Digital transformation is accelerating in rapid fire fashion in industrial companies today thanks to the increasing adoption of digital twins. The primary drivers for the growing implementation of digital twins are the advances being made in IIoT, machine learning, and artificial intelligence. Digital twins are enabling industrial and manufacturing companies to develop and increase time to value in ways not previously seen before.
A digital twin can be defined as a digital representation that virtually simulates a real-life object, process, or system. A digital twin is typically comprised of internet of things (IoT) technologies, sensors, artificial intelligence, machine learning, and software analytics. Their sole purpose is to act as a living, digital simulation model that replicates and reflects the updates and changes that their physical counterparts experience. This enables the user to test different production scenarios in a test environment and validate these changes prior to implementing the new features in the production environment.
MACHINE LEARNING: THE FOUNDATION FOR THE DIGITAL TWIN
The primary purpose of a digital twin is to produce simulated data. This virtual environment can replicate and iterate an infinite number of repetitions and scenarios. The simulated data that is created via this process is propagated and perpetuated through continuous machine learning. This helps the system recreate potential real-world conditions that can be tested in a virtual environment. Another advantage is the ability to plan and test new features. While the digital twin represents reality, it allows users to get a view into the future.