LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks
Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings. This paper proposes an evidence-grounded framework that jointly performs supply chain bottleneck prediction and faithful natural-language risk explanation by coupling a Temporal Graph Attention Network (TGAT) with a structured large language model (LLM) reasoning module. Using maritime hubs as a representative case study for global supply chain nodes, daily spatial graphs are constructed from Automatic Identification System (AIS) broadcasts, where inter-node interactio...
| Authors | Zhiming Xue, Yujue Wang, Menghao Huo |
|---|---|
| Primary category | cs.AI |
| Published | 5 March 2026 |
| Last updated at source | 21 July 2026 |
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