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What is a Digital Twin, and do you need one?


If you are interested in a proper definition of the term Digital Twin, you probably want to check out the wikipedia page on Digital Twin, as well as Gartner's take on it.

If instead you are looking for a fairly pragmatic view on what a Digital Twin is and what it does, continue the read and please share your thoughts in the comments!


Now that this is out of the way, let's bring in our view on what a digital twin is. Usually, when people ask me this question, I give the provocative answer "It's whatever you want it to be". And when I look at the wide range of Digital Twin offerings, I do believe that this is somehow the case.

However, in my opinion there are a couple of recurring aspects that are more often tagged as Digital Twin ingredients than others, and this is what I wanted to have a closer look at.

Digital Twin Ingredients

A Digital Twin has an analog counterpart

Whether it is a product or a process or some production environment, a Digital twin is the digital counterpart of something from the analog world. And the major benefit is that the Digital Twin is capable of digitally representing the behavior if its counterpart. In this context, I struggle a little with the understanding that Digital Twins require simulation capabilities, especially when this is the only "feature" of a Digital Twin.

For me, a Digital Twin is more about the now, and not necessarily about the future or the past. However, to map out the behavior, you will have to collect and analyze data, e.g. look into the past. And only understanding today's behavior will allow you to project into the future.

To summarize, to create a Digital Twin of something analog, you will have to collect data from the now, look back to analyze, to be able to make assumptions on the future.

In any case, now we are entering the data part...

A Digital Twin is data-driven

A Digital Twin, in essence, collects data from its analog counterpart, and nothing else. As explained above, the journey towards the Digital Twin starts in the NOW, hence (close to) real-time data is likely to be your base. Depending on your use case, this might be the latest engineering version, some performance data of an asset, or the current position or status of a given object.

The Digital Twin representation will grow stronger the more data you feed it, given that you are capable of handling the data properly. Simply collecting raw data and storing it in a database will not help you. Instead, you want to process the data for a value-creating use case, like send warnings on out-of-range performance, or build a Track&Trace case for your pre-products.

And here is my point: from my perspective, a Digital Twin is established, when the data that you are collecting brings an outcome, e.g. value that you are expecting and helps your day-to-day business. In best case, your Digital Twin will include several use case capabilities, so that you can get more value out of your data. Alternatively, a simple monitoring solution may suffice your needs.

I have had conversions on whether data analytics are crucial to building a Digital Twin, but as I lined out before, I personally do not think so, at least not on a "must" level.

A Digital Twin is tangible

I cannot recall having a Digital Twin discussion without looking at something tangible. A Digital Twin usually is in the foreground, rather than in the background of your perception.

And I remember well the moment back in 2016, at the E4TC in Aachen, Germany, when I first saw Elisa' s 3D Technology in action: the 3D view of the factory showing operational data (I found the video I created shortly after joining Elisa) made the whole concept of a Digital Twin very tangible for me. It is also when I started to grasp the value of really seeing the data of the factory.

Ever since I am convinced that data visualization plays a major role in the Digital Twin concept. Well, the rest is history.

Conclusion: Yes, you need a Digital Twin

Why? Because you need to collect data on the products, processes and environments that are relevant to you. I also firmly believe that you should not stop with data collection and processing but additionally consider the foreground of your project, as the tangibility will make it easier for people to relate to your digitalization project, and help you get them excited about the outcome.

From my experience making the results tangible will increase the overall value of the data you are collecting, and according to the above concept, will lead to a Digital Twin.

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