Predictive Analysis Inventory Management and Intelligent Logistics Solutions from the Perspective of Power Supply Chain Optimization

As the core of national energy infrastructure, the power supply chain faces unprecedented challenges from global energy transformation, tech innovation, and market competition. This paper explores integrating predictive analysis, inventory management, and intelligent logistics to optimize it, enhancing efficiency, cutting costs, and boosting stability. Predictive analysis uses multi-dimensional models (ARIMA, LSTM) with 12 factors, achieving over 85% accuracy for core materials. Inventory management adopts ABC classification and a "dual-cross-linkage" mechanism, utilizing ¥30.32m surplus materials and releasing ¥35.79m funds (2023-2024). Intelligent logistics builds a 3-level IoT/AI platform, cutting transport costs by 15% and raising punctuality to 97.2%. Regional/provincial cases verify 192% higher inventory turnover, 75% shorter emergency time, and 8% lower carbon emissions. This research provides a replicable framework for global power supply chain digital transformation, aligning with energy security, "double carbon" and smart grid goals.