Leveraging large language models to enhance multi-agent risk assessment in supply chain networks

Key Points

• The research aims to explore how large language models can improve risk assessment processes in supply chain networks involving multiple agents.
• Utilized large language models to analyze data from supply chain networks.
• Implemented multi-agent simulations to evaluate risk assessment strategies.
• Conducted comparative analyses to determine effectiveness.
• Enhanced accuracy of risk predictions in multi-agent scenarios.
• Improved decision-making capabilities within supply chain systems.
• Demonstrated potential for significant efficiency gains.