This paper proposes a dynamic inventory optimization method for power materials supply chains leveraging Digital Twin (DT) technology and Model Predictive Control (MPC). Modern power systems involve complex supply networks for critical materials such as transformers, cables, and switchgear components, which face challenges from fluctuating demand, supplier lead times, logistical uncertainties, and operational costs. The DT collects real-time data on material consumption, equipment status, supplier performance, as well as transportation issues, providing a synchronized virtual representation of the supply chain. MPC utilizes this data to dynamically optimize inventory decisions, determining order quantities and timing to balance demand fulfillment and cost efficiency. A multi-objective optimization model is embedded to manage trade-offs among inventory holding costs, stockout risks, and delivery delays. By integrating predictive analytics with situational awareness, the proposed framework enhances supply chain resilience, minimizes material waste, improves response times, and ensures efficient operations across the power materials network. The approach establishes a foundation for intelligent, data-driven decision-making in future smart grid supply chain management.