From manufacturing to oil and gas exploration, various industries have taken huge risks and sometimes suffered considerable losses within their operations due to trial and error processes performed during production. The healthcare industry in particular has seen many patients lose their lives due to wrong diagnosis or the use of inadequate equipment. For ages, it has been of the utmost importance for organizations worldwide to provide the best services and products to customers while ensuring their safety. With innovation and performance as the cornerstones of their operations, most companies are competing to develop enhanced services, products, and operations in order to cater to their consumers' needs.
The idea of having a platform to design and test products before manufacturing them gave birth to the notion of Digital Twin Technology. A digital twin is simply a digital representation of an actual physical product or process, which allows for alteration and adjustment try-outs that would have otherwise been too risky and expensive to perform on real physical objects. The high competitiveness within markets has forced many businesses to focus on the application of sophisticated virtual product models to shorten marketing time and increase the performance of product development. Those virtual models are the result of cyber-physical production systems (CPPS), together with model-based systems engineering (MBSE), and the growing digitalization of manufacturing. They not only allow for the early and efficient assessment of the function of products, based on the results of design decisions but they also efficiently predict the effects of product and process development.
In simple terms, a digital twin accesses the important properties of product models such as interoperability and scalability as well as different aspects of the product life-cycle – development, introduction, growth, maturity, and decline. The twin can be a prototype before the physical version is built or it can be designed based on a prototype of its physical counterpart, receiving input from sensors and gathering data to simulate the physical product while giving insights into performance and potential problems. For instance, the digital twin of a self-driving car is not simply a 3D model but it also takes into consideration the complex environments of navigation, communication, electronics, climate control, collision avoidance, etc... the digital twin of that car would be able to analyze how the car will perform not just in its physical environment, over its entire lifecycle but also under every condition imaginable, from its conception to its last day on the road.
Another good example is General Electric's Digital Wind Farm. The company used its Predix software platform to create a digital twin of its wind farm, in order to collect, visualize, and analyze unit and site-level data. According to Brian Case, VP Product at GE Renewable – "the goal was to convert unplanned maintenance into planned activities, thereby minimizing turbine downtime and reducing tower climbs." He carries on to say that having a digital wind farm with advanced analytics has allowed the company to increase revenue, reduce risk, and reduce cost.
Although digital twin technology presents great prospects for the future – due to the rapid proliferation of computing power – few concerns arise with regards to the affordability of the technology to small and medium enterprises. As digital twin could save a company about 20 to 30 percent of development costs, the threshold cost for setting a digital twin is estimated at €50,000 according to High Tech Software Cluster. But as it develops, economies of scale are likely to improve, reducing the threshold cost and allowing its application on a wider scale.
With the ongoing race to gain a competitive advantage, success stories about digital twin technology will surely continue to emerge, leading more companies to invest in it. Although the concept of "digital twin" was first introduced about 18 years ago, we are only now witnessing a breakthrough in the industrial world, not just for cars but eventually for everything else created by man.