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Autoren: 
Haße, Hendrik
van der Valk, Hendrik
Weißenberg, Norbert
Otto, Boris
Datum: 
2020
Quellenangabe: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] 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 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 763-795
Zusammenfassung: 
Purpose: Digital Twins attract much attention in science and practice, because of their capability to integrate operational data from a wide variety of sources. Thus, providing a complete overview of an asset throughout its entire life cycle. This article develops and demonstrates a Digital Twin, which enables a sovereign and multilateral sharing of sensitive IoT data based on proven standards. Methodology: The design described in this paper is developed following the design science research methodology. Current challenges and solution objectives are derived from literature and the solution approach is implemented and demonstrated in a central artefact. The findings are evaluated and iterated back into the design of the central artefact. Findings: For multilateral data exchange of sensitive operational data, standards are needed that allow for interoperability of several stakeholders and for providing a secure and sovereign data exchange. Therefore, the designs of the Plattform Industrie 4.0 Asset Administration Shell and the International Data Spaces are merged in this contribution. In this way, Digital Twins can be used in cross-company network struc-tures. Originality: Multilateral data sharing is still associated with considerable security risks for the companies providing the data. Therefore, the consideration of data sovereignty aspects for Digital Twins is very limited. Furthermore, Digital Twins are seldom addressed in the context of cross-company data sharing.
Schlagwörter: 
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
https://creativecommons.org/licenses/by-sa/4.0/
Dokumentart: 
Conference Paper

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