Proposing a data-driven decision-making model for evaluating sustainable and resilient suppliers in the automotive industry

Document Type : Original Article

Authors

Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran.

Abstract
Purpose: In light of the growing challenges in today's supply chains, including market fluctuations, increasing environmental and social pressures, and the need to enhance resilience against foreseeable crises such as the COVID-19 pandemic and economic disruptions, the strategic importance of selecting suppliers that simultaneously meet sustainability and resilience criteria has become more prominent. Accordingly, the main objective of this study is to present a comprehensive, data-driven, and forward-looking decision-making model for evaluating and selecting suppliers within the supply chain, accounting for multiple dimensions of sustainability and resilience simultaneously.
Methodology: In the proposed model, the weights of the defined criteria and sub-criteria were initially determined using the Stochastic Best-Worst Method (SBWM). Supplier performance was then evaluated using the Stochastic VIKOR Multi-Criteria Decision-Making (MCDM) method. In the final stage, the Random Forest regression algorithm was applied to predict future supplier performance. The model was tested through a case study conducted at SAIPA Kashan Automotive Company using expert input collected via structured questionnaires.
Findings: Sustainability and resilience criteria play a central role in supplier selection in the automotive industry. Among the sub-criteria, "greenhouse gas emissions" and "energy consumption reduction" were most influential due to environmental regulations. At the same time, "cost" and "safety stock level" had the greatest impact due to their direct effect on economic performance and operational continuity. Furthermore, the Random Forest algorithm achieved high predictive accuracy (RMSE = 0.0976), confirming the model's ability to generate reliable, data-driven forecasts.
Originality/Value: Although each of the methods used in this research (Random Best-Worst Method, Random VIKOR, and Random Forest algorithm) has been employed individually in previous studies, the main innovation of this study lies in presenting an integrated framework that combines all three approaches. In fact, this research is the first to merge MCDM methods with a machine learning algorithm, offering a comprehensive, data-driven decision-making model. This model not only assesses the current performance of suppliers but also enables prediction of their future performance. Such a combination has not previously been introduced in the supplier selection literature with a simultaneous focus on supply chain sustainability and resilience in the automotive industry, marking a clear methodological innovation.

Keywords


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