A Supply Chain Digital Twins Framework with Generative Knowledge Graph

With the explosive development of Artificial intelligence generated content(AIGC), generative AI technology has gradually been applied to supply chain optimization and management. However, current research lacks guiding principles and general framework for the application of generative AI in supply chain system optimization and management. To address this issue, this paper designs the supply chain digital twins framework with generative knowledge graph(SCDT-GKG), based on digital twin technology.Firstly, a foundational architecture for supply chain digital twins is proposed based on a five-dimensional model. Secondly, on this foundational architecture, a generative knowledge graph module is introduced to construct SCDT-GKG. Lastly, a general usage guide is provided, detailing the application processes in supply chain scenarios, thereby offering guidance for the construction and use of SCDT-GKG.This research effectively addresses the challenges of applying generative knowledge graph in supply chain systems and the issue of insufficient data. It also aids in achieving real-time collaborative optimization of supply chain system, paving the way for new method.