Abstract
Recent technological developments and advances in artificial intelligence (AI) have enabled sophisticated capabilities to be a part of digital twins (DTs), virtually making it possible to introduce automation into all aspects of work processes. Given these possibilities that DT can offer, practitioners are facing increasingly difficult decisions regarding what capabilities to select when deploying a DT in practice. The lack of research in this field has not helped. It has resulted in the rebranding and reuse of emerging technological capabilities such as prediction, simulation, AI, and machine learning (ML) as necessary constituents of DT. Inappropriate selection of capabilities in a DT can result in missed opportunities, strategic misalignments, inflated expectations, and the risk of it being rejected as hype by the practitioners. To alleviate this challenge, this paper proposes a digitalization framework, designed and developed by following a design science research (DSR) methodology over a period of 18 months. The framework can help practitioners select an appropriate level of sophistication in a DT by weighing the pros and cons for each level, determining evaluation criteria for the digital twin system, and assessing the implications of the selected DT on the organizational processes and strategies and value creation. Three real-life case studies illustrated the application and usefulness of the framework.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may be only provided with restrictions. The student reports used for the model validation are confidential but can be provided after signing a nondisclosure agreement (NDA).
Acknowledgments
We thank all the experts who gave us their valuable time and feedback. We also thank the graduate students who used the framework in their class and made this study stronger. We acknowledge the financial support provided by Center for Integrated Facility Engineering (CIFE) at Stanford University. The authors also express their gratitude to Tulika Majumdar, Rui Liu, Hesam Hamledari, and Alberto Tono for their valuable feedback on the framework and the manuscript.
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Journal of Management in Engineering
Volume 38 • Issue 3 • May 2022
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© 2022 American Society of Civil Engineers.
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Received: Oct 1, 2021
Accepted: Dec 22, 2021
Published online: Mar 1, 2022
Published in print: May 1, 2022
Discussion open until: Aug 1, 2022
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