Integrating AI into Product R&D With Michelin
Michelin has been working on incorporating more machine learning into its processes for tire design and testing. This video tells more about how they achieved this.
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The following Q&A occurred during the Everyday AI Conference Paris, during which Michelin hosted two sessions.
The use of AI within an industrial group has several advantages: faster product design, better quality results, and improved industrial performance. What is essential to us is our speed in scaling the gains obtained through AI within our 85 industrial sites around the world.
Dataiku is used daily in our industrial sites as well as in our central teams on broad themes impacting quality, maintenance, machine availability, supply chain, and energy consumption.
A good example of the use of Dataiku is predictive maintenance: By collecting and analyzing the data provided by the machines, we are able to predict breakdowns before they occur and alert the maintenance technician so that he or she repairs them preventively.
For me, Everyday AI is about empowering extraordinary people to change the way they do things based on data.
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