The term “digital twin” is often used as if it refers to a single type of solution. In reality, it covers a wide range of use cases, from individual machines to entire production environments, and also includes product behaviour where the interest is how the actual product reacts.
Understanding the objectives and where the risk is highest is the key to identifying where to start.
One of the most common use cases for digital twins is within machine development and prototyping. Here, digital twins are used to:
This allows developers to detect and resolve issues earlier in the processes, before machines are built or delivered.
Digital twins also play an important role when systems must interact and new machines are added to an existing production line. They allow engineers to test how machines communicate with external equipment, validate timing and synchronisation between systems, and ensure safe operation across integrated processes. By connecting the actual PLC code to the virtual model, digital twins enable parallel commissioning, making it possible to commission the machine both mechanically and functionally before it is deployed in the real production environment.
This is particularly valuable when multiple vendors or technologies are involved. For example, a Danish equipment manufacturer needed to increase capacity on an existing production line in Germany. By testing, optimising and running products through both the new equipment and the existing equipment before travelling to site, the team reduced on-site commissioning from a planned three weeks to just one week — without using any physical equipment for testing before installation.
The impact is not only less time spent on site, but it also frees up critical resources to start the next project prior to schedule.
In average we expect digital twins to reduce commissioning time with around 40% compared to traditional commissioning time. The key is that the equipment is tested and verified end-to-end before being assembled and manufactured.
At a broader level, digital twins can represent parts of, or even entire production environments. In this context, they are used for:
This helps companies make better decisions about how their operations are designed and run. It is estimated that an issue discovered in the commissioning phase requires 60-100 times more labour effort than issues discovered in the design phase.
Another emerging use case is training. Digital twins can provide safe environments where operators learn how systems behave, maintenance teams can practice troubleshooting, and new employees can get familiar with equipment, without having any effect on the actual production.
Beside increasing operator safety, training in a virtual environment reduces production loss and increase material yields. Even when primarily used for development, the same models can support learning and onboarding.
Over time, digital twins can support ongoing optimisation by evolving alongside the system they represent. As changes are introduced, the models can be used to test upgrades before implementation, analyse issues remotely, and evaluate potential improvements. This creates a continuous feedback loop between development and operations, enabling more informed decisions and more efficient system improvements.
It’s important to recognise that there is no single “correct” digital twin setup. And that no digital twin can (effectively) do everything. Different use cases require different approaches:
In other words, be specific in terms of what the purpose of the digital twin is.
Digital twins are not limited to a single phase or function. They can support everything from development and testing to operations, training and continuous improvement.
The key is to start where complexity, risk or uncertainty is highest and build from there.
Want to talk about your needs and how a digital twin can support your business? Reach out to Lotte Høeg Jul Jensen, Head of Digital Twin & AI at ProjectBinder: