Core Insights - Traditional supply chain demand forecasting methods have significant limitations, relying heavily on human experience and simple statistical models, which struggle to adapt to sudden market fluctuations [1] - A breakthrough in material demand forecasting was achieved through a collaboration between RRS Supply Chain and China Mobile, resulting in the development of an AI-based forecasting project that won a second prize at the 18th Modern Logistics Technology Innovation Conference [1] - The project successfully integrated the Transformer model into material demand forecasting, allowing for precise capture of individual product demand patterns and insights into complex inter-product relationships, thus transforming supply chain decision-making from reactive to real-time and automated [1] Operational Impact - The project demonstrated significant operational improvements, achieving a monthly average purchase volume reduction of over 20% while maintaining year-on-year sales growth [2] - Inventory turnover days were drastically reduced from 120 days to 26 days, resulting in a 462% efficiency increase [2] - The total supply chain cost savings amounted to millions of yuan per month, greatly enhancing capital utilization efficiency and addressing long-standing discrepancies between forecasts and actual sales [2] Future Outlook - RRS Supply Chain aims to deepen its centralized and standardized management advantages in the telecommunications sector, further promoting intelligent supply chain upgrades to contribute to high-quality development in the industry [2] - China Mobile expressed its commitment to continue collaborating with RRS to drive innovation and deliver more technological benefits to society [2] - The project's value extends beyond technological breakthroughs, as it reconstructs traditional supply chain decision-making mechanisms by replacing experience-based judgments with data-driven approaches and algorithmic predictions, providing a replicable and scalable path for the industry's digital transformation [2]