The Road Ahead for Digital Twins

Digital twin representations, where an actual real-world product, process or system serves as a digital counterpart for simulation, testing, monitoring and integration, aren’t new. However, the growing importance of generative AI platforms are presenting industry with an opportunity to rapidly grow these capabilities. 

Creating doppelgangers of the entire technology stack of the world to experiment on anything from telephone infrastructure, to optimizing manufacturing workflows or experience testing new products, the digital twinning could well be the new way of collecting data, its analysis and pattern identification that helps industry respond with substantially higher accuracy. 

As representations of tangible assets or systems in the real world, digital twins can transform industries through sharper and end-user-focused experiments, thus creating better technology solutions for challenges than ever before. According to Mike Kuniavsky, senior principal at Accenture Labs, the world of digital twins is growing by the day. 

How does the digital twin system work?

As explained earlier, digital twin is a virtual representation of a real object or system that traverses its entire lifecycle, getting real-time data and using simulation, machine learning and artificial intelligence to generate insights that help smarter and faster decision-making processes in the enterprise. 

It can be described as a tech-enabled proxy that mirrors the state of the real thing or process or model, person or even an organization. There are several built-in functionalities such as analytics, 3D models, IoT platforms and CRMs that allow a one-to-one association enabling easy monitoring through a complete lifecycle. 

These doppelgangers are closely connected to the real ones that allow experimentation and manipulations in ways that are difficult or almost impossible in the physical world. Being intertwined also ensures that when there is a change in the real object, these are captured and reflected in the virtual twin. 

So, what difference does all of this make?

For starters, the process of quantitative analysis of complex business processes or real-time visualization of a single object becomes far easier. For example, the design, manufacture, go-to-market strategies to eventual sale of a new product can be twinned to access data on a real-time basis. The same goes for robotic processes being twinned for observation. 

Kuniavsky believes that going forward digital twins would allow organizations to use all tools developed for analyzing real-time digital phenomena more efficiently and with a built-in analytical framework. Right from databases to filters, visualizations to pattern identification, the entire process can be monitored virtually without messing up with the physical assets. 

Industry experts say that virtual environments and digital twins have already taken root across multiple processes such as testing and refining, optimizing workflows to creating sandbox ecosystems for partner-driven experiments. Manufacturing, retail, construction, transportation, healthcare and telecom are some of the industries already experimenting with digital twins. 

Companies like IBM, Nvidia and Ericsson are using digital twins while others like Nokia are researching 5G use cases with AI and ML processes run on the doppelganger systems. While monitoring the impact of 5G implementations, these are able to even suggest next steps in an automated fashion. 

In the industrial automation regime, digital twins are being used during the production process to collect data and transmit it over a 5G link while displaying the digital twins of machines so that the real-time adjustments around operating parameters are automated. Of course, twinning also supports capacity management and process optimization efforts.  

The twins will get smarter with artificial intelligence

There could be several AI technologies to analyze digital twin objects or processes across multiple ways that could substantially cut down time taken for the same process using traditional means. For example, pattern recognition is one area where digital twins can become smarter as there are AI tools that can distinguish between images faster without errors. 

If applied to a business process, these analytics where AI goes through billions or trillions of data points to identify otherwise unrecognizable patterns could be used across industries for finding and fixing issues. Kuniavsky notes that AI could recognize the wobble of a wheel on a train and predict track renewal timeframes. 

In fact, going forward generative AI could even provide inputs of how much of a sample size needs to be analyzed to be able to identify a pattern. By reviewing data from an actual phenomenon, digital twins of the future may well be able to create enough virtual examples that could result in new versions of existing products, processes or systems.