Barriers and Enablers for Traceability and Federation in Model-Based Digital Twin Engineering

Context: Digital Twins (DTs) have gained significant momentum over the past decade, reshaping industrial practices across various sectors, including manufacturing, construction, and infrastructure management. While this rapid adoption has driven innovation in cyber-physical integration, the scientific foundations and engineering principles of DTs are still being actively developed. Objective: Although foundational contributions to model-based engineering (MBE) for digital twins exist, mainly in the form of conceptual frameworks, critical dimensions, such as traceability and federation, remain underexplored. This study aims to provide a comprehensive overview of the state of the art in model-based digital twin engineering, with a focused investigation of these two pivotal concerns. Method: We conducted a multivocal literature review including 20 peer-reviewed publications and 56 gray literature sources, complemented by a survey of 51 expert stakeholders. The survey was administered during an open session of the MATISSE project plenary meeting, which included both consortium members and external participants from academia and industry. This mixed-method approach was designed to capture both academic and practitioner perspectives on the enablers and barriers to traceability and federation in DTs Results: Our study reveals how traceability and federation are currently conceptualized, supported, and challenged within model-based DT engineering. We synthesize a landscape of relevant modeling languages, tools, application domains, and technical enablers, and identify key limitations and research gaps. Furthermore, we evaluate existing MBE solutions in light of industrial needs, distinguishing between areas of maturity and those requiring further innovation. The survey results should be interpreted in light of the sampling context, as the respondent pool mainly reflects researchers and early adopters. Conclusion: This work offers evidence-informed guidance for researchers and practitioners by identifying concrete opportunities for improving traceability and federation in DTs. Our findings point to standardized modeling approaches, cross-domain interoperability, and AI-supported traceability mechanisms as promising directions warranting further investigation. Future research priorities include the development of protocols for federated DT systems and the adoption of a system-of-systems perspective to address complex industrial and societal challenges.