A systematic review of digital twins as decision support systems: a classification framework and identification of organisational constraints

Abstract

Digital twins (DTs) are increasingly framed as decision support systems (DSSs), yet no framework has systematically examined how DT configuration shapes the decision support they provide. This study presents a systematic literature review of 299 studies from four databases, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to examine how DT configuration influences decision support and value realisation. We develop a four-dimensional classification framework comprising abstraction level, DSS mechanism, decision level, and value type, and statistically test the relationships between these dimensions. The results show that DTs support decisions through four distinct mechanisms that differ in how information flows and how involved the decision maker remains. Model-driven DTs, which compute answers through embedded models, take on more of the reasoning themselves, whereas knowledge-driven DTs, which let users explore scenarios, keep decision makers actively involved. Whether a DT delivers value depends on organisational conditions as much as on its technical capability. Fully automated decisions are rare and occur mainly in model-driven DTs, suggesting that governance and accountability, rather than technology, limit automation. Model-driven DTs are also seldom used for long-term strategic decisions, as are DTs that represent only a single component rather than a wider system or enterprise. These patterns have been stable over time and do not appear to be driven by the uneven spread of studies across industries. Overall, the findings indicate that DT value depends on alignment with organisational decision contexts rather than technical sophistication alone. This paper highlights organisational and governance factors that cannot be captured by purely technical analyses.