Billions of things will be represented by the digital twins in the next three to five years, signifying the tremendous potential of the digital twin technology. Businesses from GE to Siemens are currently using digital twins to monitor the conditions of wind turbines and manufacturing equipment in real time. Tesla has a digital twin of every VIN they manufacture, based on which Tesla regularly downloads software updates to its customers’ cars. Digital twin has become so vital to business today that it was named one of Gartner’s Top 10 Strategic Technology Trends for 2017. Many research firms are optimistic about the digital twin technology with IDC predicting that by 2018, companies who will invest in digital twin technology will see a 30 percent improvement in cycle times of critical processes.
Digital twin is a cloud-based virtual model of a process, product, or service. According to Dr. Michael Grieves, who introduced “digital twin” to the world, there are three tests to certify a virtual copy to be a true digital copy: First is the vision test where the virtual visual inspection would include disassembling the product and seeing completely realistic representations of its component parts; second is showing that the virtual product react realistically to testing, such as a digital wind tunnel or simulated tests; and the third test is about getting information from the virtual product through a physical inspection, just as a user would from an actual product. This coupling of the virtual and physical worlds allows analysis of data, monitoring of systems, and simulation of real-world conditions to proactively respond to changes, prevent downtime, improve operations, and develop new revenue models. Digital twin technology covers the entire lifecycle of the product or service from conceptualization till it retires.
The genesis of digital twin was in the form of 3D CAD models designed for the new product introduction. However, the proliferation of digital levers like cloud connectivity, IoT, augmented reality (AR), and machine intelligence provided a significant momentum for large-scale implementation of digital twins in various industries. The capability to loop back the physical product to its digital twin even after the physical model has left the factory with the help of sensors greatly enhances the utility of the digital twins, limiting itself not only to product designing and testing and, thus, qualifying all the test criteria to be a true digital twin. The digital twin has all the real-time performance data, sensor data, and inspection data along with the history of the maintenance performed, configuration changes, parts replacement, and warranty data, leading to reduced lifecycle ownership cost of the asset and valuable product intelligence for superior product innovation.
The bidirectional blending of the digital and physical worlds is happening today. That means that on one hand, it drives the reality of the physical twin into the world of the digital twin, allowing the product engineer to see the real-world conditions of any physical machine remotely, and to run simulation models and analysis using live data. On the other hand, it also enables a maintenance engineer closer to the physical machine to refer back to the design and service data of the digital twin products with the help of an augmented reality interface.
Digital twin is one of the IoT-fuelled business cases and it will need data coming from the sensors as well as it will draw data from various enterprise systems, such as the product lifecycle management (PLM), enterprise resource planning (ERP), customer relationship management (CRM), and service lifecycle management (SLM) for mining information about different aspects of the product or process at different points of time. Digital twin concept can be built leveraging the “digital core” as the central component, enabling easy and quick integration with the requisite elements.
Digital Twin was one of Gartner’s Top 10 Strategic Technology Trends for 2017
Finally, the future will move toward the digital representations of factories and environments, as well as personnel and processes which along with digital twins of physical assets will enable an increasingly detailed demonstration of the physical world for simulation, analysis, and control such that failures can be recreated virtually to gain substantial improvements in costs and cycle times.
