A digital twin is a virtual model of a real-world object or system, designed to accurately reflect the attributes and life cycle of the subject.
Digital twin technology works by outfitting a physical object with IoT sensors through its development, production and operation. These sensors are designed to produce reliable data about the subject and its functionality. This data can then be communicated in real-time to a separate processing tool and used to create a highly accurate digital model.
Examples of digital twin technology include:
- Traffic engineering
Digital twins can be created for entire roadway systems. By mapping roads and analysing traffic data in real time, planners can gain a more comprehensive view of how traffic moves through a city, where it encounters bottlenecks, and what kinds of adjustments could be made to relieve traffic congestion. - Telecommunications
Telecommunications companies may apply digital twin solutions to map and optimise their networks based on the movements of network users and the applications they are currently running. - Real estate
Digital twins are widely used by real estate companies to create virtual representations of building ecosystems. This allows decision makers to see the real-time properties of their buildings, including management systems data, security system data, HVAC system data, interior and exterior floor plans, tenant interaction data and more. - Energy production
As energy needs increase, the energy sector is using digital twin technology to improve the efficiency of power-generating wind turbines. Attached sensors feed real-time performance data to the turbines' digital twins, making it possible for engineers to make improvements and adjustments where needed to optimise energy output. - Healthcare
Digital twins can be created for living systems as well as inanimate ones. Wearable sensors give healthcare providers deeper insights into their patients' health and allow them to simulate treatments and procedures before applying them in real life.
Of course, digital twin technology is not limited to these examples; any system represented in a virtual environment for data collection is working alongside its own digital twin.
Because digital twins allow organisations to collect real-time, real-world data, they carry with them many distinct advantages. These include:
R&D benefits from using digital twins. Rather than having to wait for prototypes to be completed before testing performance, companies can apply accurate digital twin data to achieve actionable insights and make vital changes and improvements before the product enters production.
Manufacturing processes are well suited to digital twin solutions. Companies can track production systems and investigate the results of possible efficiency-improving adjustments before they apply those changes in the real world. Digital twins help ensure that every refinement that is put in place is backed by reliable evidence and that the change being introduced will help improve the system.
Whether referring to a single component or an entire set of processes, digital twins bring reliable data insights to areas that might otherwise suffer from less-concrete decision-making processes. This makes it possible to trim away inefficiencies and improve performance.
Real-time IoT sensor data and advanced mathematical modelling give decision-makers all of the information that they need to identify trends and get in front of issues before they occur. AI and machine learning technologies further improve the predictive recommendations made possible through a digital twin.
Digital twins can significantly reduce the time needed to bring an idea from concept to reality. This means reduced development work and a faster debut—not only for the products themselves but also for other assets and processes relevant to production and deployment.
As previously addressed, digital twins range in size from small and specific to large and inclusive. Various scientific disciplines have even applied digital twin solutions to create behaviour models of microscopic subjects (such as human cells) and macroscopic systems (like the large-scale structure of the visible universe). That said, businesses tend to categorise their digital twin efforts using four levels of magnification:
- Components
Single-functioning components are the most basic units of digital twins. Components may be further broken into parts for a more granular approach. - Assets
When the digital twin consists of more than one component, it is classified as an asset. Asset twins recover performance data from the individual components and the interaction between them. - Systems
System twins are made up of two or more assets operating in tandem to create a functioning system. - Processes
Finally, process twins focus on the interaction of multiple systems to create full processes.
Often, the terms digital twin and simulation are used interchangeably. And in many ways, the two are remarkably similar. Digital twins and simulations use real-world data to replicate objects in a digital environment. But where they differ is in the scope of the digital model
Most simulations are designed to study only single, specific processes. Additionally, they tend to rely on deferred data which retains the value it was assigned at the time it was collected.
Digital twins can do much more. A digital twin creates an actual, working digital environment in which organisations can run a range of simulations to test and study many different processes. This is all informed by real-time data that provides a clear and up-to-date picture of what is happening right now. This data flows into the digital twin to ensure real-time accuracy and any insights created by the processing system can also be sent back to optimise the subject.
