The promise of digital twins to streamline maintenance and asset utilization is becoming clear.
The proliferation of the Internet of Things devices is laying the groundwork for digital twins, which promise to spur efficiency and improved performance in products and operations. This article explores the use of digital twins in the current marketplace, how digital twins help predictive maintenance and improve product design, common digital twin challenges, and different digital twin platform types.
With roots in NASA research, digital twins promise to upgrade product development and operational performance in an array of contexts. Today, the digital twin is mainstream as technologies such as sensors, big data analytics, and cloud computing power a new generation of Internet of Things (IoT)-connected products, industrial assets, buildings, and even city infrastructure.
In this article we:
- Take a look at how digital twins are creating a blueprint for operational efficiency
- Explore how digital twins enable predictive maintenance
- Look at how digital twins can improve product design
- Discuss the key hurdles to embracing digital twin strategy
Imagine the value of knowing when and how much to throttle back plant floor machinery to stretch the life of an expensive asset. What about learning which features of a consumer appliance are underused or prone to failing to improve subsequent versions. Consider the cost efficiencies associated with understanding how wind turbines, oil rigs, or jet engines perform, allowing for modifications that improve asset uptime.
Once far-fetched scenarios, these are now real-world examples of how companies can enlist the digital twin concept. Introduced by NASA decades ago as part of its spacecraft monitoring mission, the digital twin has emerged in the mainstream as technologies such as sensors, big data analytics and cloud computing power a new generation of Internet of Things (IoT)-connected products, industrial assets, buildings, and even city infrastructure.
Companies have created 3D geometric models of physical assets for years using CAD (computer-aided design) and simulation software, but the digital twin concept brings design to another level, creating a holistic software representation of an entity’s physical state along with its behavior. IoT injects another information layer into the mix, capturing real-time data related to performance and the context of the physical entity after it’s built. By bridging the gap between physical and virtual worlds, a digital twin can drive operational efficiencies, reduce warranty and maintenance costs, improve production performance, and even create the basis for new as-a-service business models.
Given the range of possibilities, interest in digital twins is rising. According to a 2019 IoT implementation survey by Gartner, 13% of organizations have already used digital twins, while 62% are in the process or plan to use them within the next year. By 2022, Gartner predicts that more than two-thirds of companies implementing IoT will have at least one digital twin in production, and early pioneers have already moved on to more sophisticated digital twin pilots. A total of 61% of companies on the development path have integrated at least one digital twin of equipment like pumps or generators to get a more complete picture of their operating environments.
“Digital twins are inextricably linked to IoT,” said Ian Hughes, senior analyst, Internet of Things, at 451 Research. “If you simply model something in CAD [computer-aided design] and build it physically, you have no relationship to model and no idea about what’s going on with the thing you built. A digital twin keeps all the contextual information together about a pump, an oil refinery, or a city.”
Digital Twins Creating Blueprint for Operational Efficiency
The promise of digital twins to streamline maintenance and asset utilization is becoming clear.
The proliferation of the Internet of Things devices is laying the groundwork for digital twins, which promise to spur efficiency and improved performance in products and operations. In this report, we explore the significance of digital twins on the factory floor, their role in improving product design and product quality, as well as challenges still facing the technology.
How Digital Twins Help Predictive Maintenance
The fastest-growing use scenario for digital twins involves predictive maintenance and performance optimization, on the factory floor, or in the field. Industrial assets like sensored plant floor machinery, wind turbines, and oil rigs continuously provide data on operating conditions and patterns that feed a digital twin, uncovering insights to prevent downtime, boost efficiencies, and reduce costs. An LNS Research study found that 30% of digital twin projects aim to reduce manufacturing costs, while 28% intend to curb unplanned downtime.
Consider Elekta, a manufacturer of equipment used to treat people with cancer and brain disorders, which is using digital twins to garner insights into product performance and service requirements. The effort allows the company to remotely fix more than 30% of machine issues while helping customers avoid unplanned downtime. Phoenix Contact Electronics uses digital twins to predict the life span of configurable safety relays. The relays terminate power when problems occur to prevent injuries and damage to factory automation systems. The ability to predetermine the remaining life cycle for each relay allows engineers to repair equipment before it fails and schedule maintenance when machinery is not in service, avoiding costly factory floor downtime.
Traditionally, factory equipment such as relays rely on scheduled maintenance. The ability to predict wear based on digital twin data enables optimization of such maintenance while enabling coordination of planned service outages. Organizations using the technique can create operational efficiency while reducing maintenance costs over time.
