Research on the Application of Large Models in Supply Chain Management

Abstract

With the rapid advancement of artificial intelligence technology, large language models (LLMs) are emerging as a revolutionary productivity tool, profoundly reshaping traditional operational paradigms in the field of supply chain management. This paper systematically examines the application value, implementation pathways, and future trends of LLM technology in supply chain management. Research indicates that LLMs, by leveraging their powerful capabilities in data processing, pattern recognition, and predictive analytics, demonstrate significant advantages in core supply chain processes such as demand forecasting, inventory optimization, supplier management, and logistics distribution. These capabilities enable precise market insights, real-time dynamic adjustments, and intelligent decision-making support. The study further highlights that the application of LLMs in the supply chain domain still faces multiple challenges, including data quality, privacy and security, technical implementation, and cost-effectiveness, necessitating targeted strategies for mitigation. Looking ahead, as the technology continues to mature and the ecosystem improves, LLMs are expected to drive supply chain management toward greater intelligence, collaboration, and sustainability, thereby creating substantial value for enterprises.