利用在线内容与大语言模型提升新兴经济体的供应链透明度
Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs
AI总结：
本研究提出结合大语言模型与网络爬虫技术的供应链知识图谱挖掘系统，自动收集分析在线信息，有效提升新兴经济体供应链透明度并弥补现有数据集空白。
摘要：
在当前的全球经济中，供应链透明度通过使公司能够监控供应商绩效并促进问责制和责任感，在确保这种安全方面发挥着关键作用。尽管像 Bloomberg 和 FactSet 这样的供应链关系数据集取得了进展，但由于信息不对称和监管制度差距等问题，供应链透明度在新兴经济体中仍然是一个重大挑战。本研究提出了一种新方法，通过利用在线内容和大语言模型（LLMs）来提升新兴经济体的供应链透明度。我们开发了一个供应链知识图谱挖掘系统，该系统将先进的 LLMs 与网络爬虫技术相结合，以自动收集和分析供应链信息。该系统的有效性通过一项以半导体供应链为焦点的案例研究得到了验证，该领域近期因供应链风险而备受关注。我们的结果表明，所提出的系统对大陆等新兴经济体具有更强的适用性，弥补了现有数据集中的数据空白。然而，仍存在一些挑战，包括对资金流和物质流的准确估计、时间序列数据的处理、同义词消歧以及减轻在线内容带来的偏差。未来的研究应集中于解决这些问题，以进一步增强系统的能力，并将其应用扩展到其他新兴经济体和行业。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
