Integrating Graph Retrieval-Augmented Generation With Large Language Models for Supplier Discovery

As supply chain complexity and dynamism challenge traditional management approaches, integrating large language models (LLMs) and knowledge graphs (KGs) emerges as a promising method for advancing supply chain analytics. This article presents a methodology crafted to harness the synergies between LLMs and KGs, with a particular focus on enhancing supplier discovery practices. The primary goal is to transform and integrate a vast body of unstructured supplier capability data into a harmonized KG, thus improving the supplier discovery process and enhancing the accessibility and findability of manufacturing suppliers. Through an ontology-driven graph construction process, the presented methodology integrates KGs and retrieval-augmented generation with advanced LLM-based natural language processing techniques. With the aid of a detailed case study, we showcase how this integrated approach not only enhances the quality of answers and increases visibility for small- and medium-sized manufacturers but also amplifies agility and provides strategic insights into supply chain management.