A digital twin is a dynamic software model of a physical thing or system that relies on sensor data to understand its state, respond to changes, improve operations and add value. Digital twins include a combination of metadata (e.g., classification, composition and structure), condition or state (e.g., location and temperature), event data (e.g., time series), and analytics (e.g., algorithms and rules).
Within three to five years, hundreds of millions of things will be represented by digital twins. Organizations will use digital twins to proactively repair and plan for equipment service, to plan manufacturing processes, to operate factories, to predict equipment failure or increase operational efficiency, and to perform enhanced product development. As such, digital twins will eventually become proxies for the combination of skilled individuals and traditional monitoring devices and controls (e.g., pressure gauges, pressure valves).