How Digital Twins Are Driving the Future of Business

Soap will never be the same. With a 2019 pilot at a factory in Brazil, CPG goods multinational company Unilever began its transition to digital twinning its production with the goal of connecting 300 plants around the world that manufacture soap and other consumer staples. And it’s easy to see why. Implementing a digital replica of operations at that one factory in Brazil to monitor performance and test process improvements elevated productivity while using less energy, saving the company US$2.8 million, the company told the Wall Street Journal. Unilever is really cleaning up.

Other companies are providing digital twin examples and success stories as well.  Tire maker Bridgestone is developing a commercial smart tire that will use a digital twin to predict tires’ longevity and need for replacement.

Digital twins are a virtual representation of a physical object. It could be anything – as complex as a car or a manufacturing production line, or as simple as a piece of furniture. The digital twin emulates all the parts of the object (or set of connected objects) to create a virtual proxy. A car’s digital twin would model its shape, tires, seats, engine, transmission, everything. Companies use a digital twin to design a 3D model of the original, enabling teams to analyze the performance of the object under different conditions. Successfully deployed, digital twins can save serious money, improve product designs, and elevate efficiency and productivity.

Up until now, heavy industries that work with large assets and have adopted product lifecycle management systems – those including oil and gas extraction, aerospace, and automotive – have been leaders in digital twin adoption. But that has been changing in recent years because the components that make digital twins possible and useful are now much less expensive, easier to use, and easier to access.

Over the long run, digital twins will form their own networks, which experts call a “digital thread.” If a digital twin enables us to create a digital representation of a piece of equipment or facility, then a digital thread is the continuous, connected stream of information provided by an intelligent asset throughout its life cycle, from design to decommission.

Digital twins are a virtual representation of a physical object. It could be anything – as complex as a car, or as simple as a piece of furniture.

Implemented effectively, digital twins can serve as strategic catalysts. They can provide visibility into an organization’s processes and ways to improve them and, in turn, strengthen customer experience and relationships. Digital twins provide a sandbox where innovations can be tested and refined before they’re launched into the real world. They afford businesses a cost- and time-efficient way to design smarter products and assets while capturing more information about them. So too, they enable companies to make products better, faster, and safer and generate new revenue opportunities. These include service offerings that create as-a-service business models, which remove the burden of large capital outlays and lifetime maintenance from the customer and keep them connected with the service provider.

The making of a digital twin: Access sensors, collect data

Digital twins should not be confused with a digital model or simulation, which has the digital part but not the existing physical object. The digital twin mirrors its physical doppelgänger in every way, including when the physical object changes over time. It does this by employing a constant, unbroken, and as-close-to-real-time-as-possible data stream, so that the digital object changes over time, in parallel with the physical object.

Craft beer drinkers get a taste of Industry 4.0 intelligence with digital twins

The concept can be traced back to product lifecycle management expert Michael Grieves in 2002. For product manufacturers, the earliest adopters of digital twins, that concept meant more efficient production, less waste, and better predictive maintenance.

To create a digital twin, you need to record the base information about the object and capture how it’s performing and being used. Sensors do that, whether it’s a machine in a factory or a tire on the road. The development of the Industrial Internet of Things (IIoT) has pushed digital twins even further, as IIoT-enabled machines and components collect and feed data, in real time, meaning they are essentially self-reporting their own conditions.

To create a digital twin, you need to record the base information about the object and capture how it’s performing and being used.

As the cost and complexity of digital twins has fallen, their adoption has spread beyond manufacturing to many different types of businesses. “The cost of sensors continues to decline, the amount of data collected is exponentially growing, and storing is affordable in various cloud services. Manufacturing companies can tap increasing opportunities to gain insights. The interest is in getting all this data off the factory floor and analyzed to help understand not only what’s going on today, but also forecast what plant operators can do in the future to improve productivity and quality,” says C. V. Ramachandran, digital transformation and operations improvement expert at PA Consulting.

