The digital twin concept is rooted in industrial simulation software, used since the 1970s for the precision design of parts in manufacturing, aerospace, semiconductors, and other sectors.

Internet of things (IoT) technology, data analytics, and machine learning enable digital twins to simulate connected processes and physical assets in several industries. Manufacturers use them to replicate machines on the factory floor, increasing uptime by predicting when equipment needs maintenance. Medical researchers are using digital twins of human organs to design safer, more effective treatments. In Las Vegas, planners are using a virtual version of the city in hopes of improving air quality and energy usage.

Among enterprise organizations that have implemented IoT projects, 62% plan to use digital twins, according to a Gartner survey.

Supply chains represent a powerful use case for digital twins, which often apply machine learning across numerous data streams to simulate the real-time movement of goods, material flows, inventory positions, and warehouse operations.

DHL, the international shipper, created a digital twin of a food-processing warehouse in Southeast Asia. Using sensors and cameras to capture data, the digital twin represents pallets inside the warehouse as virtual objects inside a 3D computer model. Using real-time operational data generated by the twin, warehouse supervisors can ensure that perishable goods are correctly shelved within 30 minutes of arrival and that goods slated for delivery are ready to be shipped within 95 minutes of request.

Digital twins allow thousands of decisions to be automated and applied at scale in real time, says Dan Isaacs, chief technology officer at Digital Twin Consortium, a global organization created to drive enterprise adoption of digital twins across multiple industries.