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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Digital twin-enabled decision support services in industrial ecosystems
Authors: Meierhofer, Jürg
Schweiger, Lukas
Lu, Jinzhi
Züst, Simon
West, Shaun
Stoll, Oliver
Kiritsis, Dimitris
et. al: No
DOI: 10.3390/app112311418
Published in: Applied Sciences
Volume(Issue): 11
Issue: 23
Page(s): 11418
Issue Date: 2021
Publisher / Ed. Institution: MDPI
ISSN: 2076-3417
Language: English
Subjects: Digital twin; Smart service; Data modeling; Decision support; Simulation; Semantic modeling
Subject (DDC): 658.5: Production management
Abstract: The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project: Digital-Twin-basierte Dienstleistungen zur Unterstützung der Entscheidungsfindung entlang des Produktlebenszyklus von Investitionsgütern
Appears in collections:Publikationen School of Engineering

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Meierhofer, J., Schweiger, L., Lu, J., Züst, S., West, S., Stoll, O., & Kiritsis, D. (2021). Digital twin-enabled decision support services in industrial ecosystems. Applied Sciences, 11(23), 11418.
Meierhofer, J. et al. (2021) ‘Digital twin-enabled decision support services in industrial ecosystems’, Applied Sciences, 11(23), p. 11418. Available at:
J. Meierhofer et al., “Digital twin-enabled decision support services in industrial ecosystems,” Applied Sciences, vol. 11, no. 23, p. 11418, 2021, doi: 10.3390/app112311418.
MEIERHOFER, Jürg, Lukas SCHWEIGER, Jinzhi LU, Simon ZÜST, Shaun WEST, Oliver STOLL und Dimitris KIRITSIS, 2021. Digital twin-enabled decision support services in industrial ecosystems. Applied Sciences. 2021. Bd. 11, Nr. 23, S. 11418. DOI 10.3390/app112311418
Meierhofer, Jürg, Lukas Schweiger, Jinzhi Lu, Simon Züst, Shaun West, Oliver Stoll, and Dimitris Kiritsis. 2021. “Digital Twin-Enabled Decision Support Services in Industrial Ecosystems.” Applied Sciences 11 (23): 11418.
Meierhofer, Jürg, et al. “Digital Twin-Enabled Decision Support Services in Industrial Ecosystems.” Applied Sciences, vol. 11, no. 23, 2021, p. 11418,

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