A digital twin can be a helpful tool to help companies test and validate concepts ahead of their launch or to analyze the impact of potential changes to existing entities. These virtual models are designed to replicate wide ranging physical objects as well as entire cities or regions. Engineers and planners use digital twins to identify potential failures ahead of time, saving valuable resources including time and money.
What is a digital twin?
A digital twin is a virtual model designed to accurately represent a physical object. Essentially, digital twins are rich virtual environments that can be used for simulations, testing and monitoring without making changes to the actual physical object.
These objects use data obtained from the physical object being modeled. As an example, a digital twin of a jet engine could be created by analyzing data obtained from sensors placed on different parts of the engine and from existing related knowledge. Information about the engine’s performance, energy consumption, the impacts of weather conditions and other factors could help to create an accurate digital twin for use in simulations.
Digital twins are not the same as simulations. Both use digital modeling to study the processes of physical entities, but digital twins offer a much deeper potential for analysis. Essentially, digital twins can operate at scale, modeling multiple simulations at a time, whereas a simulation usually focuses on a single process. Digital twins can also use real-time data, another significant improvement over a single simulation model. They are effectively a more modern iteration of simulation design work.
Types of digital twins
The concept of a digital twin is relatively broad and encompasses four specialized types across different industries: component twins, asset twins, process twins and system twins.
Component digital twins represent individual parts of systems or products—for example, a single hinge or gear. This type of digital twin technology can model key components more likely to be subject to stress or factors like extreme heat. By digitally modeling integral parts at risk of failure, analysts can examine how they might improve their integrity by subjecting them to open-ended simulations. Being able to estimate the upper limits of parts (and figuring out how to improve them) can lead to significant improvements in terms of how long products might last and to safety improvements for operators.
Asset twins are sometimes called product twins. In a way, asset twins are the flip side of component twins, as they are virtual representations of an entire physical entity rather than its individual parts. The main function of an asset twin is to help engineers and designers develop an understanding of how individual parts work together within a single product. For example, an asset twin modeled on a car or truck could help automotive engineers pinpoint potential failure points based on mileage, weather conditions and other factors.
System twins are sometimes called unit twins. These virtual representations replicate entire systems of products working together. These twins build on the asset twin approach, which looks at how individual parts work together within an entity by modeling individual entities within larger systems. For example, a system twin might focus on several identical machines linked together on a factory floor. Factory design engineers could study a system twin to determine the best workflow to improve individual machine integrity, improve productivity and efficiency and identify problems that could impact the entire floor.
Process twins zoom out even further than system twins. These digital twins look at how systems work together. For example, while system twins might study a single manufacturing line or a single factory floor, process twins can also pull in systems like staffing, multiple factory locations, the supply chain and more.
Digital twin benefits
What are the benefits of digital twins? The overarching benefit of this technology is the ability to model potential scenarios in a way that would be much more costly and difficult with physical objects.
Four specific benefits of digital twins include:
- Performance and efficiency improvements
- Resource savings (including financial resources)
- User satisfaction as a result of improved product performance and integrity
- Remote collaboration possibilities (analysts from across the globe can collaborate with virtual modeling in shared locations)
Digital twin applications
While digital twin technology has the potential to benefit a virtually endless list of applications, several industries stand out among the most common digital twin adopters. They include the following:
Each of these fields employs digital twin technology to model the potential impact of both expected and unexpected occurrences on performance, though the preferable outcomes may differ by application. Governments can use digital twins to determine where to invest in the local economy to spur growth, for example, while telecommunications firms might use digital twins to make decisions about where to add hubs. Any industry that moves goods through the supply chain can make good use of digital twins to estimate the impact of environmental factors and consumer behavior.
Getting started with digital twin technology
McKinsey suggests a three-step process for developing a digital twin:
- Create a blueprint—essentially, develop a framework based on the desired end result of the digital twin and determine the scope and scale needed.
- Build an initial digital twin—for many companies and individuals, this will mean connecting with an outside firm or working closely with a specialized IT unit.
- Boost the digital twin’s capabilities—once developed, work to strengthen the digital twin’s capabilities by adding more sensors and other information-gathering processes.
Bottom line: Digital twins
Digital twins can offer deep insight for analysts, developers, engineers and other professionals across a wide range of industries. These virtual representations of physical entities (including physical objects and larger systems like cities and regions) empower these professionals to model scenarios and estimate their impact on performance, longevity, efficiency and other outcomes. Building a digital twin requires specialized computer skills and dedicated company resources, but the benefits over field testing or single-simulation approaches are tremendous.