Smart Supply Chain Optimization Using IoT and Predictive Analytics

Modern supply chains operate in highly dynamic environments where uncertainty in demand, transportation delays, and inventory imbalances can significantly reduce operational efficiency. Traditional supply chain management systems rely heavily on historical data and manual decision making, which limits their ability to respond to real time disruptions. This paper proposes a smart supply chain optimization framework that integrates Internet of Things (IoT) technologies with predictive analytics to enhance visibility, responsiveness, and decision accuracy across the supply chain. IoT enabled sensors continuously collect real time data related to inventory levels, environmental conditions, transportation status, and equipment performance. Predictive analytics models analyze this data to forecast demand fluctuations, detect potential disruptions, and recommend proactive optimization strategies. The proposed approach supports data driven decisions for inventory management, logistics planning, and risk mitigation. Experimental analysis demonstrates that the integration of IoT and predictive analytics significantly improves forecasting accuracy, reduces stockouts and excess inventory, and enhances overall supply chain efficiency. The results confirm that smart, predictive supply chains are essential for achieving resilience and sustainability in modern logistics networks.