Boost Your Business With Digital Twin Technology

It’s a simple concept with powerful potential. A digital twin is a digital representation of a physical object, process, or even person presented in a digital version. Digital twins can help an organization simulate real situations and outcomes, enabling better decision making.

Digital twins let adopters create a complete digital footprint of their subjects, from design and development through the end of their lifecycles. The technology helps organizations understand not only the subject as designed, but also the system that built it and how the product will used in the field, says Tim Gaus, smart manufacturing leader and principal at Deloitte Consulting in an email interview. “With the creation of a digital twin, companies may realize significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue.”

A digital twin allows adopters to experiment and find new approaches that can improve production quality, reduce emissions, enhance worker safety, improve maintenance, mitigate flooding, and more, explains Jason Mann, vice president of Internet of Things at AI and analytics provider SAS, via email. “Organizations can then use the knowledge gained from the digital twin to make better, faster decisions, improve citizen and customer experiences, and optimize operations.”

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Multiple Benefits

Digital twins can help adopters solve physical issues faster by detecting them sooner, predict outcomes with a high degree of accuracy, design and build better products, and, ultimately, help better serve customers, Gaus says. “With this type of smart architecture design, companies may realize value and benefits iteratively and faster than ever before.”

Digital twins allow businesses to answer questions that can directly impact strategic and operational decisions. “Organizations can move from answering simple questions about asset performance to understanding how these assets -- machines, assembly lines, supply chains -- will operate in the future, and what actions the business can take to meet performance and uptime goals,” Mann explains.

Manufacturers are the businesses most likely to gain value from digital twin technology. “Manufacturers look to understand the causes of downtime, model scenarios to improve efficiency, and reduce waste,” says Devin Yaung, senior vice president, group enterprise, IoT products and services, at technology and business solutions provider NTT, in an email interview.

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Digital twins of individual machines permit instant views into maintenance issues and potential failures. “The growth of connected IoT sensors and devices has allowed all industries to gain insights into assets,” Yaung says. “Because of this explosion of connectivity, we are seeing large adoption not only in manufacturing but also in utilities, mining, hospitals, ports, airports, logistics/transportation, agriculture, and many other industries.”

The Basics

There are several basic types of digital twin technology, Gaus says. These include component twins (digital models of a single part), asset twins (models showing how two or more parts work together), system twins (models of a system with multiple components, such as a production line), process twins (the digital model of an entire facility, such as a smart factory), and performance twins (taking an end-to-end twin and then allowing simulation to provide insight into a supply chain’s future performance).

The first step when planning a digital twin deployment is to determine the likely ROI by identifying business objectives and needs, Yaung says. The next step is creating a comprehensive strategy, “or else your business may find itself with a collection of point solutions that don’t fully integrate,” he warns. “The strategy must not only include the technology roadmap but should consider the new operating model and change management -- how will the organization change and act upon the new insights.” Security and privacy must also be at the core of the roadmap, Yaung adds.

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A digital twin strategy must be well-planned and have all of its required elements in place before it can go live, Mann says. “These include a robust, scalable data architecture that manages a diverse set of real-time and enterprise data; advanced analytics that process the data and uncover opportunities to optimize any anomalies; a way to use the analytics to test scenarios; and a method to efficiently transfer analytical findings to the right people within the organization.”

There are two main roadblocks organizations face when adopting digital twin technologies. The first is the upfront cost. While digital twin technology can save money over time, the initial adoption is often a complex and costly process depending on what capabilities your organization is currently working with, Gaus says. “This is why prioritization is important -- incorporating digital twins into the areas that will lead to the most value.”

Proceed with Caution

As with any data-driven technology, digital twin output quality is directly related to the accuracy of the information received. “This is especially important when dealing with high-fidelity sensor data.” Mann says. “Quality IoT and streaming data are essential to the performance of digital twins and their data veracity,” he states. “Having the ability to perform analytics on high-velocity data provides the accuracy needed to make better, faster decisions.”

“All digital twin data must be trusted and actionable or else it just becomes a lot of noise,” Yaung says.