By simulating a piece of equipment, a complex system, a production line or an entire factory in near-real time, virtual or digital twins will transform maintenance operations and jobs.
In an industrial environment that is becoming digitalized, the collection and interpretation of data from the company’s various assets is paramount. From this data, thanks to Artificial Intelligence techniques such as Machine Learning, Deep Learning and neural networks, prediction models are built.
The data, once interpreted, can be used to improve overall system reliability to significantly reduce downtime, discriminate the origins of performance concerns, or maximize asset availability. All of this information allows to significantly optimize maintenance time and therefore the availability of the asset. For industrial companies, these technological advances represent a major saving in time and money.
Globally, the emergence and rise of the 4.0 industry has seen many innovative companies appear on the market. These companies offer and implement services and software that enable manufacturers to be more efficient.
BUSINESS CASE : METROSCOPE
These companies with innovative and disruptive technologies are facing issues related to their high growth potential. Positioning, processing of macro-economic data, analysis of the competition already in place, understanding the organization and structure of the supply chain. To initiate the development of these structures it is essential to have done a powerful research and investigation work upstream.
As part of a diversification of its customer portfolio, Metroscope missioned Surfeo to benefit from its expertise and market knowledge. The objective was to establish a win-win strategy allowing the start-up to deploy its solution in a strategic and controlled development process.
To support these companies, Surfeo apply a approved process:
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