利用知识图谱与大语言模型提升供应链可见性
Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models
AI总结：
针对供应链伙伴信息共享有限导致的可见性不足问题，提出结合知识图谱与大语言模型的零样本框架，从公开来源提取信息构建图谱，经电动车供应链案例验证可拓展可见性、助力风险管理。
摘要：
在当今全球化经济中，全面的供应链可见性对有效风险管理至关重要。由于供应链合作伙伴之间的信息共享有限，实现可见性仍是一项重大挑战。本文提出了一种新颖的框架，利用知识图谱（Knowledge Graphs, KGs）和大语言模型（Large Language Models, LLMs）在不依赖利益相关方直接信息共享的情况下提升供应链可见性。我们的零样本、LLM驱动的方法可从各类公开来源自动提取供应链信息，并构建KGs以捕捉供应链实体间的复杂相互依赖关系。我们将零样本提示用于命名实体识别（Named Entity Recognition, NER）和关系抽取（Relation Extraction, RE）任务，无需进行大量特定领域的训练。我们通过一个电动汽车供应链的案例研究验证了该框架，研究重点是追踪电池制造所需的关键矿产。结果显示，该框架在供应链映射方面取得了显著改进，将可见性范围拓展至二级供应商之外。该框架揭示了关键依赖关系和替代采购选项，强化了风险管理与战略规划。凭借在NER和RE任务上的高准确率，它为理解复杂的多层级供应网络提供了有效工具。本研究提出了一种可扩展、灵活的特定领域供应链KG构建方法，解决了长期存在的可见性挑战，为数字供应链监控的发展铺平了道路。
英文摘要：
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.
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。
