Digital Twin: From Concept to Practice

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.