Improving risk assessment for interdependent urban critical infrastructures - Sandra König (Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29, 2020)
Purpose: Urban critical infrastructures are highly interdependent not only due to their vicinity but also due to the increasing digitalization. In case of a security incident, both the dynamics inside each infrastructure and interdependencies between them need to be considered to estimate the overall impact on a city. Methodology: An existing high-level model of dependencies between critical infrastructures is extended by incorporating more details on the individual infrastructure's behavior. To this end, a literature review on existing models for specific sectors is conducted with a special focus on machine learning models such as neural net-works. Findings: Existing models for the dynamics of specific urban infrastructures are reviewed and integration in an existing dependency model is discussed. A special focus lies on simulation models since the extended model should be used to evaluate consequences of a security incident in a city. Originality: Existing risk assessment approaches typically focus on one type of critical infrastructures rather than on an entire network of interdependent infrastructures. However due to the increasing number of interdependencies, a more holistic view is necessary while the dynamics inside each infrastructure should also be considered.
urn:nbn:de:gbv:830-882.0115084; hdl:10419/228914; https://econpapers.repec.org/RePEc:zbw:hiclpr:29
Keywords: 650; Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science