A Knowledge Graph Perspective on Supply Chain Resilience
Yushan Liu Bailan He Marcel Hildebrandt Maximilian Buchner Daniela Inzko Roger Wernert Emanuel Weigel Dagmar Beyer Martin Berbalk Volker Tresp
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.