Simply put, a simulation can offer a glimpse into the impact of changes made within a system. A digital twin takes things further, by providing detailed insights into what is currently happening within multiple systems or processes, and what is likely to happen in the future depending on which variables change.
When are digital twins valuable? The broad answer is "whenever an organisation wants real-time insights into components, assets, systems or processes". More specifically, however, digital twins are most useful when they are applied at specific times:
Before a physical product is created or a system is established, a digital twin prototype (DTP) can be created representing the data relevant to the designs, analyses and processes that the finished product will need to incorporate. Organisations use DTPs to test and modify parameters and operating conditions to better understand products and systems in development, and to make beneficial changes to their designs before they enter full production
After a product is released or a specific system or process gets deployed, the digital twin is still a vital part of data collection and analysis. Digital twin instances (DTIs) give organisations accurate information about the object or entity throughout its lifecycle, and digital twin aggregates (DTAs) bring together groups of DTIs to determine capabilities in various conditions, test new operating parameters, and offer a more comprehensive picture moving forward.
Clean, straightforward simulations based on estimates and deferred data have their benefits, but products and businesses do not exist in purely virtual worlds. To understand how they really operate and where they might be improved, organisations need comprehensive real-world information presented with up-to-the-second consistency.
This greater emphasis on real-world data has created unique and valuable solutions for groups across many fields and industries. These solutions include:
- Creation of multiple digital variations of products for easier customisation
- Efficiency improvements in racing cars
- Fatigue testing and corrosion resistance for wind turbines
- Easy sharing of digital twin data across disciplines
- Improvements to product manufacturing and business processes
- Process optimisation to track manufacturing performance indicators
- Product lifecycle extension
- Reduced need for physical prototypes in car manufacturing
- Improved customer experience through product, performance and distribution data
- Workflow, staffing and procedure improvements for hospitals
- Manufacturing: Optimising the manufacturing process, reducing downtime and improving product quality are all made possible through digital twins. For example, digital twins can simulate the behaviour of a production line to identify bottlenecks and optimise the workflow.
- Healthcare: Digital twins are used to monitor machines like MRI or vaccine/medication shipping. To identify potential problems or more accurately gauge performance. This technology may also be used to create personalised simulations of organs or systems within the body, helping doctors and researchers better understand disease progression and develop targeted treatments.
- Construction: Through simulation and optimisation, digital twins can reduce costs, improve safety and minimise waste.
- Energy: Digital twins allow for optimisation of energy production and distribution systems. For example, digital twins can simulate the behaviour of a power plant to identify inefficiencies and improve performance.
- Transportation: Digital twins can be leveraged to optimise traffic flow and logistics. For example, digital twins can simulate the behaviour of a shipping port to identify bottlenecks and optimise the workflow.
- Supply chain: Companies can gain insights into how the system is functioning and identify potential areas for improvement, making it possible to optimise inventory management, reduce waste and improve quality control. For example, a digital twin of a manufacturing plant could be used to simulate different scenarios and identify bottlenecks in the production process.
- Retail: Digital twin technology can be leveraged in retail environments to produce virtual replicas of physical stores, allowing customers to view and purchase products in a virtual setting while also giving employees the power to test out different store layouts and product placements before making changes.
This list includes only a fraction of the possible use cases for digital twin technology. Because digital twins allow organisations to create, analyse, and adjust the parameters of objects and processes in a fully digital environment, even the most complex challenges can be rendered and resolved faster, easier and at lower cost than has been traditionally possible.
Data has always played a key role in business decision-making. As digital twin solutions become widespread, they are addressing the challenge of capturing and analysing real-world data. Unfortunately, although the technology is already available, finding the right tools and resources to create digital twins for your business can be a difficult prospect.
ServiceNow is bringing IoT and workflow together, integrating the award winning Now Platform® with multiple IoT partner tools. Operating together, these advanced solutions provide you with the accurate, real-time data collection, analysis and ingestion that you need to monitor your critical assets and infrastructure. Take your IoT data further by incorporating it into your workflows with ServiceNow Customer Service Management (CSM) and Field Service Management (FSM). And through it all, enjoy the award-winning service and support that has made ServiceNow the leader in enterprise IT management.
Expand your data analysis capabilities into the real world; click here to demo ServiceNow today!