What is digital twin technology? It’s a virtual software model designed to produce virtual replicas of a product, service, or process.
Many ERP vendors talk about digital twins in their sales pitches, assuming you know exactly what they’re talking about. Truth be told, they might as well be speaking another language.
We’re here to explain digital twins – and not in the form of a sales pitch. Read on to understand how the technology works and what you need to know to make a purchase decision.
2021 ERP Report
This report summarizes our independent research into organizations' selection and implementation decisions and their project results.
What is Digital Twin Technology and How Does it Work?
It might sound like something out of the Jetsons, but digital twin technology is real and here to stay. These complex models virtually represent a range of both tangible and intangible items, including:
- Physical objects
At its core, a digital twin is founded on geographic information systems (GIS) technology. An advanced framework for capturing, managing, and analyzing data, GIS uses positions on the Earth’s surface to show items on a map.
Through GIS, analysts can create digital twins that replicate both the natural and built world. Today, this technology has expanded to include even large-scale items, such as:
As you might imagine, digital twins are useful across a range of industries, combining different types of data into a single view that managers can access at any point in a project’s lifecycle.
Where do digital twins get all this data? They can be integrated with a variety of business applications, including:
- Manufacturing execution systems
- ERP software
- Supply-chain-specific tools
This is why ERP vendors are always talking about digital twins. It’s clear that they have the potential to add value to organizations’ ERP implementations.
The History of Digital Twins
One of the earliest concepts for digital twin technology appeared back in 2002, attributed to Dr. Michael Grieves, who developed the concept in his executive product lifecycle management (PLM) courses. As a doctoral student in the 90s, Grieves first devised the strategy, describing it as moving physical work into virtual environments.
Then dubbed the Mirrored Space Model (MSM), it was used in theory, but wasn’t applied in practice until around 2010, when NASA began using twinning to improve the simulation of physical space models.
Grieves’ colleague, NASA Principal Technologist John Vickers, first used the phrase “digital twin” in a roadmap report prepared for the agency.
To truly trace the roots of digital twins, you’ll need to go back more than 50 years. NASA has actually been using some form of digital twin technology since the 1960s, when the organization was creating physically-duplicated ground-model systems to replicate its systems orbiting in space.
While those early systems were sophisticated in their own right, modern digital twins are even more so. Thanks to developments in IoT technology, the virtual renderings provided by these newer models are incredibly precise.
They’re also available to a wider range of businesses and capable of supporting a greater number of applications. Until recently, the data storage requirements necessary to support digital twins limited their use to only those corporations that had the resources required to gather, store, and analyze the intel. As such, it was reserved mostly for those in the digital technology sphere.
Now, it’s more cost-effective and accessible than before, thanks to developments across certain fields, including:
- Machine learning
- Cloud connectivity
- Internet of Things (IoT)
- Data interpretation software
Who Uses Digital Twins?
Today, companies in a variety of sectors derive value from digital twin technology, especially companies in the construction, automotive, and manufacturing industries.
Across industries, real-time models allow for easier data tracing throughout the product lifecycle. For example, in manufacturing, digital twins are used to facilitate product development and design customization. They can also help improve shop floor performance and aid predictive maintenance activities.
Likewise, automotive leaders can utilize these models to advance design and development efforts. Most recently, this industry has used digital twins to simulate and better understand the technology behind self-driving cars.
On the construction side, digital twins can help firms gain visibility into real-time building performance. With the data provided, they can make necessary adjustments to optimize efficiency, while laying the groundwork for future improvements.
What is a Digital Twin in Manufacturing?
For years, manufacturers have relied on 3D renderings and computer-assisted design (CAD) drawings to develop prototypes, manage assets, and predict outcomes.
Digital twins improve on these processes, enabling improved accuracy and greater control. These models rely on IoT sensors, which transmit data from an object directly to its corresponding twin.
How does it work? First, a physical asset is built. It will contain one or more sensors capable of collecting ERP real-time data and transmitting operational status. This data is then sent over the cloud, where it’s analyzed and replicated in the twin.
The result is a process that’s quicker, safer, and potentially more cost-effective than ever before.
Take recent developments at NASA, for instance. Currently, the agency is making digital twins of rocket parts that were first built using additive manufacturing, also known as 3D printing. Once the twins are up and running, they can simplify and expedite product testing and qualification, making it easier and more economical than running real-world tests and experiments.
Digital Twins vs Simulations
It’s easy to confuse digital twins with simulations, and the two do share some similarities. However, there are a few key differences to note.
First, it’s important to understand that digital twins utilize more than one type of technology. In fact, they can be comprised of a range of technologies, including 3D simulations, cloud computing, IoT, and more.
Second, digital twins are used to facilitate the entire development lifecycle, all the way from initial design to final deployment. On the other hand, simulations are primarily only used for the design phase, though they can also be helpful in offline optimizations.
The most notable difference? Simulations can help users predict what might happen in the real world, but digital twins predict this with more accuracy because they know what is happening in the present. They offer much more than a “what-if” scenario and can actually report on what happens when assets interact with products, people, and processes in real time.
Types of Digital Twins
There are four main types of digital twinning technologies. These include:
1. Parts Twinning
Part twins are digital representations of individual components. These twins help engineers better understand the characteristics of a part on a physical, electrical, and mechanical level.
2. Product Twinning
Product twinning allows engineers to see how different parts interact with each other (and the environment) in real time. With this knowledge, they can optimize each individual part to improve overall performance and minimize metrics such as:
- Mean time between failures (MTBF)
- Mean time to repair (MTTR)
3. System Twinning
Systems are comprised of various types of products, all working together in unison. With system twinning, engineers can see how these products work together to achieve a given result.
This visibility can unlock new levels of effectiveness and efficiency and shows great potential in widespread systems, such as:
- Energy systems
- Communication systems
- Industrial manufacturing systems
- Traffic control systems
4. Process Twinning
This is the most complex level of digital twinning because it isn’t limited to physical products. Rather, this could mean twinning business processes or even workflows.
In manufacturing, process twinning can help users understand how making changes to certain inputs might affect outputs. They can gain this insight without altering or upsetting current workflows, which could affect performance and productivity.
Benefits of Digital Twins
The real-time simulation provided by digital twins allows for improved data capture and integration. Other benefits available through this technology include:
- Cost savings
- Increased safety
- Improved real-time visualization
- More accurate, advanced data analysis
- Predictive analytics
- Easier collaboration and information sharing
Digital twinning can be useful for COVID-19 motivated use cases, as well. Last October, Gartner predicted that by 2023, one-third of mid-to-large-size companies that implemented IoT will have implemented at least one digital twin for a reason related to COVID-19.
Is Digital Twin Technology for You?
As the IT industry discovers new answers to “What is digital twin technology?”, future-focused organizations are urged to pay close attention.
If you’re considering implementing digital twin technology along with your new ERP system, it’s important to consider its use cases and how it could benefit your business. While it does have plenty of benefits to offer, make sure they’re truly aligned with your organizational goals.