A Dual-Track LLM Framework for Traceable Supplier Delivery Risk Analysis in Electric Vehicle Supply Chains

Abstract

Supplier delivery instability can disrupt production continuity and increase inventory and coordination pressure. This study proposes a dual-track framework for supplier delivery reliability risk analysis under private, temporally sparse, small-sample enterprise data. A replaceable structured track produces on-time delivery estimates and risk grades for the next observed period. A locally deployed, LoRA-adapted large language model uses retrieved evidence to generate risk explanations and management recommendations. Numeric anchoring, multi-level verification, and provenance auditing connect the two tracks, and the language model does not alter the structured results. Under temporally frozen evaluation, persistence achieves the lowest MAE (0.252), and no learned predictor outperforms this baseline. Factorial experiments show that domain adaptation improves explanation coverage, numeric anchoring brings conditional anchor fidelity to 96.2%, and retrieval reduces the training-value restatement rate from 24.4% to 7.3%. However, 19.5% of outputs in the default configuration still degenerate. A blind evaluation by three domain experts indicates that the overall usefulness of the current explanations remains limited and that they require human review. By linking quantitative estimates with traceable explanations, the framework supports delivery risk analysis under human supervision. Its potential contribution to supply-chain resilience and resource efficiency requires further validation through prospective operational studies.