. In the first stage, supplier evaluation is conducted based on sustainability and resilience criteria. This assessment employs two innovative decision-making approaches: the stochastic fuzzy Best-Worst Method (BWM) and stochastic VIKOR. In the subsequent stage, a multi-objective mathematical model is formulated by incorporating stochastic-fuzzy uncertainty. To solve the model, a fuzzy robust optimization approach combined with a modified multi-choice goal programming method based on a utility function is applied. Findings: In this study, the supply chain of SAIPA Kashan Automotive Company is analyzed across three key dimensions: general criteria, sustainability, and resilience. Indicators such as cost, quality, and reductions in energy consumption were identified as the most critical evaluation factors. Supplier evaluation and ranking were carried out using the fuzzy VIKOR method. The results indicate that, among the main suppliers, the second and fifth options, and among the backup suppliers, the second option, received the highest scores. Furthermore, to assess the robustness and validity of the proposed approach, the results were compared with those obtained using conventional methods. Originality/Value: The value of this research lies in presenting a comprehensive decision-making model under uncertainty and in improving supply chain performance across economic, environmental, and social perspectives. The findings can significantly assist managers and policymakers in the automotive industry in addressing complex supply chain challenges. Purpose: This work addresses the automotive industry with two important, evolving concepts: sustainability and resiliency. The proposed model is designed to balance economic, environmental, and social objectives while maintaining adaptability to potential supply chain changes and disruptions. A real automotive company is then investigated as the case study to assess the applicability, validity, and performance of the developed model and, eventually, render useful managerial and decision aids. Methodology: To achieve this objective, a comprehensive decision-making model has been developed. In the first stage, supplier evaluation is conducted based on sustainability and resilience criteria. This assessment employs two innovative decision-making approaches: the stochastic fuzzy Best-Worst Method (BWM) and stochastic VIKOR. In the subsequent stage, a multi-objective mathematical model is formulated by incorporating stochastic-fuzzy uncertainty. To solve the model, a fuzzy robust optimization approach combined with a modified multi-choice goal programming method based on a utility function is applied. Findings: In this study, the supply chain of SAIPA Kashan Automotive Company is analyzed across three key dimensions: general criteria, sustainability, and resilience. Indicators such as cost, quality, and reductions in energy consumption were identified as the most critical evaluation factors. Supplier evaluation and ranking were carried out using the fuzzy VIKOR method. The results indicate that, among the main suppliers, the second and fifth options, and among the backup suppliers, the second option, received the highest scores. Furthermore, to assess the robustness and validity of the proposed approach, the results were compared with those obtained using conventional methods. Originality/Value: The value of this research lies in presenting a comprehensive decision-making model under uncertainty and in improving supply chain performance across economic, environmental, and social perspectives. The findings can significantly assist managers and policymakers in the automotive industry in addressing complex supply chain challenges.

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