Abstract
Supply Chain Risk Management focuses on the identification, assessment and management of disruptive events that can affect companies, transport routes and resources involved in critical goods supply chains. Modern supply chains consist of interconnected components that can be complex and dynamic in nature. In this demo, we present our system for analysing the resilience of supply chains for crisis relevant products. A dependency Bayesian Network is automatically generated from relevant information about the supply chain maintained in a Knowledge Graph. The main objective of the proposed approach is the early identification of bottlenecks and timely prediction of the consequences of probable disruptions of the network.
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Acknowledgements
This work was funded by the German Federal Ministry for Economic Affairs and Climate Protection (BMWK) as part of the AI Innovation Competition under contract 01MK21006A.
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Karam, N., Matini, S., Laas, R., Hoppe, T. (2023). A Hybrid Knowledge Graph and Bayesian Network Approach for Analyzing Supply Chain Resilience. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_5
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DOI: https://doi.org/10.1007/978-3-031-43458-7_5
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