Architecting Digital Twins for Smart Manufacturing: A Unified Framework for Operational Efficiency
Keywords:
Digital Twin, Smart Manufacturing, Predictive Maintenance, Operational Efficiency, Industry 4.0, Cyber-Physical Systems, Energy OptimizationAbstract
The adoption of digital twin (DT) technologies in smart manufacturing is accelerating, driven by the need for enhanced operational efficiency and responsiveness. This study presents a unified DT framework that integrates predictive maintenance, energy management, and production scheduling to optimize performance metrics such as Overall Equipment Effectiveness (OEE), downtime, and energy consumption. Employing a mixed-method approach, the research synthesizes data from empirical studies, benchmarks industry KPIs, and validates the framework through scenario-based simulations. Results demonstrate significant operational gains: OEE improved by 10–15 percentage points, unplanned downtime was reduced by up to 45%, and energy usage decreased by as much as 30%. These efficiency gains are attributed to DTs’ capabilities in real-time monitoring, predictive analytics, and rapid decision-making. The proposed framework also addresses major implementation challenges, including system interoperability, data integration, and organizational change readiness. By linking digital architecture to measurable KPIs, this research contributes a scalable model for guiding digital transformation in smart manufacturing systems.
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