Using Digital Twins for Managing Change in Complex Projects

Conference Paper (2025)
Author(s)

Jennifer Whyte (Imperial College London, University of Sydney)

Ranjith K. Soman (Imperial College London, TU Delft - Integral Design & Management)

Rafael Sacks (Technion)

Neda Mohammadi (University of Sydney, Georgia Institute of Technology)

Nader Naderpajouh (University of Sydney)

Wei Ting Hong (University of Sydney)

Ghang Lee (Yonsei University)

DOI related publication
https://doi.org/10.1007/978-981-96-4051-5_150 Final published version
More Info
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Publication Year
2025
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Pages (from-to)
1575-1582
Publisher
Springer
ISBN (print)
9789819640508
Event
Downloads counter
196
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Abstract

Complex systems are not entirely decomposable; hence, interdependencies arise at the interfaces in complex projects. When changes occur, significant risks arise at these interfaces as it is hard to identify, manage and visualise the systemic consequences of changes. Particularly problematic are the interfaces in which there are multiple interdependencies, which occur where the boundaries between design components, contracts and organisation coincide, such as between design disciplines. In this paper, we propose an approach to digital twin-based interface management, through an underpinning state-of-the-art review of the existing technical literature and a research agenda to identify the characteristics of future data-driven solutions. We set out an approach to digital twin-based interface management and an agenda for research on advanced methodologies for managing change in complex projects. This agenda includes the need to integrate work on identifying systems interfaces, change propagation and visualisation, and the potential to significantly extend the limitations of existing solutions by using developments in the digital twin, such as linked data, semantic enrichment, network analyses, natural language processing (NLP)-enhanced ontology and machine learning.

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