Extracting Supply Chain Information From News Articles Using Large Language Models: A Fully Automatic Approach
Jaewon Kim, Eunbi Kim, Dongsoo Kim, Yoojoong Kim, Taesu Cheong · IEEE Access · 2025
Supply chain mapping is crucial for global companies to identify and mitigate potential risks. Although natural language processing techniques are analyzed to extract supply chain maps from textual sources to automate the process, they require extensive manual annotation of training data, limiting the scalability and efficiency of these approaches. This study explores novel methods using large language models to fully automate supply chain mapping, focusing on synthetic data generation and improving relation classification techniques. This study investigates the use of large language models for relation classification in supply chain contexts and explores their potential for generating synthetic datasets. The performance of these synthetic datasets is compared with those of manually annotated datasets. The experimental results demonstrate that synthetic data outperform manual data if manual data are insufficient. To demonstrate its practical applicability, this study applies the developed method to generate a supply chain map from mining-related news articles. The subsequent supply chain map visualization for mining-related industries indicates the effectiveness of the method in automated, real-time mapping using publicly available information, enabling the rapid identification of supply network changes. This study contributes to the supply chain management and natural language processing fields by advancing sophisticated, AI-driven supply chain analysis tools.