How Digital Twins Improve Product Design
Digital twins can also play a role in improving product quality, a goal 34% of respondents in that LNS survey cited. Specifically, the ability to test new design ideas (16%) and develop product enhancements (14%) were among the benefits that can boost product development and engineering workflows. Providing engineers access to real-world data about how a product performs in the field, for example, helps them better understand areas that are over- or under-engineered, allowing them to address the issues and optimize the design in subsequent iterations. Because a digital twin provides a holistic view of all relevant information related to an asset accessible, it can facilitate change management on an enterprise-scale — a scenario that’s more difficult using traditional product lifecycle management (PLM) systems since they are tuned to the needs of engineering.
Part of the value of a digital twin is its ability to provide insights throughout an organization. Engineers, designers, and field technicians have an unprecedented view of how products function in the real world. A digital twin also facilitates a closed-loop quality process while facilitating more effective design for manufacturability and service product development workflows.
At the same time, digital twin technologies have found a home on a much grander scale to optimize traffic patterns in smart cities or to maximize energy output on resources like wind farms and utilities as part of an intelligent grid infrastructure. National Grid and partners, for instance, are piloting a digital twin of the electric grid that maps power flow, voltage, and infrastructure from the substation to homes to improve efficiency and reliability. Virtual Singapore is one of the first examples of using digital twins on a city scale — the $73 million effort aims to create a digital twin of the city. The project will help test smart services. It will analyze pedestrian patterns to improve parks and evacuation routes or help telecommunications companies optimize wireless network coverage.
Digital Twin Challenges
While digital twins have plenty of momentum, significant challenges remain. Technology providers haven’t adopted a standard definition of a digital twin, and most describe the set of capabilities in slightly different ways.
There’s also the issue of data sovereignty — specifically, how to persuade customers and partners to share data collected from assets with manufacturers so they can improve the digital twin to the point it can support preventative maintenance services and optimize future product designs.
What’s also required is the standardization of data management processes and clarity about the available tools to create and manage digital twins [see “Digital Twin Tool Sets Still a Workin- Progress”].
While implementing digital twins doesn’t require a whole new toolbox, it does require a new approach for managing and using data, according to Rafael Go, senior research analyst at Navigant Research.
Apart from technology, Go said there are also the usual organizational and cultural hurdles.
“There is still a lot of inertia that prevents manufacturing companies from doing things in different ways,” Go said. “Building organizational comfort and buy-in with the digital twin concept is important.”
Digital Twin Tool Sets Are Still a Work in Progress
One of the key hurdles to embracing a digital twin strategy is figuring out the right class of tools and technologies that can be tapped to develop a fully-featured model.
Just as there is no singular definition of a digital twin, there is no one off-the-shelf category of tools and technologies that support the design of a digital twin. 3D modeling tools and computer-aided engineering (CAE) software play a role in creating specific aspects of a digital twin while emerging Internet of Things (IoT) platforms are another instrumental piece of the equation.
Part of the challenge is that a digital twin is still more of a marketing concept than an actual category of product. “The industry doesn’t yet have something that says this is a digital twin because it has these forms of data,” said Ian Hughes, senior analyst, Internet of Things, at 451 Research. “There isn’t a single, standardized format . . . and there aren’t necessarily products there yet to support it.” Hughes said rather than a specific toolset, organizations should focus on standardizing data management practices and tap into data schemas that could vary from industry to industry and might not specifically be classified in the digital twin category.
Nevertheless, platforms, providers, and tools are at the epicenter of early digital twin deployments. Here is a breakdown of the platform types:
CAD and PLM offerings. Several traditional vendors specializing in computer-aided design and product lifecycle management offer entire portfolios of software applications designed to help organizations build virtual 3D models of assets and their corresponding behaviors, whether it’s a new product design or a piece of industrial machinery. Such offerings increasingly support virtual and augmented reality functionality to provide novel ways to visualize the bridging of the physical and virtual worlds. These offerings also tend to support the creation of a virtual representation of a product and processes on a manufacturing floor.
IIoT platforms with digital twin support. As the industrial IoT market matures, digital twins are taking on a more prominent role in offerings from several platform vendors. Already capable of aggregating and analyzing data from sensors equipment, such platforms can support a digital twin strategy. Other than the traditional 3D modeling vendors, big enterprise players have IoT platforms and capabilities for building digital twins.
When evaluating the digital twin vendor landscape, it is important to gauge the potential accuracy of the models intended to replicate equipment and physical processes. In addition to vendor maturity relating to data integration, another consideration is the accuracy of their physics simulation software and their capacity to update physics models in digital twins with data streaming from IoT devices. Such capabilities can be used to make more informed decisions that affect product performance, reduce the risk of unplanned downtime, and serve as a guide for future product development efforts.