Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models

• URL: http://arxiv.org/abs/2408.07705v1
• Date: Mon, 5 Aug 2024 17:11:29 GMT
• Title: Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models
• Authors: Sara AlMahri, Liming Xu, Alexandra Brintrup,
• Abstract summary: This paper presents a novel framework leveraging Knowledge Graphs (KGs) and Large Language Models (LLMs) to enhance supply chain visibility. Our zero-shot, LLM-driven approach automates the extraction of supply chain information from diverse public sources. With high accuracy in NER and RE tasks, it provides an effective tool for understanding complex, multi-tiered supply networks.
• Score: 49.898152180805454
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models [5.205252810216621]
  We develop a supply chain graph for the civil engineering sector using large language models (LLMs) We fine-tune an LLM to classify entities within the supply chain graph, providing detailed insights into their roles and relationships. Our contributions include the development of a supply chain graph for the civil engineering sector, as well as a fine-tuned LLM model that enhances entity classification and understanding of supply chain networks.
  arXiv  Detail & Related papers  (2024-10-16T21:24:13Z)
• SPOQchain: Platform for Secure, Scalable, and Privacy-Preserving Supply Chain Tracing and Counterfeit Protection [46.68279506084277]
  This work proposes SPOQchain, a novel blockchain-based platform that provides comprehensive traceability and originality verification. It provides an analysis of privacy and security aspects, demonstrating the need and qualification of SPOQchain for the future of supply chain tracing.
  arXiv  Detail & Related papers  (2024-08-30T07:15:43Z)
• Large Language Model Supply Chain: A Research Agenda [5.1875389249043415]
  Large Language Models (LLMs) have revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastructure, foundation models, and downstream applications. This paper offers a structured approach to identify critical challenges and opportunities through the dual lenses of Software Engineering (SE) and Security & Privacy (S&P)
  arXiv  Detail & Related papers  (2024-04-19T09:29:53Z)
• HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment [3.439495194421287]
  We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module. Our model outperforms in experiments on a real-world supply chain dataset.
  arXiv  Detail & Related papers  (2023-11-07T00:54:04Z)
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach [47.00450933765504]
  We propose the use of explainable artificial intelligence for decentralised computing of estimated contributions to a metric of interest. This approach mitigates the need to convince supply chain actors to share data, as all computations occur in a decentralised manner. Results demonstrate the effectiveness of our approach in detecting the source of quality variations compared to a centralised approach.
  arXiv  Detail & Related papers  (2023-07-22T20:03:16Z)
• A Knowledge Graph Perspective on Supply Chain Resilience [15.028130016717773]
  Global crises and regulatory developments require increased supply chain transparency and resilience. Information about supply chains, especially at the deeper levels, is often intransparent and incomplete. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers.
  arXiv  Detail & Related papers  (2023-05-15T10:14:30Z)
• Creating Training Sets via Weak Indirect Supervision [66.77795318313372]
  Weak Supervision (WS) frameworks synthesize training labels from multiple potentially noisy supervision sources. We formulate Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels. We develop a probabilistic modeling approach, PLRM, which uses user-provided label relations to model and leverage indirect supervision sources.
  arXiv  Detail & Related papers  (2021-10-07T14:09:35Z)
• Will bots take over the supply chain? Revisiting Agent-based supply chain automation [71.77396882936951]
  Agent-based supply chains have been proposed since early 2000; industrial uptake has been lagging. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains are filling in gaps. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation.
  arXiv  Detail & Related papers  (2021-09-03T18:44:26Z)
• Data Considerations in Graph Representation Learning for Supply Chain Networks [64.72135325074963]
  We present a graph representation learning approach to uncover hidden dependency links. We demonstrate that our representation facilitates state-of-the-art performance on link prediction of a global automotive supply chain network.
  arXiv  Detail & Related papers  (2021-07-22T12:28:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.