Application Of Mathematical Modeling In Supply Chain Management: A Review

Mathematical modeling plays a critical role in modern supply chain management (SCM) by enabling optimization, forecasting, and informed decision-making across complex networks. This review synthesizes recent advancements in mathematical modeling across five key domains of SCM: demand forecasting, inventory control, logistics and distribution, sustainability, and the integration of emerging technologies. We explore a wide array of modeling techniques, including time-series analysis, stochastic models, optimization algorithms, and machine learning, highlighting their applications, strengths, and limitations. Special attention is given to the evolving roles of artificial intelligence (AI), Internet of Things (IoT), and digital twins in enhancing model responsiveness and adaptability. Despite these advancements, challenges remain in achieving model transparency, scalability, and seamless integration with real-time technologies such as IoT and digital twins. Future research should focus on developing more interpretable machine learning models, improving data governance, and fostering cross-domain collaboration to enhance decision-making across the supply chain. This review provides a foundational understanding while identifying key gaps and future pathways for innovation in mathematical modeling for supply chain management.