SHIELD : LLM -Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024)

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.

## introduction the expected widespread adoption of electric vehicles (evs) is threatened by risks associated with the geographic and economic concentration of critical battery minerals, such as lithium, cobalt, and nickel. to enhance the resilience of the ev battery supply chain, manufacturers must anticipate disruptions caused by natural disasters and geopolitical tensions. proactive strategies and supply diversification are essential to mitigate these risks 1 . completed by y. dong and y. hu during remote visits, and a. shi and w. liu during cmu internships. z. cheng, y. dong, a. shi, w. liu, and y. hu contributed equally. j. o'connor, a. hauptmann, and k. whitefoot provided guidance. see appx. l for details. correspondence: zhiqic,alex@cs.cmu.edu, kwhitefoot@andrew.cmu.edu. figure 2 : overview of the supply chain schema construction process, illustrating the collection of diverse sources, schema extraction using large language models, and the integration into a unified schema library. to address these challenges, we introduce shield (schema-based hierarchical induction for ev supply chain disruption), a two-stage framework that integrates llms and domain expertise for predictive analytics in ev battery supply chains (fig. 1 ): evaluated on 12,070 paragraphs from 365 sources (2022-2023) (sec. 5), shield outperforms baseline gcns and llm+prompt methods (e.g., gpt-4o) in disruption prediction. by integrating llm capabilities with domain expertise, this framework enhances supply chain risk assessment. key contri-butions include: (1) an llm-expert integration methodology for accurate, interpretable predictions; ## dynamic analysis of supply chain disruptions supply chain news dataset. we developed an ev supply chain news dataset (january 2022 -december 2023) to evaluate our system's real-world performance (appx. b.2). the dataset comprises 247 articles from major news outlets and 118 enterprise reports from ev battery-related companies (fig. 7 and fig. 8 ). after preprocessing-including text extraction, language standardization, and noise reduction-we obtained meta data with 3,022 paragraphs. we then fused international news with contemporaneous corporate stories in the meta data, creating 354 diverse documents comprising 12,070 paragraphs. the final dataset contains approximately 660k words ( ## experiments our evaluation comprises two parts: (1) schema learning assessment and (2) supply chain disruption prediction. we assess learned schemas against expert knowledge and evaluate our schema induction process's effectiveness in predicting supply chain events. detailed experimental setup and evaluation metrics are in appx. i. ## conclusion we present shield, a two-stage framework that integrates large language models (llms) with domain expertise, yielding promising results in ev battery supply chain analytics and risk assessment. while demonstrating particular strength in early disruption detection and event prediction for critical battery materials, significant challenges remain in schema integration, real-time adaptability, and error reduction. future research will systematically address these limitations, enhance the system's robustness, and explore broader applications across diverse industries and supply chain ecosystems. a extended related work supply chain risk management. ai techniques have been increasingly applied to predict and mitigate supply chain risks (ganesh and kalpana, 2022). while agent-based approaches @xcite @xcite enable inter-agent communication for forecasting, they often lack robust predictive capabilities and have limited parameter sets. rule-based reasoning methods @xcite @xcite @xcite offer decisionmaking frameworks but provide minimal quantitative insights. to address these limitations, machine learning (dl) and deep learning (dl) techniques have been employed @xcite @xcite @xcite , enhancing demand forecasting and disruption prediction @xcite . recent studies have begun exploring the potential of large language models (llms) in supply chain management @xcite . however, most current works prioritize predictive performance over interpretability, hindering practitioners' ability to make informed decisions. our approach addresses this gap by integrating llms for schema induction, extracting hierarchical knowledge-graph structures from academic resources to predict supply chain disruptions, thereby enhancing both predictive performance and interpretability.