2022/01/14 by Ashwin Agrawal, Agrawal, Ashwin, Martin Fischer +3
Engineering · #Artificial Intelligence (cs.AI) #Digital Transformation in Industry #FOS: Computer and information sciences #Flexible and Reconfigurable Manufacturing Systems #Human-Computer Interaction (cs.HC) #Manufacturing Process and Optimization #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2201.06912
openalex publication_date 2022/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent technological developments and advances in Artificial Intelligence (AI) have enabled sophisticated capabilities to be a part of Digital Twin (DT), 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 while deploying a DT in practice. The lack of research in this field has not helped either. It has resulted in the rebranding and reuse of emerging technological capabilities like 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 risk of it being rejected as just hype by the practitioners. To alleviate this challenge, this paper proposes the 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, deciding 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 illustrate the application and usefulness of the framework.