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Designing solutions with the product-service systems digital twin: What is now and what is next?
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (Product Development Research Lab)ORCID iD: 0000-0002-5076-3300
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (Product Development Research Lab)ORCID iD: 0000-0001-5114-4811
2022 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 138, article id 103629Article in journal (Refereed) Published
Abstract [en]

Digital Twins (DT) are of particular interest in the domain of Product-Service Systems (PSS), to predict hardware availability, to inform about the needed features of new solutions, and to forecast the expected performances of new configurations in operation. The aim of this paper is to shed light on the extent to which ‘twins’ are applied today across the PSS life cycle, and to spotlight the ability of DT-related case studies to capture a full value perspective vs. simply attempting to represent hardware and services in the digital realm. By means of a systematic literature review combined with a mapping study, the paper reveals how only a minimal part of the existing literature is able to demonstrate how real-time physical-to-virtual and virtual-to-physical connections can be used to improve the design of servitized solutions. The analysis shows how contributions in the topic are mostly proposing frameworks and methods, as opposed to models and tools, as well as how ‘evaluation’, ‘validation’ tasks are largely neglected. As a result, the paper proposes a specialized definition of the PSS DT, together with a set of research questions that need to be answered to empower the engineering teams with relevant DT for PSS design.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 138, article id 103629
Keywords [en]
Product-service systems, Digital twin, Systematic review, Systematic mapping
National Category
Other Mechanical Engineering
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-22605DOI: 10.1016/j.compind.2022.103629ISI: 000754066000004OAI: oai:DiVA.org:bth-22605DiVA, id: diva2:1636170
Part of project
Model Driven Development and Decision Support – MD3S, Knowledge Foundation
Funder
Knowledge Foundation, 20180159
Note

open access

Available from: 2022-02-09 Created: 2022-02-09 Last updated: 2022-03-07Bibliographically approved

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Bertoni, MarcoBertoni, Alessandro

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