Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs

TL;DR

This paper introduces a novel system combining web crawling and large language models to improve supply chain transparency in emerging economies, validated through a semiconductor industry case study.

Contribution

It presents a new approach using LLMs and web data to address transparency challenges in emerging economies' supply chains, filling data gaps.

Findings

The system effectively collects and analyzes supply chain data from online sources.

It enhances transparency in emerging economies like China, complementing existing datasets.

Challenges such as data accuracy and bias mitigation remain to be addressed.

Abstract

In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the advancements in supply chain relationship datasets like Bloomberg and FactSet, supply chain transparency remains a significant challenge in emerging economies due to issues such as information asymmetry and institutional gaps in regulation. This study proposes a novel approach to enhance supply chain transparency in emerging economies by leveraging online content and large language models (LLMs). We develop a Supply Chain Knowledge Graph Mining System that integrates advanced LLMs with web crawler technology to automatically collect and analyze supply chain information. The system's effectiveness is validated through a case study focusing on the semiconductor supply chain, a domain that has recently gained significant attention due to supply chain risks. Our results demonstrate that the proposed system provides greater applicability for emerging economies, such as mainland China, complementing the data gaps in existing datasets. However, challenges including the accurate estimation of monetary and material flows, the handling of time series data, synonyms disambiguation, and mitigating biases from online contents still remains. Future research should focus on addressing these issues to further enhance the system's capabilities and broaden its application to other emerging economies and industries.