Research on Digital Supply Chain Optimization Based on Machine Learning

This paper proposes a system framework that combines machine learning algorithms with digital supply chain optimization design, aiming to improve the overall performance of the supply chain in a data-driven way. The system first collects real-time data from all links of the supply chain, including demand forecasting, inventory management, production scheduling and other information, and uses supervised learning and unsupervised learning algorithms in machine learning for data analysis and pattern recognition. The optimization model uses reinforcement learning algorithms for scheduling optimization to minimize inventory costs, transportation costs and production cycles in the supply chain, thereby improving the overall operational efficiency of the supply chain. The simulation part of the system uses a simulation model based on historical data, and analyzes the performance of the optimized supply chain model in actual operation by adjusting different optimization strategies. The simulation results show that the supply chain optimized by the machine learning algorithm can significantly reduce the total cost and improve resource utilization compared with the traditional management method. Data analysis further verifies the effectiveness of the optimization design. The results show that inventory and transportation costs are reduced by 12% and 8% respectively, and production efficiency is increased by 15%.