How AI is Supercharging Digital Twins

The term “digital twins” (which refers to simulations of real-world environments) might be relatively new, but the technology isn’t. It dates back to the 1960s, when NASA replicated its spaceships on earthbound machines. However, recent innovations like AI are making digital twins a key part of industry 4.0 and taking them to the next level. A recent study predicted the market would reach $90 million by 2032 — representing a 25% increase from 2023.

This makes it an exciting investment opportunity, especially for those interested in the manufacturing industry and related sectors. Let’s look at the growth and applications of digital twins, and which companies are leading the space.

Introducing digital twins

A digital twin is exactly what it sounds like — a digital representation of something that exists in physical reality. They’re often used to improve efficiency (especially in a manufacturing context) since engineers can test improvements, analyze where problems lie, and even run simulations. Other uses include R&D, the disposal of products reaching the end of their lifecycle, and the reduction of a company’s carbon footprint.

Generally, digital twins are made by attaching sensors to the physical object manufacturers want to model. This allows analysts to obtain data about the object’s performance — for instance, in different temperature or weather conditions — to ensure the twin is accurate.

Note that digital twins are slightly different from simulations since they can use real-time data (obtained from sensors). Plus, they can run multiple simulations instead of just one. Digital twins are most often used in manufacturing, but they can also benefit healthcare, urban planning, design, automotives (especially autonomous vehicles), and more.

The role of AI

So, where does AI enter the picture? Artificial intelligence can improve the efficiency of digital twins by providing insights that go beyond what real-world sensors provide. It can also make predictions about the future. AI can independently decide which tests it needs to run based on the data it receives, and it can then predict which actions would achieve the desired outcomes — and all of this happens automatically. Plus, algorithms can quickly pick up on any abnormal information from the sensors.

Essentially, digital twins can operate at three different levels of sophistication:

1. Collection of data to create a virtual twin that monitors physical items.
2. Virtual twins that also allow for simulations.
3. The addition of machine learning to analyze the data collected continuously.

For even more sophisticated results, digital twins may also include virtual reality, mixed reality, or internet of things (IoT).

Companies providing digital twin software

Not all companies can or want to develop their own digital twins and will instead rely on other firms’ software. Software companies working on digital twin programs are therefore a major investing opportunity.

Amazon’s AWS is trying to improve the accessibility of creating digital twins with its AWS IoT TwinMaker. This provides software for companies that makes it more cost-effective to build digital twins. Using the program, they can create a virtual representation of their system, and they can choose between adding their own data connectors or using built-in data connectors from AWS. 

It also allows users to add advanced tools, such as machine learning analytics and simulations. Further developments are likely to come.

Microsoft offers Azure Digital Twins, which has a similar proposition: The ability to create digital twins using its software. It also integrates with Azure data analytics to make it easier to model environments and get insights, and twins made through the program can also connect to Azure AI and IoT platforms.

Finally, GE Digital (an offshoot of General Electric) offers digital twin software that includes machine learning to help achieve industrial optimization. It has also worked alongside AWS to help build a universal, cloud-based platform that can bring their strengths together.

Companies using digital twins

Then there are the companies putting the technology into practice. There are countless applications of digital twins, including exciting ideas such as smart cities — for example, Singapore and Shanghai both have digital twins for the entire cities. However, from an investing perspective, manufacturing is one of the most promising areas.

One of the most well-known companies making use of digital twins is Tesla, which creates digital simulations for every single car it sells. Each vehicle has sensors and sends data to the cloud, and Tesla then uses AI to analyze their performance. This helps the company to make improvements and reduce the need for repairs in future.

Unilever built virtual twins of its factories using technology from Microsoft. It uses data from sensors to test out different operational changes and make production more efficient. Then, it supplemented this with machine learning to look for ways to boost efficiency and flexibility. The result was cost savings of $2.8 million at a site in Brazil alone thanks to productivity increases and reduced energy use. Now, it has ambitions to create a digital twin of its entire supply chain to continue boosting efficiency.

Other companies worth watching include Siemens, Cisco Systems, and General Electric, all of which are using digital twins to improve their operations.

What it means for you

Digital twins are everywhere, and potential applications only seem to be growing as technology develops. For investors, there are two principal areas to look at: Software companies with digital twin programs, and firms using digital twins and AI to supercharge their own operations. Manufacturing is one of the most promising areas right now, but as applications are likely to grow over the next few years.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.