Two young men working in technology lab, developing digital twins technology

Opening doors to operational improvements

The Digital Twin program, a collaboration between six Dutch universities, an industrial consortium, and the Dutch government, aims to find ways to use digital twins to respond to companies’ biggest challenges.

Bayu Jayawardhana, project leader and professor at the University of Groningen, says the key objective of the program is for a digital twin to be used as a tool for decision-making, not just on a machine but also at an operational level. For example, maintenance of assets is expensive because of labor and downtime; predictive maintenance, with data provided by a digital twin, is more efficient. “To do that means you need to have something to allow you to predict the behavior of your assets in the future,” he says.

One of the Dutch program’s industry partners is Tata Steel. The company invented a new hot iron steelmaking process it calls HIsarna, which promises to reduce carbon emissions and energy usage. But to get it ready for commercialization, they needed to make the process more reliable. Because the process requires very complex technology, there were regular interruptions when the prototype reactor was working. Experts are now creating a digital twin to simulate the process, using data and models to figure out where the weak points are and how to solve them – a task that would be incredibly costly and time-consuming to do using the actual physical plant.

Digital twins enable new business models, such as when an equipment manufacturer offers a lease on a digitally enabled component.

Another company the program works with is Philips. The company, which has made electric razors for decades, wanted to improve the time-to-market and design process by using data from a line of smart shavers that users can customize based on their skin sensitivity. Sensors embedded in the shavers collect information on how they’re being used, and that information is fed into a digital twin to advise the consumer of a specific shaver.

Digital twins are also enabling new business models. For example, an equipment manufacturer may offer a lease on a digitally enabled component. Through such a “product-as-a-service” model, customers reap the advantages because there’s a much smaller initial financial outlay and much less downtime. Although you won’t know what’s going on inside a machine or component until it breaks or stops functioning correctly, a digital twin using IIoT means faster servicing and attention before something bad happens, thanks to predictive analytics. One company that is already offering this is Germany’s Kaeser Kompressoren. Thanks to a digital twin, its “smart air” as-a-service product uses predictive analytics to assess operating health and perform maintenance before there’s a problem.

Man wearing safety goggles using transparent touchscreen device

Digital twins for factory workers

Digital twins can even be used for people. Indeed, at Texas State’s Ingram School of Engineering, Jesus Jimenez is directing research into digital twinning workers in a factory.

Capturing biometric data with sensors can help keep individuals and society safe and healthy

The idea, says Jimenez, an industrial engineering professor, is to look at not only productivity, but also research and ergonomics of, for example, a machine operator or forklift driver. They use sensors and motion capture systems with cameras that capture the way a study subject moves while performing different types of actions commonly found in manufacturing, like lifting and reaching. Data on the physiology of the operator – heart rate, calories burned, respiration rate – are collected and put into the twin to help predict things like fatigue.

“You can see not only how to train your operators better, you can also look at things that are providing feedback on when the human will be ready for a transition to a different job or a different schedule,” says Jimenez.

Jimenez’s team is expanding its research to include cognitive functions to help better understand how humans in these contexts make decisions and errors, also factoring in cognitive decline. Here, too, sensors provide the data for workers’ digital twins. “There are lots of opportunities, like creating training tools for the operator,” Jimenez says. “We could do something like capturing the highly skilled operators and save a copy of that person.” That way, when someone retires, for example, all those years of knowledge and experience aren’t lost. Their past actions and successful approaches to solving problems could be passed on to other employees.

Two people using digital screen to look at data

Future possibilities for digital twins

In addition to researchers looking at operators on the factory floor, experts point to other possibilities for digital twins:

These examples illustrate that researchers are still exploring future possibilities for digital twins. Ramachandran notes that the possibilities in a case like the supply chain example depend on collecting enough data – and the right data, including from third parties and business partners – to support strategic decision-making. That will take work.

In the meantime, like a good soaping of the factory floor at the end of another productive day, digital twins are poised to provide clean views of a manufacturer’s processes and help uncover better ways of doing business.