Michelin: Democratizing AI for Improved Industrial Performance

Michelin uses Dataiku to democratize AI, improving quality, maintenance, machine availability, supply chain, energy consumption, and more.

The following Q&A occurred during the Everyday AI Conference Paris, during which Michelin hosted two sessions. 

Interview of Matthieu Leynet, Scrum Product Owner, AI & Simulation for Manufacturing at Michelin

Watch the Everyday AI Paris Talk


What Benefits Did You Achieve by Democratizing AI?  

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.


How Do You Use Dataiku?

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.


What 3 Tips Would You Give Dataiku Users? 

  1. The first tip would be to think big and start small.
  2. The second tip would be to ask business stakeholders and the process owner to express the progress they would like to make in democratizing AI.
  3. The third tip would be to build agile governance of data and digital products as your usage of AI increases.


What Does Everyday AI Mean to You? 

For me, Everyday AI is about empowering extraordinary people to change the way they do things based on data.

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