Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment

TL;DR

This work tackles risk assessment for deploying hyperconnected logistic hub networks in VUCA environments by introducing an LLM-driven pipeline that orchestrates multiple analytical tools to process unstructured data. The framework enables systematic risk identification, daily-to-yearly risk aggregation, and risk-based clustering of hubs, leveraging long-term memory for scalability and explainability for decision support. Case study results from a Georgia-based testbed show that the approach can reveal distinct risk clusters that align with geographic patterns, guiding regionally targeted mitigation and resource allocation. Overall, the method provides a scalable, interpretable, and data-driven path toward risk-aware hub deployment in physical internet-inspired supply chains, with potential extensions to dynamic risk factors and multi-agent architectures.

Abstract

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.

Figures (4)

• Figure 1: Hyperconnected logistic hub networks.
• Figure 2: Workflow of the multi-tool integrated framework for risk assessment in logistic hub deployment.
• Figure 3: An example response to identify risk types for logistic hubs in Georgia using Traffic Status Tool.
• Figure 4: Risk-based hub similarities and clusters.