An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning

• URL: http://arxiv.org/abs/2503.07231v1
• Date: Mon, 10 Mar 2025 12:15:45 GMT
• Title: An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning
• Authors: Ge Zheng, Alexandra Brintrup,
• Abstract summary: We propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility.<n>FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange.<n>GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections.
• Score: 52.79646338275159
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy.

Related papers

• Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs [0.40964539027092917]
  This study proposes a novel approach to enhance supply chain transparency in emerging economies by leveraging online content and large language models (LLMs)<n>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.
  arXiv  Detail & Related papers  (2024-12-22T08:46:16Z)
• Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs [52.79646338275159]
  Relationship prediction aims to increase the visibility of supply chains using data-driven techniques.<n>Existing methods have been successful for predicting relationships but struggle to extract the context in which these relationships are embedded.<n>Lack of context prevents practitioners from distinguishing transactional relations from established supply chain relations.
  arXiv  Detail & Related papers  (2024-12-04T15:19:01Z)
• Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models [49.898152180805454]
  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.
  arXiv  Detail & Related papers  (2024-08-05T17:11:29Z)
• 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)
• 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)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)
• 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)
• Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations [51.8796674904734]
  We propose a new cooperative learning framework with pre-trained heterogeneous observation representations. We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
  arXiv  Detail & Related papers  (2020-12-24T04:52:29Z)

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.