Michelin accelerates transformation with Confluent

Using Confluent Cloud, Michelin streamlined Apache Kafka® operations to achieve a 35% reduction in operating costs and eight-to-nine months faster time to market.

Michelin, the leading mobility company, is using Confluent to power its global inventory management system. By leveraging Confluent Cloud, Michelin was able to quickly scale its real-time inventory system to meet global demand while cutting operational costs by 35%. 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.

“Confluent plays an integral role in accelerating our journey to becoming a data-first and digital business,” said Yves Caseau, Group Chief Digital and Information Officer, Michelin. “Today’s customers demand rich, personalized experiences, and business operations must be optimized to stay ahead of the competition. 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.”

Michelin’s Challenges with Self-Managed Apache Kafka

As one of the world’s largest tire manufacturers, Michelin’s teams require 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. And, Michelin’s mobility solutions like predictive insights for tire replacements and route recommendations for fuel optimization 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.

Finding Cloud-Native Agility with Confluent

“Given today’s economic pressures, many businesses are faced with the challenge of cutting costs while also keeping ahead of the competition and customer expectations,” said Erica Schultz, President of Field Operations, Confluent. “We’re proud to help companies like Michelin achieve success on both fronts. With a truly cloud-native data streaming platform that goes above and beyond Apache Kafka, we help offload the costs and risks of self-managing Kafka while also helping drive real-time, data-driven decisions and operations.”

With Confluent’s fully managed Kafka service, Michelin addressed the challenges of Kafka operations and accelerated their journey to the cloud. They built a centralized 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, thanks to the cloud-native platform that greatly reduces the operational issues of self-managed Kafka.

•Achieve faster time to market – 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.

•Improve uptime – With Confluent’s 99.99% SLA, the Michelin team can offload operations and have peace of mind that mission-critical data streaming workloads in the cloud are resilient and highly available.

Michelin expects widespread adoption of data in motion across a number of new use cases as the business continues to experience a high ROI on Confluent projects.

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