As a concept, digital twin has been around for a long time and was pioneered by NASA which created full-scale mockups of space capsules to detect and diagnose problems in a risk-free environment these mockups were eventually upgraded to full digital simulations. However, after Gartner listed digital twin in its top 10 strategic technology trends, the concept regained popularity and caught the imagination of business leaders. Digital twin technology is being widely adopted by organizations across sectors due to the numerous benefits it provides.
With an estimated 21 billion connected sensors and endpoints, digital twins will exist for billions of things in the near future.
Gartner
A digital twin is a virtual model or digital representation of a system or physical object spanning its entire lifecycle. Real-time data flows between the physical object or system and its digital model in both directions, any change made to one is automatically reflected in the other. Real-world data provided to digital objects is helpful in determining the performance of physical objects in real-time. Furthermore, digital representation can mirror and synchronize processes and people providing comprehensive insights into aspects of operations that have largely remained inaccessible.
As mentioned above, the concept of digital twin was introduced by NASA for its space missions. In fact, in the famous rescue mission when Apollo 13 developed a technical snag stranding the astronauts in space, a digital twin model of the space shuttle was used by engineers on the ground to diagnose and work out a solution that led to the safe return of the astronauts. However, the concept never really picked up and even NASA went on to rely more on simulations.
Part of the reason the concept of digital twin did not go mainstream could be the technological limitations including prohibitive costs of bandwidth and computational and storage capabilities. This has changed radically in recent years. The hardware industry alone has made massive strides. Data storage has become cheaper, computational abilities have improved exponentially and so has connectivity. With increased capabilities, the feasibility of creating a virtual representation or model of physical objects, processes, and systems is no longer bound by constraints.
In Industry 4.0, digital twins have become a valuable technology for organizations using IoT and advanced data analytics. IoT devices generate a large amount of real-time data that can be used along with digital twin technology for several benefits. For instance, data generated by sensors embedded on physical objects is fed into the virtual model to study performance issues and suggest improvements. In a manufacturing space that deploys a lot of IoT devices and sensors, digital twins can provide an accurate and real-time virtual representation of what’s happening on the factory floor by capturing machinery performance and environmental condition data.
As a continuous analysis process, the two-way flow of data between physical objects and digital models helps gain greater insights into the performance of the manufacturing process and provides information for optimization. The fact that all kinds of real-time data can be captured and analyzed, and all processes replicated differentiates digital twin technology from simulations. If Industry 4.0 is described as a digital manufacturing environment with IoT devices, advanced data analytics, and interconnected processes, digital twins play a crucial role.