Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models

TL;DR

This paper introduces a novel framework using Knowledge Graphs and Large Language Models to improve supply chain visibility by automating information extraction from public sources, enabling better risk management and strategic planning.

Contribution

It presents a zero-shot, LLM-driven approach to construct supply chain knowledge graphs without extensive domain-specific training, enhancing visibility across multiple tiers.

Findings

Significant improvements in supply chain mapping accuracy.

Extended visibility beyond tier-2 suppliers.

Effective identification of dependencies and sourcing options.

Abstract

In today's globalized economy, comprehensive supply chain visibility is crucial for effective risk management. Achieving visibility remains a significant challenge due to limited information sharing among supply chain partners. This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility without relying on direct stakeholder information sharing. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources and constructs KGs to capture complex interdependencies between supply chain entities. We employ zero-shot prompting for Named Entity Recognition (NER) and Relation Extraction (RE) tasks, eliminating the need for extensive domain-specific training. We validate the framework with a case study on electric vehicle supply chains, focusing on tracking critical minerals for battery manufacturing. Results show significant improvements in supply chain mapping, extending visibility beyond tier-2 suppliers. The framework reveals critical dependencies and alternative sourcing options, enhancing risk management and strategic planning. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks. This research offers a scalable, flexible method for constructing domain-specific supply chain KGs, addressing longstanding challenges in visibility and paving the way for advancements in digital supply chain surveillance.