Digital transformation in the manufacturing industry : business models and smart service systems

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dc.identifier.uri http://dx.doi.org/10.15488/9383
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/9437
dc.contributor.author Dreyer, Sonja ger
dc.date.accessioned 2020-02-20T12:37:55Z
dc.date.available 2020-02-20T12:37:55Z
dc.date.issued 2020
dc.identifier.citation Dreyer, Sonja: Digital transformation in the manufacturing industry : business models and smart service systems. Hannover : Gottfried Wilhelm Leibniz Universität, Diss., 2020, XXIII, 110 S., 83 S. Anh. DOI: https://doi.org/10.15488/9383 ger
dc.description.abstract The digital transformation enables innovative business models and smart services, i.e. individual services that are based on data analyses in real-time as well as information and communications technology. Smart services are not only a theoretical construct but are also highly relevant in practice. Nine research questions are answered, all related to aspects of smart services and corresponding business models. The dissertation proceeds from a general overview, over the topic of installed base management as precondition for many smart services in the manufacturing industry, towards exemplary applications in form of predictive maintenance activities. A comprehensive overview is provided about smart service research and research gaps are presented that are not yet closed. It is shown how a business model can be developed in practice. A closer look is taken on installed base management. Installed base data combined with condition monitoring data leads to digital twins, i.e. dynamic models of machines including all components, their current conditions, applications and interaction with the environment. Design principles for an information architecture for installed base management and its application within a use case in the manufacturing industry indicate how digital twins can be structured. In this context, predictive maintenance services are taken for the purpose of concretization. It is looked at state oriented maintenance planning and optimized spare parts inventory as exemplary approaches for smart services that contribute to high machine availability. Taxonomy of predictive maintenance business models shows their diversity. It is viewed on the named topics both from theoretical and practical viewpoints, focusing on the manufacturing industry. Established research methods are used to ensure academic rigor. Practical problems are considered to guarantee practical relevance. A research project as background and the resulting collaboration with different experts from several companies also contribute to that. The dissertation provides a comprehensive overview of smart service topics and innovative business models for the manufacturing industry, enabled by the digital transformation. It contributes to a better understanding of smart services in theory and practice and emphasizes the importance of innovative business models in the manufacturing industry. ger
dc.language.iso eng ger
dc.publisher Hannover : Institutionelles Repositorium der Leibniz Universität Hannover
dc.rights CC BY 3.0 DE ger
dc.rights.uri http://creativecommons.org/licenses/by/3.0/de/ ger
dc.subject Business model eng
dc.subject Value network eng
dc.subject Value co-creation eng
dc.subject Availability orientation eng
dc.subject Installed base management eng
dc.subject Digital twin eng
dc.subject Predictive maintenance eng
dc.subject Smart Service ger
dc.subject Geschäftsmodell ger
dc.subject Wertschöpfungsnetzwerk ger
dc.subject Wertschöpfung ger
dc.subject Verfügbarkeitsorientierung ger
dc.subject Digitaler Zwilling ger
dc.subject Vorausschauende Wartung ger
dc.subject.ddc 650 | Management ger
dc.title Digital transformation in the manufacturing industry : business models and smart service systems eng
dc.type DoctoralThesis ger
dc.type Text ger
dcterms.extent XXIII, 110 S., 83 S.
dc.description.version publishedVersion ger
tib.accessRights frei zug�nglich ger


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