A Knowledge Graph Perspective on Supply Chain Resilience
This work addresses supply chain resilience for companies by enabling automated risk identification, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of opaque and incomplete supply chain information by modeling supply networks as knowledge graphs, achieving transparency up to tier-3 suppliers and predicting missing data with a mean reciprocal rank of 0.4377.
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.