What is a digital twin?
What is a digital twin?
With business constantly growing and developing, we are always looking for effective and easy ways to test our products and services. Many companies have begun to use digital twins to help with this. But what is a digital twin? And how can you apply it to your business?
What is meant by digital twins?
The use of a digital twin is a model that mirrors a real-life physical object, organisation, person or process. So it is simply an assistance system for production facilities that use integrated simulation models to find solutions quickly when problems arise.
These simulations of production facilities help you to determine the ideal operational workflow for your product. As an integrated part of a digital twin, these simulations can draw on current data flows and swiftly find clear options to help the system operators reach a decision when problems arise.
Why are digital twins important?
According to the IoT implementation survey by Gartner, organisations implementing IoT already use digital twins (13%) or plan to use it within a year (62%). So it is important that businesses catch up and learn why it is important that these models are upcoming. Forbes discusses what real-world companies are currently using digital twins and how they are so upcoming.
Digital twins are significantly improving enterprises’ data-driven decision-making processes. Because they have the ability to link to their real-world equivalents, businesses are using these models to understand the state of the physical components, respond immediately to changes, significantly adapt operations, and improve value to the systems.
Why are digital twins important?
There are a few different types of digital twins, depending on the level of product magnification. The main difference between these is the area of application, and it is even common to have two different types of twins working together within a system or process.
Component/ part twins
These are the basic unit of the digital twin, the smallest example of a functioning component. Part twins are roughly the same thing, but are slightly less important.
When two or more components are working together, they form an asset. Asset twins allow you to study the interaction of these components, creating a mass of performance data that can be processed, then turned into actionable insights.
The next level of magnification involves system twins, which show you how different assets come together to form an entire functioning system. System twins allow you to fully see the interaction of assets, and mean you can suggest performance enhancements.
Process twins are the macro level of magnification. They reveal how systems collaborate to form an entire production facility. Process twins can help determine the precise timings that ultimately influence the overall effectiveness of your product or service.
The benefits of using a digital twin
Another important question to ask other than “what is a digital twin?” is “why should you use a digital twin?”With the help of a digital twin, companies can test and validate a product before it even exists in the real world. By creating a digital replica of the planned production process, a digital twin allows engineers to identify and visualise any process failures before the product even goes into production.
Engineers can disrupt the system to synthesise unexpected scenarios, then examine the system’s reaction, and identify any mitigation strategies. This will improve risk assessment, accelerate the development of new products, and enhance the production line’s reliability.
Additionally, as a digital twin system’s IoT (internet of things) sensors generate big data in real time, businesses have the ability to analyse their own data to proactively identify any problems within the system. Allowing businesses to more accurately schedule predictive maintenance, therefore making production lines more efficient and lowering maintenance costs significantly.
Businesses can often find it difficult or even impossible to get a real-time and in-depth view of a large physical system. But with the use of a digital twin, this can be accessed anywhere, enabling workers to monitor and control the system’s performance remotely.
There are many new upcoming technologies changing the way businesses work, even with the recent development of the Metaverse, it is important for companies to stay up to date.
What to consider before using a digital twin
As this is a new revolution to many businesses, there are of course some things to consider before implementing a digital twin to your business. Firstly, like any new practice, users of digital twin technology must adopt new ways of working.
This could potentially lead to problems in building new technical capabilities. Companies will need to make sure that their staff has the required skills and have received proper training and tools to work with digital models.
As well as this, these types of models depend on the data from thousands of remote sensors that communicate over sometimes unreliable networks. Your business must be able to exclude bad data and manage gaps in the data streams if you want to effectively access digital twin models.
The amount of that data is collected from these numerous endpoints is extreme. Each of the endpoints represent a potential area of security vulnerability. So companies must assess and update their security protocols before beginning to adopt digital twin technology.
For some companies, simulation technologies may be more suited, in one of our blogs we discuss digital twins vs simulations technology.
Do AI and digital twins have a relationship?
Artificial intelligence and digital twins do in fact have a mutual relationship, where both contribute to one another. Digital twins have the potential to help businesses generate simulated data which can then be used to train and develop AI models.
AI could also benefit from digital twins as this type of digital model can virtually create an environment where machine learning products can test scenarios. Depending on the score of the virtual environment, data scientists and engineers can maintain AI solutions.
However, digital twins can also benefit from artificial intelligence. AI and machine learning algorithms allow companies to both build some digital twins and process large amounts of data collected from those digital twins. For example, engineers can accelerate the design processes to quickly evaluate possible design alternatives by leveraging AI capabilities with the digital twins. Many people believe that digital twins are actually the next phase of AI.
Summary - what is a digital twin?
Digital twin technology, when combined with the latest machine learning and artificial intelligence tools, can help companies across many industries reduce operational costs, increase productivity, improve performance, and change the way predictive maintenance is done.
For product manufacturers specifically, digital twin technology is a crucial element to achieving more efficient production lines and faster time-to-market.