用大模型分析物流枢纽部署风险，自动识别地缘、金融等潜在威胁
Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment
- 大模型驱动的风险评估流水线，整合多种分析工具
- 通过风险相似性聚类，实现枢纽风险画像与匹配
- 适合供应链规划、风险管理及智能决策领域研究者
全球供应链对能效与环境可持续性的重视，给超连接物流枢纽网络的部署带来新挑战。在当前动荡、不确定、复杂且模糊（VUCA）的环境下，动态风险评估对枢纽成功部署至关重要。然而传统方法难以有效捕捉和分析非结构化信息。本文设计了一种基于大语言模型（LLM）的驱动风险评估框架，集成多种分析工具，以系统化方式识别潜在风险，涵盖地缘政治不稳定、金融趋势、历史风暴事件、交通状况及新闻源中的新兴风险。这些数据经由一套分析工具处理，由LLM自动调用，支持结构化、数据驱动的枢纽选择决策。此外，我们设计提示词引导LLM利用工具评估各类风险类型与水平的可行性。通过基于风险的相似性分析，LLM可将风险特征相近的物流枢纽聚类，实现结构化风险评估。该框架具备可扩展性与长期记忆能力，通过解释与解读增强决策，为超连接供应链网络中的物流枢纽部署提供全面风险评估支持。
原文摘要 · Abstract (English)
The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networks. In current volatile, uncertain, complex, and ambiguous (VUCA) environments, dynamic risk assessment becomes essential to ensure successful hub deployment. However, traditional methods often struggle to effectively capture and analyze unstructured information. In this paper, we design an Large Language Model (LLM)-driven risk assessment pipeline integrated with multiple analytical tools to evaluate logistic hub deployment. This framework enables LLMs to systematically identify potential risks by analyzing unstructured data, such as geopolitical instability, financial trends, historical storm events, traffic conditions, and emerging risks from news sources. These data are processed through a suite of analytical tools, which are automatically called by LLMs to support a structured and data-driven decision-making process for logistic hub selection. In addition, we design prompts that instruct LLMs to leverage these tools for assessing the feasibility of hub selection by evaluating various risk types and levels. Through risk-based similarity analysis, LLMs cluster logistic hubs with comparable risk profiles, enabling a structured approach to risk assessment. In conclusion, the framework incorporates scalability with long-term memory and enhances decision-making through explanation and interpretation, enabling comprehensive risk assessments for logistic hub deployment in hyperconnected supply chain networks.
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