<![CDATA[This research investigates the optimization of supply chain efficiency through the application of advanced Decision Support Systems (DSS), focusing on minimizing operational costs while maintaining high service levels. The main objective is to explore how DSS, integrated with real-time data, artificial intelligence (AI), and machine learning (ML), can enhance decision-making processes across production, inventory management, and transportation. The research employs a multi-objective optimization model, developed to minimize production, inventory, transportation, and shortage costs, while dynamically adjusting decisions based on real-time demand and supply data. A numerical example is used to test the model’s effectiveness, revealing significant cost reductions in production and transportation but highlighting challenges in maintaining consistent service levels. The results indicate that DSS can substantially improve supply chain efficiency by enabling data-driven decisions in real time, though its adoption remains limited by technical and scalability challenges, particularly for small-to-medium enterprises (SMEs). This study contributes to the growing body of knowledge on supply chain optimization, offering practical insights into DSS implementation and its potential impact on operational performance. The conclusions suggest that future research should focus on developing more sophisticated DSS models capable of handling uncertainty, sustainability, and resilience, as well as enhancing scalability to make DSS more accessible to a broader range of businesses.]]>
