How do you implement a digital twin for manufacturing process optimization?

Digital twin implementation involves: Physical asset modeling (3D CAD, kinematic models), Sensor integration (vibration, temperature, power, vision systems - edge computing for real-time data), Data infrastructure (time-series database, cloud platform like Azure IoT, AWS), Physics-based models (FEA, CFD, process models), Machine learning integration (anomaly detection, predictive models trained on historical data), Real-time synchronization (bidirectional data flow, latency management), Visualization and analytics (3D real-time visualization, dashboards), and Closed-loop control (parameter optimization, adaptive control). Applications: Predictive maintenance (remaining useful life prediction), Process optimization (real-time parameter adjustment), Quality prediction (correlate process data to outcomes), and Virtual commissioning. ROI typically 10-25% efficiency gain. Requires cross-functional team (OT, IT, data science, domain experts).