Over the last decade, we’ve witnessed the emergence of numerous technologies that seem to have been taken out of a Sci-Fi movie. This is partly due to the Internet since it allowed computers worldwide to share data like never before. This convergence of information has been one of the main drivers for the rise of innovative technologies such as artificial intelligence, blockchain, augmented reality, and digital twin, among others.
One interesting aspect to highlight is that these advancements enhance their efficiency when working together in a synergic way. For instance, digital twin employs the Internet of Things (IoT) to create near-perfect simulations, or cognitive assistants use machine learning to solve complex tasks. In this article, we will talk about digital twin — a technology that has proven to increase efficiency across different industries. Its impact is such that it was included in Gartner’s Top 10 Strategic Technology Trends back in 2017. Let’s check it out.
In this article, we will talk about digital twin — a technology that has proven to increase efficiency across different industries. Its impact is such that it was included in Gartner’s Top 10 Strategic Technology Trends back in 2017. Let’s check it out.
What is digital twin technology?
In a nutshell, a digital twin (DT) is a virtual representation of a physical object or system. It’s software that replicates an item from a physical into a digital scenario. Having this clear, you might ask, why is this important? This technology enables you to simulate real-world conditions, thus collecting information regarding performance and outcomes. By doing so, you’ll be able to predict the behavior of an object, process, or service before it happens in the physical world.
Imagine you create a digital model of a central processing unit (CPU). With it, you’ll be able to run all kinds of tests, such as the maximum speed, overclocking capabilities, temperature control, and know how it’ll perform without putting it into production.
How does digital twin technology work?
The most important input is data, and the more, the better. To model an exact copy of a physical system, it’s necessary to provide all possible findings regarding its components and characteristics. Once created, it is fed from real-time sensors that provide constant information of the “real world” version. Back to the CPU example, sensors will provide data regarding temperature, clock speed, frequency, etc.
Its complexity depends on the amount of information provided. Consequently, there is a directly proportional relation between data and accuracy.
Types of Digital Twins
The technology can be divided into three main categories:
Product digital twins are utilized to test design efficiency. Pretty much as the previous CPU example, they are used to check and analyze product performance and suggest changes that add value.
Production digital twins are designed to verify the efficiency of a production line. Simulating real-world processes is useful to identify flaws, bottlenecks, and opportunities for improvement. An interesting real-life example of what could be considered the “analog” version of a production digital twin was the kitchen of the first McDonalds restaurant back in the 1940s. To achieve full efficiency in the assembly line, the kitchen area was drawn and tested on a tennis court repeatedly.
Digital models fed with real-time data from production lines are a great asset to increase speed and efficiency. They can even tell when is the best time to perform maintenance.
Performance digital twins can help companies collect and analyze large amounts of operational data. These insights are a great source of information for decision-makers while developing strategies and improving the production system.
Benefits of using digital twin technology
Proactive risk mitigation
Having the possibility of validating a product’s performance before it goes into production lets you identify flaws in its design. By doing so, you reduce the risk of going to market with a faulty product, jeopardizing your company’s reputation, and having to deal with massive recalls.
Maintenance with perfect timing
Once your digital model is deployed in a virtual scenario, you’ll know in advance when is the best time to perform the actual maintenance. This accuracy brings a lot to the table since you’ll prevent downtime as well as unexpected operational failures. It’s like having a preventive oracle by your side.
Remote monitoring access
With this technology, you’re capable of monitoring any system’s status without physically being there. All the diagnostics are performed remotely, in the digital environment, with the input from the sensors in the physical twin.
A good example of remote monitoring is NASA. According to John Vickers, manager of NASA’s National Center for Advanced Manufacturing, “Only when we get it to where it performs to our requirements do we physically manufacture it. We then want that physical build to tie back to its digital twin through sensors so that the digital twin contains all the information that we could have by inspecting the physical build.”
Setting up a digital model requires specialized data engineers as well as integration with AI and IoT. Despite its high initial costs, once developed, the insights and information provided have a positive impact on the ROI. This was the case of Challenge Advisory: one of their goals was to improve their client’s annual profits by 15-20 percent. After deploying the digital model, they were able to upgrade their client’s manufacturing process, increasing the profit margins by 41-54 percent.
Use cases of digital twin technology
Despite being in its nascent stage, this technology has become quite popular. Gartner notes that 75 percent of organizations implementing IoT today already use DT or plan to integrate it in the next 12 months. As stated before in this article, companies that are at the forefront of their industry, like NASA, have already taken advantage of this innovation. Now, let’s review some use cases in other sectors.
- Digital models in oil and gas
British Petroleum (BP) has been using this technology to control, simulate, and optimize their production. They’ve created APEX, a system in charge of monitoring and streamlining simulations, based on real-time data. After being tested in demanding locations, it proved to deliver operational efficiency. The company stated that APEX added over 30,000 barrels to their global production in 2017.
Managing wells and pipelines with intelligent automation solutions empowers enterprises to streamline processes, optimize operating costs, minimize nonproductive time, and eliminate downtime. TEAM International supports oil and gas leaders with smart IoT products globally and helps them create digital models of their infrastructures and check sensor readings from the fields anytime and anywhere.
- Digital models in aviation
Airservices Australia is an organization that provides security services to 11 percent of the world’s airspace. In early 2020 one of their initiatives consisted of exploring how a digital model could enhance their ability to manage air traffic. After a series of proofs of concept, they optimized flight routes and delivered a better user experience. In fact, they were given the 2020 ISG Paragon Award (APAC) for their digital twin prediction technology.
Check out their “Digital Twin of the Skies” video:
- Digital models in the tire industry
Bridgestone — one of the world’s most renowned tire manufacturers — has been using this technology over the last couple of years. On the one hand, they’ve implemented digital simulations to collect data and improve tire life and performance. On the other hand, they’ve developed performance digital twins to test their entire value chain to spot enhancement opportunities, increase efficiency, and reduce time-to-market.
- Digital models in the automotive industry
Siemens has developed a software solution used to design and simulate scenarios in the automotive industry. With Siemens NX CAD, engineers can design a car from scratch and perform tests in a fully virtual environment. This digitalization has been useful since it’s not necessary to have prototypes for every single test.
Despite being a young technology, it has the potential to disrupt many industries. The fact that it integrates with IoT, AI, and ML is an aspect that widens its opportunities. It’s, by all means, a technology that will pave the way for exciting developments soon. Simulations of body organs, people, and even cities are within the bounds of possibility. Keep in mind that this transformation is not immediate. Nevertheless, in the next 2-3 years, adoption and demand will increase significantly.