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

A Knowledge Graph Perspective on Supply Chain Resilience