End-to-End Digital Twin Approach for Near-Real-Time Decision Support Services

Lukas Schweiger (), Jürg Meierhofer, Cosimo Barbieri and Mario Rapaccini
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Lukas Schweiger: Institute of Data Analysis and Process Design, Zuerich University of Applied Sciences
Jürg Meierhofer: Institute of Data Analysis and Process Design, Zuerich University of Applied Sciences
Cosimo Barbieri: Department of Industrial Engineering, Università degli Studi di Firenze
Mario Rapaccini: Department of Industrial Engineering, Università degli Studi di Firenze

A chapter in Smart Services Summit, 2022, pp 67-75 from Springer

Abstract: Abstract An end-to-end approach for near-real-time decision support services constructed of different elements from the fields of digital twins, decision support systems, data analytics, symbiotic simulations, and product-service systems is proposed based on a literature review. Parts of the concept have been validated based on two practical cases in an earlier research project. The model presented combines elements of those existing approaches from the literature into a single end-to-end model. The resulting end-to-end model will be tested in an industrial context to support service decision-makers.

Keywords: Product service system; Digital twin; Symbiotic simulation; Near-real-time decision making (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-97042-0_7

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