A Knowledge Graph Perspective on Supply Chain Resilience

TL;DR

This paper models supply chains as knowledge graphs to improve transparency, predict missing data, and identify risks, thereby enhancing supply chain resilience amid global crises and regulatory changes.

Contribution

It introduces a novel approach of using knowledge graph modeling and completion techniques for supply chain transparency and risk management.

Findings

Achieved a mean reciprocal rank of 0.4377 in predicting missing supply chain information.

Identified critical entities in supply networks using graph analysis algorithms.

Enhanced supply chain risk detection and transparency through knowledge graph methods.

Abstract

Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is often intransparent and incomplete, making it difficult to obtain precise predictions about prospective risks. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers. To predict missing information in the graph, we apply state-of-the-art knowledge graph completion methods and attain a mean reciprocal rank of 0.4377 with the best model. Further, we apply graph analysis algorithms to identify critical entities in the supply network, supporting supply chain managers in automated risk identification.