Mobility company Michelin is leveraging Confluent Cloud to quickly scale its real-time inventory system to meet global demand while cutting operational costs by 35%.
Confluent said this is a major step in Michelin’s evolution from a manufacturer that makes and sells tires to a leader of data-driven services and customer experiences.
“Today’s customers demand rich, personalised experiences, and business operations must be optimised to stay ahead of the competition,” said Yves Caseau, group chief digital and information officer at Michelin.
“We use Confluent Cloud as an essential piece of our data infrastructure to unlock data and stream it in real time, with use cases like customer 360, e-commerce, microservices, and more,” said Caseau.
Michelin’s teams need constant access to up-to-date information. For example, accurate status updates on raw and semi-finished materials are needed to ensure success across global supply chains and logistics operations.
Also, Michelin’s mobility solutions like predictive insights for tire replacements and route recommendations for fuel optimisation are dependent on frequent updates. To power its business with real-time data, Michelin initially turned to Kafka’s open source data streaming platform.
Kafka provided Michelin with a real-time view into its business with the ability to collect, store, and process data as continuous streams. This was a significant improvement from legacy applications that delivered daily or hourly updates using batch processing.
However, as they expanded Kafka’s footprint across the business, Michelin’s teams found Kafka increasingly difficult to scale and manage. A full-time team was needed to babysit Kafka clusters and maintain its complex, distributed infrastructure, causing both costs and risks to rise.
Also, the open source technology did not provide a clear path to the cloud, which held Michelin back from a company mandate to transition off of monolithic, on-premises systems.
They built a centralised data streaming hub with Confluent Cloud on Microsoft Azure, which helped reduce costs. Michelin estimates a 35% savings with Confluent compared to on-premises operations.
Also, Confluent helped Michelin save an estimated eight to nine months of time to market due to Confluent’s millions of hours of experience running Kafka in production in the cloud for customers.
