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A digital twin is a computational model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as a digital counterpart of it for purposes such as simulation, integration, testing, monitoring, and maintenance.

By its strict definition, a digital twin is distinguished from an ordinary simulation in that it continuously uses real data from its physical counterpart to dynamically synchronize with the real system. A model that operates without data from its physical counterpart may also be described as a digital twin, but this is considered an overly broad and largely marketing-oriented interpretation of the concept.

A digital twin is "a set of adaptive models that emulate the behaviour of a physical system in a virtual system getting real-time data to update itself along its life cycle. The digital twin replicates the physical system to predict failures and opportunities for changing, to prescribe real time actions for optimizing and/or mitigating unexpected events observing and evaluating the operating profile of the system." Though the concept originated earlier (as a natural aspect of computer simulation generally), the first practical definition of a digital twin originated from NASA in an attempt to improve the physical-model simulation of spacecraft in 2010. Digital twins emerged through continual improvements in modeling and engineering.

In the 2010s and 2020s, manufacturing industries began moving beyond digital product definition to extending the digital twin concept to the entire manufacturing process. Doing so allows the benefits of virtualization to be extended to domains such as inventory management including lean manufacturing, machinery crash avoidance, tooling design, troubleshooting, and preventive maintenance. Digital twinning therefore allows extended reality and spatial computing to be applied not just to the product itself but also to all of the business processes that contribute to its production.^{[citation needed]}