Group 1 - The essence of digital supply chains lies in building capabilities for end-to-end integration and innovation, rather than merely applying specific technologies or tasks [2] - Three key suggestions for AI application in supply chains include establishing a foundation for data and process integration, avoiding an exclusive focus on large models, and combining AI with operations research for better decision-making [2][4] - The evolution of China's supply chain has gone through four stages, from execution-focused to strategic integration, user-centric digital connections, and now to building supply chain ecosystems [3] Group 2 - Current trends in supply chain management emphasize rapid response, resilience, reconstruction, and green low-carbon initiatives, all of which are fundamentally supported by digitalization [4] - Future supply chain capabilities will depend on deeper data integration and analysis, merging operations research models with AI technology to enhance decision-making processes [5] - Successful logistics companies have transitioned from human management to system management, focusing on optimizing data usage to improve employee experience and customer value [5] Group 3 - The diversification of channels leads to order fragmentation, with many brands unaware of the full fulfillment path, resulting in high costs and inefficiencies [6] - AI and big data analytics play a crucial role in optimizing supply chain operations, such as determining warehouse locations and balancing inventory levels [6] - Multi-point intelligent supply chains utilize AI for efficient route planning and inventory management, significantly improving operational metrics like delivery efficiency and stock availability [7]