A digital twin is a dynamic, instance-based digital representation of an asset, process, or system, built from models that define its structure and behavior. It integrates multiple data sources to provide real-time visibility, simulation capabilities, and predictive insights. Digital twins enable organizations to break down data silos, improve collaboration, and support decision-making through analytics and AI. Different types—status, operational, and simulation twins—serve varying levels of complexity and use cases. By combining real-time data, machine learning, and simulation, digital twins allow organizations to optimize performance, predict failures, and continuously improve operations.

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