Digital Twin - ThinkNotch

Many things in Industry 4.0 are advancing at a much faster rate than expected because of the interaction of different technologies, which have strong mutually conducive relations. Similarly, Digital twin, a child of Industry 4.0, is a technology that is getting thrust from advancing simulation technologies and products of the Internet of Things and big data analytics. Digital twin concept is not technically new but it is considered to be one of the top strategic technologies which are currently adopted mainly by R&D departments and is expected to have exponential growth across the board in the near future.

We will begin by answering a basic question, “What is a digital twin?”. A digital twin can be defined as a digital model that can represent a real-life product or any process in the development of that product. But, it is more than just a CAD model. The next question which arises is, “How is it different from simulation techniques which have now been in existence for a few decades?”. The answer to this question lies in the capabilities of a digital twin. Traditional modeling techniques focus on simulating any one of the phases of product life cycle: design, manufacturing, operation, maintenance or decommissioning; digital twin can be used to simulate each of these phases, but, its actual strength is its capability to simulate all the phases together and get feedback from one phase for another. This cross-functionality makes this technology useful for multiple layers of the organization.

The digital twin adds value to different phases of the product life cycle. During the design and manufacturing phases, it can be used for detecting the defects early, improving the overall quality of the product and reducing lead time and cost to launch new products by the use of feedback using IoT. In operation and maintenance phases the digital twin can reduce the fluctuations in the processes and provide useful information on proactive maintenance. It also provides decision support in optimizing logistics and inventory. Finally, Sales and aftermarket activities can get a boost from the information from the digital twin, which paves the way for more efficient servicing and warranty claims. A digital twin is capable of providing much more information than stated in this paragraph.

The next important question is, Do we really need digital twin technology? The answer is hidden in the exponential growth pace of current technology markets. Digital twins can be used to monitor the product life cycle, validate the design with the feedback of real-world data and get information to make informed decisions on design changes. System models also reduce the need for large scale testing of new automated systems and we get a plethora of diagnostic and prognostic information.

What does digital twin being a less adopted tell us? Well, it tells us that the path of this technology has some hurdles. A large investment is required by the companies to develop this technology, so only a few companies can afford to invest in it. Another factor is less confidence in the validity of the information or equations used to develop the digital twin. But, the future of digital twin appears to be bright as the adoption of this technology is increasing and more investment in it will lead to the development of robust digital twin models