From digital shadows to assured intervention: a systematic review of embodiment, authority, and assurance in manufacturing quality digital twins

Rapid advances in AI and robotics are extending manufacturing digital twins beyond passive monitoring to closed-loop control and physical intervention. Yet in manufacturing QA, the term “digital twin” is used for both systems that only observe and systems that act on the physical process. This distinction matters because once a digital twin acts, model validity is no longer enough; the intervention must also be authorized, bounded, traceable, and verified. Existing maturity frameworks capture connectivity and automation but give less attention to the assurance requirements that arise when digital-twin outputs are translated into physical action. We address this gap by introducing an Embodiment-Authority-Assurance (EAA) framework that distinguishes how quality information leads to physical action, who has authority over that action, and how the intervention is assured. We apply the framework to a systematic review of 235 manufacturing QA studies, selected from 868 publications identified through Scopus and Web of Science between 2017 and 2026, classifying them across five maturity levels from digital shadows to governed adaptive QA twins. We found that the frontier of manufacturing QA digital twins is shifting from monitoring and advisory support toward human-approved and bounded autonomous intervention, with higher-maturity applications becoming increasingly visible in recent years. Yet these advances remain fragmented across the literature, with no reviewed study bringing them together into a fully assured intervention chain. By revealing this shift, our study reframes manufacturing QA digital-twin maturity around a new frontier: not simply whether digital twins can act, but whether increasingly autonomous interventions can be governed and assured.