| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | The digital twin as a service enabler : from the service ecosystem to the simulation model |
| Authors: | Meierhofer, Jürg West, Shaun Rapaccini, Mario Barbieri, Cosimo |
| et. al: | No |
| DOI: | 10.1007/978-3-030-38724-2_25 |
| Proceedings: | IESS 2020 : Exploring Service Science |
| Editors of the parent work: | Nóvoa, Henriqueta Drăgoicea, Monica Kühl, Niklas |
| Page(s): | 347 |
| Pages to: | 359 |
| Conference details: | 10th International Conference on Exploring Service Science, Porto, Portugal, 5–7 February 2020 |
| Issue Date: | Feb-2020 |
| Series: | Lecture Notes in Business Information Processing |
| Series volume: | 377 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-030-38723-5 978-3-030-38724-2 |
| ISSN: | 1865-1348 1865-1356 |
| Language: | English |
| Subjects: | Smart services; Servitization of manufacturing; Digital twin |
| Subject (DDC): | 670: Manufacturing |
| Abstract: | This paper investigates the concept of the digital twin as an enabler for smart services in the context of the servitization of manufacturing. In particular, a concept is developed and proposed for the derivation of appropriate simulation models starting from the model of the service ecosystem. To do so, smart industrial services are analyzed from the point of view of their value proposition. Next, the role of the digital twin as an enabler for these services is analyzed and structured in a multi-layer architecture. Hybrid simulation approaches are identified as suitable for building simulation models for this architecture. Finally, a procedural end-to-end approach for developing a simulation based digital twin departing from the service ecosystem is proposed. |
| URI: | https://hdl.handle.net/2158/1223056 https://digitalcollection.zhaw.ch/handle/11475/19469 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
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Meierhofer, J., West, S., Rapaccini, M., & Barbieri, C. (2020). The digital twin as a service enabler : from the service ecosystem to the simulation model [Conference paper]. In H. Nóvoa, M. Drăgoicea, & N. Kühl (Eds.), IESS 2020 : Exploring Service Science (pp. 347–359). Springer. https://doi.org/10.1007/978-3-030-38724-2_25
Meierhofer, J. et al. (2020) ‘The digital twin as a service enabler : from the service ecosystem to the simulation model’, in H. Nóvoa, M. Drăgoicea, and N. Kühl (eds) IESS 2020 : Exploring Service Science. Cham: Springer, pp. 347–359. Available at: https://doi.org/10.1007/978-3-030-38724-2_25.
J. Meierhofer, S. West, M. Rapaccini, and C. Barbieri, “The digital twin as a service enabler : from the service ecosystem to the simulation model,” in IESS 2020 : Exploring Service Science, Feb. 2020, pp. 347–359. doi: 10.1007/978-3-030-38724-2_25.
MEIERHOFER, Jürg, Shaun WEST, Mario RAPACCINI und Cosimo BARBIERI, 2020. The digital twin as a service enabler : from the service ecosystem to the simulation model. In: Henriqueta NÓVOA, Monica DRĂGOICEA und Niklas KÜHL (Hrsg.), IESS 2020 : Exploring Service Science [online]. Conference paper. Cham: Springer. Februar 2020. S. 347–359. ISBN 978-3-030-38723-5. Verfügbar unter: https://hdl.handle.net/2158/1223056
Meierhofer, Jürg, Shaun West, Mario Rapaccini, and Cosimo Barbieri. 2020. “The Digital Twin as a Service Enabler : From the Service Ecosystem to the Simulation Model.” Conference paper. In IESS 2020 : Exploring Service Science, edited by Henriqueta Nóvoa, Monica Drăgoicea, and Niklas Kühl, 347–59. Cham: Springer. https://doi.org/10.1007/978-3-030-38724-2_25.
Meierhofer, Jürg, et al. “The Digital Twin as a Service Enabler : From the Service Ecosystem to the Simulation Model.” IESS 2020 : Exploring Service Science, edited by Henriqueta Nóvoa et al., Springer, 2020, pp. 347–59, https://doi.org/10.1007/978-3-030-38724-2_25.
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