ABSTRACT This umbrella review synthesizes findings from 79 peer‐reviewed systematic literature reviews (SLR) on digital twin (DT), a rapidly advancing technology with broad applications across various industries. By aggregating and analyzing second‐order evidence from existing SLRs, this study offers a high‐level overview of how DTs are defined, applied, and challenged across diverse industries. We identify recurring research themes, methodological inconsistencies, and sector‐specific gaps, with a particular focus on standardization, resource limitations, data quality, and system integration. Our review reveals that while DTs are widely discussed in relation to predictive maintenance, operational efficiency, and lifecycle optimization, substantial variability exists in definitions, frameworks, and implementation readiness across domains. Furthermore, underexplored areas such as user involvement, data ethics, and later lifecycle stages highlight the need for more interdisciplinary and practice‐informed approaches. By consolidating this body of knowledge, we provide strategic insights to guide future DT research and development, emphasizing the need for unified frameworks, cross‐sector knowledge transfer, and more robust evaluation of DT maturity and impact.
Wooley et al. (Tue,) studied this question.