König, SandraSandraKönig2020-12-012020-12-012020-09-23Hamburg International Conference of Logistics (HICL) 29: 279-291 (2020)http://hdl.handle.net/11420/8013Purpose: Urban critical infrastructures are highly interdependent not only due to their vicinity but also due to the increasing digitalization. In case of a security inci-dent, 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 infra-structures is extended by incorporating more details on the individual infrastruc-ture'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 re-viewed 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 con-sequences of a security incident in a city. Originality: Existing risk assessment approaches typically focus on one type of criti-cal infrastructures rather than on an entire network of interdependent infrastruc-tures. However due to the increasing number of interdependencies, a more holistic view is necessary while the dynamics inside each infrastructure should also be con-sidered.enhttps://creativecommons.org/licenses/by-sa/4.0/logisticsindustry 4.0digitalizationinnovationsupply chain managementartificial intelligencedata scienceWirtschaftHandel, Kommunikation, VerkehrConference Paper10.15480/882.3123https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/10604710.15480/882.3123Kersten, WolfgangWolfgangKerstenBlecker, ThorstenThorstenBleckerRingle, Christian M.Christian M.RingleConference Paper