فهرست نشریات علمی دسترسی آزاد ایران

Effective Supply Chain Risk Management in the Automotive Parts Industry has gained increasing importance due to technical complexity, dependence on multiple suppliers, and susceptibility to internal and external factors. The sustainability approach in this field not only reduces negative environmental and social impacts but also enhances the resilience of the supply chain. Therefore, this study was conducted to examine supply chain risk management with a sustainability approach at Saipa Press. This research is of a mixed type (qualitative-quantitative). In the qualitative phase, data were collected through semi-structured interviews with 10 industry and university experts who were selected purposefully. Based on the results, a conceptual model for risk management was outlined. In the quantitative phase, risk prioritization and assessment were conducted using machine learning algorithms (decision trees) and risk matrix analysis. The results showed that the most significant risks in Saipa Press's supply chain include fluctuations in raw material prices, supplier delays, demand instability, and regulatory changes. Utilizing the machine learning model significantly increased the accuracy of risk predictions and enabled the identification of hidden patterns in operational data. Furthermore, integrating the sustainability approach optimized production processes, reduced waste, and enhanced collaboration with suppliers. The findings of this study indicate that such an approach not only increases the resilience and competitiveness of the organization but also contributes to sustainable development by reducing negative environmental and social impacts. It is recommended that companies active in the automotive parts industry update their management strategies by investing in data-driven technologies.