Automation has been a standard part of business for centuries now, ever since the First Industrial Revolution. The Third Industrial Revolution introduced businesses to the digital age, and software solutions started becoming commonplace. There’s something different now about the digital world compared to years past, however. These days, thanks to advancements in machine learning and capabilities granted by the Internet of Things (IoT), computers and software application can now communicate and carry out functions with little to no human intervention. These factors have produced the Fourth Industrial Revolution, frequently called Industry 4.0.
Industry 4.0 has allowed enterprises to connect machines in ways never seen before. These new capabilities allow for faster and more accurate data analytics than ever before. Concepts based on deep learning allow analytics solutions to keep up with real-time data, so enterprises can assure their data quality and gain actionable insights into their business processes to boost operational efficiency. What’s even better is that now there is a way to accurately design, model, and test new solutions without having to interrupt the actual workflow. This is possible thanks to digital twin technology. But what is a digital twin?
Digital Twins Defined
Put simply, a digital twin is a dynamic, digital representation of a product, service, or business process. It’s a perfect example of how the physical world and digital world often collide in a world ruled by big data and digital transformation. Thanks to the ability to collect and analyze data in real-time, it’s easy to use real-world data to create a simulation that perfectly mimics the real thing. These digital twins can then be used to make predictions about how product tweaks or revised processes will work in the real world.
Traditionally, whether you wanted to produce a physical product or a digital solution like a hotelling software program, you had to build a physical version of the product first before you could start tweaking it for better results. With the digital twin concept, you can create a digital version of the entire product or process from the design state all the way to a test deployment. Digital twins are also able to generate data in real time to help developers anticipate challenges with the real thing and overcome them in a much more cost-effective manner. Here are the basic types of digital twins that you’re likely to work with.
These are simulations of physical objects. This type of digital twin technology can accurately test a new build or existing build in a variety of conditions to anticipate any flaws it may have and address them before new products are made. This is much easier than going through the traditional product design and testing phases, which could result in multiple failed products before the right model was made.
These digital twins can replicate manufacturing floors or even entire factories and organizations. Modern software integrations allow organizations to collect master data in a single source of truth, and this data is often used in business process management to find better outcomes for business processes.
With a digital twin, an entire system can be replicated to help with predictive maintenance or even use artificial intelligence to repair sensors and other equipment without human intervention.
If you can simulate an entire system, you can also use digital twins to simulate individual processes within those systems. This digital technology has a wide variety of applications, such as increasing production efficiency or eliminating bottlenecks in the supply chain. IoT data gathered from GeoLocation systems, for example, can help fleet managers plan more efficient routes for delivery. A digital twin model could also be used to test out AI-powered camera systems or sensors on the factory floor to monitor for any possible safety or efficiency issues.
Digital twins help your enterprise design new products and processes with accuracy, so the trial and error and guesswork of the past can be eliminated.