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“Houston, we have a problem …”
In the 1970s, during the Apollo XIII mission, NASA made the first deployment of a physical twin/digital twin.

Although NASA carried out the first deployment of a physical twin/digital twin during the rescue of the ill-fated Apollo 13 mission in the 1970s, it wasn’t until the first decade of the 2000s that the term was coined as we know it today.

Image: Flight directors celebrating the return of Apollo 13. Nasa.

The digital twin is a virtual model of a process, product, or service designed to accurately reflect the functioning of a physical object. We should not confuse the physical twin/digital twin pair with standard simulations. The latter do not benefit from real-time data collected by various sensors on the physical twin, nor from the computational power provided by the virtual environment of the digital twin.

Given this context, various traditional engineering sectors (production plants, technologists) have worked to make use of this technology in order to reduce maintenance costs, promote energy savings, and minimize quality escapes on their own products.

Important advances have been made in the implementation of this technology in recent years in sectors such as:

  • Aerospace, with the use of the digital twin in the modeling of engines, both in design and testing.
  • Automotive, focused on “zero defects” initiatives in vehicle production.
  • Railway, both in the design of signaling systems and rolling stock, as well as in the modeling of the railway network of a geographic area.
  • Healthcare, not only in the simulation of medical instrumentation, but also of patients.
  • Industry 4.0, where perhaps the highest expectations for this technology are being assigned, with the goal of obtaining manufacturing processes adaptable to demand, more sustainable and predictive.

It is also necessary to note the penetration of this technology in areas where it has been less well-known, such as risk assessments in the finance sector and even predictions about natural disasters and sea level rise as a result of climate change.

However, the advancement of the physical twin/digital twin currently faces various obstacles. First, sensorizing systems is complex and expensive, especially since most industrial plants were not designed in a context of digitalization. Second, obtaining a digital twin involves real-time management of large amounts of data, bringing with it a necessary investment in data analytics and cloud. And finally, at the technology level, there is no standardized technology, with the greatest experts being technology companies that have developed their digital twins for the development and manufacturing of their products and are now launching them to the market.

Nevertheless, despite the obstacles that any technology may encounter, it is estimated that by the middle of this decade, the market for digital twins will grow to €40 billion, with the corresponding demand for highly specialized profiles and companies in data analytics, sensorization, cybersecurity, and cloud architecture.