An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning
Ge Zheng, Alexandra Brintrup
TL;DR
The paper tackles the challenge of achieving end-to-end supply chain visibility across borders under privacy and regulatory constraints. It proposes an analytics-driven framework that combines Federated Learning with Graph Convolutional Networks to perform link prediction on supply chain knowledge graphs, using GraphSAGE for inductive node representations. A comprehensive case study on the Marklines automotive dataset across ten countries demonstrates that AdapFL-based federated approaches outperform baselines, particularly for rare relationship types such as has_cert and in smaller graphs. The findings show the practical potential of privacy-preserving cross-border visibility to support proactive risk management and the development of resilient, adaptive supply chain strategies.
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.