What is a digital twin?

What is?

A digital twin is nothing more than a simulation of a real object, which allows us to know its state, interact with it, and ultimately simulate its real behavior. A close example would be that of a commercial aircraft flight simulator. Although they are proliferating now, their use goes back a long way. NASA, as far back as the 1970s, used twins (in this case physical) of its spacecraft to test their behavior on the ground. When Apollo 13 suffered damage on its flight to the Moon, it was one of the mechanisms used to provide a solution. Originally confined to the military and aerospace fields because of their prohibitive cost, they are now proliferating in many other areas for several reasons: the use of digital technology reduces construction and operating costs (by analogy, a wind tunnel costs hundreds of millions of euros and a fluid simulation program a few hundred thousand), by the proliferation of low-cost sensors for almost any type of need, the spread on a planetary scale of high-speed (4G) and low-power communications networks (SigFox, NB-IoT) and the use of cloud computing, much cheaper and more elastic than the alternative of having to acquire your own server farm. This proliferation makes the concept of the digital twin fuzzy, and in some cases confusing or mixing it with pre-existing concepts. In general, it is assumed that, in order to be called a digital twin, it must meet a series of premises: be connected to a real object, collect data from that real object, be able to interact with that real object, and ultimately, be able to test how that real object would behave in the face of certain decisions. The flight simulator example above would not fulfill some of these premises, but it is illustrative. A remote control console for a drone, which also incorporates simulation capabilities, would be a digital twin.

Why are they important to me?

In general, whenever I have a series of physical elements on which I want to know their status at all times, to which I need to send orders or new configurations and above all, in which I am interested in knowing the impact of my decisions before making them reality. It can be said that the twins that allow to know the state of things are the first generation. Their use already brings great advantages to their users, since they allow to know their status at all times and anticipate possible problems (all predictive maintenance solutions, for example, would fall into this category). If you can also interact with the object, alter its behavior, either manually (by an operator) or especially in an automated way, we can say that they are second generation. Again, this ability to alter the state or behavior of the object without having to move an operator has great advantages, because of the promptness of the response, the cost savings in human resources, etc. Finally, and what really defines a digital twin, is the ability to simulate the effect of my decisions. This third generation is the key to the digital twin concept and is undoubtedly the one that will have the greatest economic impact on my organization. What happens if I change the layout of the aisles in my warehouse? What happens if I change the green time of the traffic lights in my city? What happens if I remove a truck from my fleet of trucks? These questions would be very expensive to answer if not impossible in the real world, having to assume their consequences. In contrast, a digital twin could help us evaluate the various options with minimal impact, both in terms of time and, above all, feasibility. Therefore, the impact of using a digital twin in my organization could be summarized as follows:
  • Greater agility in decision making, by being able to see its result immediately, even in processes that would naturally take months or years.
  • Higher quality in my decisions, as I can evaluate multiple alternatives and choose the one that best suits my needs.
  • Lower operating costs, by being able to improve the maintenance of my assets, optimizing part changes, or reducing required travels.

What about the future?

If until now the digital twins have been helping us passively in decision making, from a very near future they will be a very active part of it. By learning from our reality using Artificial Intelligence or Deep Learning techniques, they will be able to suggest solutions to our problems or operational improvements. This, which years ago was science fiction, is now a reality very close and accessible to organizations. At oga we can help you build your digital twin of the logistics world, reducing your operational and fleet costs.

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Autor
Jaime Nebrera oga
Jaime Nebrera
Big Data Consultant / Project Manager en oga

Consultant specialized in new technologies and Big Data.

Pioneer in Spain in the use of cutting-edge technologies such as Apache Kafka and Druid, he has extensive experience in the design of innovative technological products.