LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks

TL;DR

This paper introduces a novel framework combining Temporal Graph Attention Networks with large language models to provide interpretable, evidence-grounded early warnings for supply chain disruptions, validated on real-world maritime data.

Contribution

It develops an integrated approach that jointly predicts supply chain risks and generates faithful natural language explanations grounded in model evidence.

Findings

Achieved a test AUC of 0.761 and recall of 0.504 on real-world data.

Produced risk explanations with 99.6% directional consistency.

Outperformed baseline models in predictive accuracy.

Abstract

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 interactions are modeled through attention-based message passing. The TGAT predictor captures spatiotemporal risk dynamics, while model-internal evidence -- including feature z-scores and attention-derived neighbor influence -- is transformed into structured prompts that constrain LLM reasoning to verifiable model outputs. To evaluate explanatory reliability, we introduce a directional-consistency validation protocol that quantitatively measures agreement between generated risk narratives and underlying statistical evidence. Experiments on six months of real-world logistics data demonstrate that the proposed framework outperforms baseline models, achieving a test AUC of 0.761, AP of 0.344, and recall of 0.504 under a strict chronological split while producing early warning explanations with 99.6\% directional consistency. Results show that grounding LLM generation in graph-model evidence enables interpretable and auditable risk reporting without sacrificing predictive performance. The framework provides a practical pathway toward operationally deployable explainable AI for supply chain risk early warning and resilience management.