Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

A Sustainable Machine Learning Approach to Supply Chain Risk Management: A Case Study of Saipa Press

Document Type : Research Paper

Authors
1 Department of Industrial Management, ST.C., Islamic Azad University, Tehran,Iran
2 Department of Industrial Management, Islamic Azad university, ST.C., Tehran, Iran
3 Department of Industrial Management, Islamic Azad University, ST.C., Tehran, Iran
Abstract
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.
Keywords
Subjects

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Volume 17, Issue 3 - Serial Number 3
Summer 2025
Pages 122-144

  • Receive Date 17 February 2024
  • Revise Date 13 March 2024
  • Accept Date 27 September 2024