The 7 biggest mistakes and challenges when starting with Digital Twinning in industry

Digital Twins help in optimizing workflows in the development, deployment and life cycles of systems. Although the value of Digital Twins has become more obvious, we still see a lot of manufacturing companies struggling to get started. 

That’s why we want to discuss the 7 biggest mistakes and challenges we see companies struggling with.

Biggest mistakes and challenges:

  1. Underestimation 
  2. Work without a use case
  3. Picking the wrong toolkit
  4. Not looking for help
  5. Finding yourself boxed in in a single use solution
  6. Picking the wrong resolutions and levels of detail
  7. Not getting the required data and information out of the organization


1 Underestimation 

Building and setting up a Digital Twin of your system can be a real struggle. Setting up a Digital Twin for use cases such as Virtual Prototyping and Virtual Commissioning will take time. Start by obtaining knowledge of theory and technology before you start working on your models.

It’s important to scope a clear goal, with a definition of what the required results should be. This helps to make informed decisions during the first setup of your model. It prevents you from getting stuck with issues along the way.


2 Working without a use case

Digital Twin technology can be used for a multitude of purposes. This versatility brings with it the risk of losing sight of the originally defined goal. A change of use case during the process could be disastrous for the end goal. For example: do you want the Digital Twin for communication and visualization purposes or do you need it for emulation and simulation? These options will respectively require a different level of detail and performance of the Digital Twin.

A well-defined use case forces you to make the necessary choices, and to “fine-tune” your virtual models and digital twin, to meet the requirements.


3 Picking the wrong toolkit

If you don’t work with a clear goal in mind, fancy marketing videos could cause the temptation to make choices that later result in issues. Some toolkits will present many options, but the tools might not enable you to actually build the desired setup. Another issue could be that the software of your choice is not open enough for the desired inputs like data, physics or behavior or doesn’t have the outputs you eventually need.

Another mistake in line to this is to program tools yourself. It’s mostly for one use purposes and takes a lot of time.


4 Not looking for help

Creating a Digital Twin is a complex process. It becomes easier when you do it more often and gain more experience. In the beginning you will inevitably run into walls and problems that will be hard to find a solution for. 

Don’t be stubborn, look for help! It requires expertise and knowhow of the tools to build a working Digital Twin. Working with experienced partners helps you to make a roadmap that suits your future needs and translates these to your Twin. They can guide you through the process and teach you the way.


5 Finding yourself boxed in in a single use solution

Here is a paradox: although you want a good “toolkit” that helps you to build the solution to solve your use case, we often see that a too “confined” box can be a killer for your digital twin. 

On 3 levels: 

  1. You need the openness to adjust for your specific context and challenges, so a too confined not open single purpose solution works suffocatingly.
  2. You need to bring in multiple data models and data sets, so the interoperability with a lot of model types combined with the openness to adjust even for example on “code-level” is where the devil finds your detail.
  3. Your digital twin cannot move over the line in your organization, and finds itself “trapped” in your software tool. 

So being too confined can result in not getting your use case out.


6 Picking the wrong resolutions and levels of detail

As mentioned in point 2, but really important. If a clearly defined use case and the experience are lacking, it can lead to picking the wrong resolutions and levels of details in model, simulations, data and so on. The difficulty here is once you’ve chosen a tool and route and find yourself running up against a wall, going back can be very expensive in time, resources and money. Having open interoperable tools helps to quickly “switch”, and having help from experienced people helps to make the best informed decisions at the start.


7 Not getting the required data and information out of the organization

The IT infrastructure of many organizations consists of a spaghetti plate of data and software types, and on some levels information isn’t digital yet. So connecting, and acquiring this information can be a challenge. Collecting available information is step one, and “connecting” “importing” and “inserting” the different types of information is step two. For the blanks one could work with dummy data. 

This is also a great way to get insight in where our organization lacks information and has blindspots



As you may have figured out by now, creating a Digital Twin is a challenging task. But on the bright side, the more you do it the easier it gets. Perspective has helped multiple companies with their digital twin journey. If you or your company is/are interested in starting with digital twins and need some guidance, you can reach out for more information.


0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *