Data has completely revolutionized our life. Each sector relies heavily on the insights garnered from data from real estate to software development. From data analytics to predictive analysis, companies can use many valuable ways to make the best use of data for their profitability. Out of all these technologies, one of the most trending technology is the digital twin.
Have you never heard of it before? Don’t worry; we got you covered. Every industrial product will have a dynamic digital representation under the Digital Twin idea, which depicts the relation between the virtual and physical world. Throughout the product development life cycle, companies can have an entire online trail of their products from the first design step to the final deployment phase. Those “related virtual items” gather statistics in actual time, permitting companies to analyze and anticipate issues troubles ahead of time or offer early warnings, reduce the product’s downtime, create new opportunities, and build better destiny merchandise at lower costs using analogs.
All of this will have an immense influence on providing a better customer experience in the workplace. Digital Twins, which combine Big Data, Artificial Intelligence, Machine Learning, and the Internet of Things, are critical components of the industry and are primarily employed in the Industrial IoT, manufacturing, and engineering sectors. Digital Twins have become more cost-effective and relevant to businesses because of the Internet of Things’ vast reach and use.
How Do Digital Twins Work
Digital Twins, or virtual equivalents of tangible assets, are formed using sensors to create digitalized duplicates of tools, machinery, or physical sites. These virtual models can be made using simulations even before the product is constructed. Engineers collect and synthesize data from many sources, including operational data, physical data, insights from analytics tools, and manufacturing to produce a product’s digital twin.
This data is fed into a physics-based simulation model built with the AI algorithms; by incorporating Analytics into these models, we can extract meaningful insights about the product. The continuous data flow aids in obtaining the most accurate insights and analysis about the product, hence improving the company outcome. As a result, the digital twin will work as a real-time simulation of the actual equipment.
Digital Twin v/s Predictive Analytics
Now that we have understood what a digital twin is, one question that might come to mind is how a digital twin is different from the regular predictive analysis.
The term’ predictive analytics’ may have different meanings for different business analytics users. They might be thinking about a simpler model that conducts a test against a set of variables in this case (e.g., how price changes might affect product sales). In this scenario, a digital twin can be considered a more exact and complex version of a predictive model.
It uses dynamic, real-time data streams rather than stored data. It might consider many of them rather than testing for a few variables. It can create a picture of the whole instead of just looking at a few results in one specific area. The quality and timeliness of incoming data distinguish digital twins from other computer models; digital twins have a real-time relationship to the item they represent. A digital twin of a computer manufacturing factory, for example, would receive real-time data from sensors embedded in the machines; a utility grid would receive real-time use statistics, and so on.
Why Digital Twins Are Generating Buzz Right Now
Digital Twins is not a brand new concept, and it has been around for decades, so one might ask, why is it in buzz now only? Why not before if it is so effective? The answer is simple since the technology has improved drastically over the year, it is now possible to use computational power for the digital twins in the industry.
The other reason is that businesses have begun to grasp the potential of digital twins after seeing the benefits of AI and data analytics. We currently use a variety of AI and machine learning models to handle individual problems; however, with a digital twin, we might deliver market intelligence for an entire company. We can utilize it to accurately model purchase behavior, improve customer experience, and forecast consumers’ needs. These things can be achieved using the current AI technologies, but we can do them better with digital twins.
One of the biggest buzzwords in the business industry is the fourth and new industrial revolution, which is also known as Industry 4.0. It consists of data interchange, automation, and manufacturing technology. Digital Twins are now at the heart of the new revolution, opening up unimagined possibilities. It replaces the old strategy of “first build, then modify” with a virtualized system-based design method that understands every equipment or system’s particular features, performance, and causes of problems, if any, to extract a significantly more efficient role from it. With the increasing innovation in technology, the functions of the digital twin will also increase. We may get a real-time panoramic perspective of what’s going on using digital twins. Remember that a digital twin is a replica of a real-world object updated regularly. As businesses strive to become more data-driven, digital twins appear to be the next generation of business AI.
Contributed by Dr. Anil Kaul, CEO, & Dr. Sudeep Haldar Senior Vice President of Growth Analytics and AI Solutions, Absolutdata(An Infogain Company)