Background and Objectives: Today, modern technologies and digital tools, relying on intelligent algorithms, enable the optimization of sustainable supply chain management systems. This approach increases efficiency and reduces production and distribution risks and disruptions by predicting risks, improving decision-making, and implementing preventive maintenance. Therefore, the aim of this study was to identify and rank the factors influencing the optimization of technology-based sustainable supply chain management systems.
Materials and Methods: This study, in terms of methodology, is based on the Structural Cross-Impact Matrix Analysis with a futures studies approach and is applied in nature. The statistical population consisted of 25 senior supply chain managers in Iran’s steel industry. Data were collected through library research, structured interviews, and a Structural Cross-Impact Matrix scoring questionnaire, and were analyzed using MICMAC statistical software.
Results: The findings showed that training and developing technology-oriented decision-making skills among supply chain human resources, developing smart supply chain management systems using predictive algorithms, and applying artificial intelligence analytics in supply chain decision-making systems are the most influential factors in optimizing technology-based sustainable supply chain management systems.
Conclusion: This study showed that it can identify the opportunities and challenges of Iran’s steel industry and, by providing appropriate strategies, improve supply chain efficiency and resilience. Employing strategic foresight also makes it possible to predict environmental changes and emerging technologies, facilitating better adaptation of the steel industry to global developments and supporting sustainable development.