Applications of machine learning in supply chain management include demand forecasting, inventory optimization, and risk management. By analyzing historical data and identifying patterns, machine learning models predict future demand and streamline inventory management, reducing overstock or stockouts. Furthermore, these algorithms can assess risks in the supply chain by analyzing potential disruptions, allowing businesses to implement contingency plans proactively. Overall, machine learning enhances efficiency and decision-making across the supply chain, leading to cost reductions and improved service levels.