🤖 AI Summary
Existing digital twin (DT) research lacks a cross-level unified architecture and empirical validation across manufacturing tiers. Method: This paper proposes a generic DT architecture encompassing data flow, core components, and interaction mechanisms; classifies multi-tier DT types and modeling techniques; and critically analyzes discrete-event simulation (DES) for dynamic system modeling. Contribution/Results: Through industrial case studies, the work empirically validates DT’s efficacy in enhancing operational efficiency, reducing downtime, and optimizing lifecycle management. It identifies three critical implementation barriers: data integration complexity, cybersecurity risks, and high deployment costs. The study provides both theoretical foundations and practical guidelines for standardized DT development and scalable industrial deployment across product/production-line, production-system, and enterprise levels.
📝 Abstract
Digital Twin (DT) has gained great interest as an innovative technology in Industry 4.0 that enables advanced modeling, simulation, and optimization of service and manufacturing systems. This article provides an extensive review of the literature on digital twins (DTs) and their utilization at the levels of product/production line, production system, and enterprise, and considers how they have been applied under real industrial conditions. This article classifies the types of DTs as well as modeling technologies of DT and applications in different fields, with particular focus on the research of strengths and limitations of Discrete Event Simulation (DES) for systems modelling. A generic structure for DT is proposed, outlining the essential components and flow of data. Case studies demonstrate the benefits of DTs for increased efficiency, reduced downtime, and improved lifecycle management, as well as challenges caused by the complexity of data integration and cybersecurity risk, and high implementation costs. This paper contributes to the growing body of knowledge by identifying both the opportunities and barriers to widespread DT adoption. This study concludes that while DTs offer transformative capabilities for enhancing efficiency and decision-making, overcoming these challenges is crucial for realizing their widespread adoption and impact across service and manufacturing sectors.