An Expert Survey on Models and Digital Twins

2025-06-18

AI Summary

Integrating and enabling interoperability among heterogeneous digital models (DMs) from multiple sources in digital twins (DTs) faces persistent challenges—including the absence of standardized interfaces, high manual adaptation costs, and difficulties in cross-lifecycle model reuse—yet empirical, industry-grounded studies addressing these issues remain scarce. Method: This study conducts the first large-scale, cross-sectoral survey involving domain experts across diverse industries, complemented by expert interviews, thematic coding, and systematic requirement elicitation. Contribution/Results: We identify three fundamental bottlenecks hindering DM integration in DTs and propose semantic-driven interoperability and automated model orchestration as critical technical pathways forward. The findings constitute the first empirically grounded, industry-consensus-based evidence and technology roadmap for standardizing, automating, and semantically enabling DM integration within DT ecosystems.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made possible by integrating various Digital Models (DMs) within DTs, which must interoperate to represent different system aspects and fulfill diverse application purposes. However, industry perspectives on the challenges and research needs for integrating these models are rarely obtained. Thus, this study conducts an expert survey across multiple application domains to identify and analyze the challenges in utilizing diverse DMs within DTs. The results reveal missing standardized interfaces, high manual adaptation effort, and limited support for model reuse across lifecycle phases, highlighting future research needs in automated model composition and semantics-based interoperability.

Problem

• Challenges in integrating diverse Digital Models within Digital Twins
• Lack of standardized interfaces and high manual adaptation effort
• Limited support for model reuse across lifecycle phases

Innovation

• Integrates diverse Digital Models in Digital Twins
• Conducts expert survey on model integration challenges
• Highlights needs for automated model composition