SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions

1Carnegie Mellon University, 2Columbia University, 3Georgia Institute of Technology
4University of Michigan, Ann Arbor, 5Boston University

*Indicates Equal Contribution
Paper Code arXiv

Abstract

The electric vehicle (EV) battery supply chain's vulnerability to disruptions necessitates advanced predictive analytics. We present SHIELD (Schema-based Hierarchical Induction for EV supply chain Disruption), a system integrating Large Language Models (LLMs) with domain expertise for EV battery supply chain risk assessment. SHIELD combines: (1) LLM-driven schema learning to construct a comprehensive knowledge library, (2) a disruption analysis system utilizing fine-tuned language models for event extraction, multi-dimensional similarity matching for schema matching, and Graph Convolutional Networks (GCNs) with logical constraints for prediction, and (3) an interactive interface for visualizing results and incorporating expert feedback to enhance decision-making. Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods (e.g. GPT-4o) in disruption prediction. These results demonstrate SHIELD's effectiveness in combining LLM capabilities with domain expertise for enhanced supply chain risk assessment.

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BibTeX

@misc{cheng2024shieldllmdrivenschemainduction,
      title={SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions}, 
      author={Zhi-Qi Cheng and Yifei Dong and Aike Shi and Wei Liu and Yuzhi Hu and Jason O'Connor and Alexander Hauptmann and Kate Whitefoot},
      year={2024},
      eprint={2408.05357},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2408.05357}, 
}