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Who isn’t familiar with Michelin? Whether it’s their extensive product line of tires for nearly every vehicle imaginable (including space shuttles), or the world-renowned Michelin Guide that has determined the standard of excellence for fine dining for over 100 years, you’ve probably heard of them. Founded in 1889, the French manufacturer has consistently charted a path of innovation that began in rubber factories and has expanded into the realm of modern, data-driven service offerings.
The company now harnesses massive volumes of data that fuel a variety of groundbreaking mobility solutions. For example, by tapping into data from connected devices like tire-mounted sensors and telematics boxes, Michelin can provide customers with critical insights that improve the safety and performance of their fleets—from measuring tire pressure and anticipating maintenance needs, to analyzing driver behavior and recommending routes that maximize efficiency.
When it comes to delivering the types of services described above, a strong technology solution and streamlined information systems are critical to ongoing success. For Michelin, ensuring their data infrastructure supports their evolving needs requires a two-step approach:
#BeEvergreen: Michelin strives to continuously modernize their information systems
#BeDataDriven: Michelin endeavors to support real-time analytics use cases to deliver insights at high speed
With this criteria in mind, Michelin adopted Apache Kafka, an open source distributed data streaming technology used for real-time data pipelines, data integration, and stream processing. With self-managed Kafka in an on-prem environment at the heart of their technology stack, Michelin had successfully transitioned to an event-driven architecture. A couple of their most impactful initial use cases include:
Fueling real-time IoT analytics with streaming data to produce insights that enrich fleet management services
Replacing the cumbersome business process engine responsible for internal product order management with a choreography pattern of 150 microservices that streams over Kafka
But Michelin’s newly acquired architecture wasn’t without growing pains. Open source Kafka proved difficult to self-manage for the company, even with the addition of three full-time employees dedicated to overseeing the clusters. The on-prem Kafka environment was in constant need of maintenance and operational management by in-house Kafka developers, making it very difficult to scale as Michelin’s use cases became increasingly complex.
This presented a problem for Michelin, which was eager to scale its architecture to address existing challenges with the inventory management system. Without reliable, scalable access to real-time data streams, the company was unable to access precise views of inventory across its supply chain. This often led to outdated reporting and inaccurate allocation of stock for customers, especially for raw and semi-finished materials.
“One of the challenges with Kafka was its operational complexity, especially as the footprint expanded across our organization. It’s a complex, distributed system, so we had to allocate a lot of our valuable technical resources and expertise to babysit it and keep it running.” - Olivier Jauze, Senior Fellow, Business Technology Platforms, at Michelin
While open source Kafka had helped Michelin jumpstart their event-driven transformation, it was time for the company’s next bold move—a leap to the cloud. It was at this point that Michelin knew they needed a managed Kafka service to alleviate the burden of Kafka operations and smooth the migration of their data from on prem to a Microsoft Azure cloud environment. Enter Confluent.
With the help of Confluent Cloud, a fully managed, cloud-native Kafka service, Michelin embarked on a cloud transition. During the process, they were able to completely offload the responsibility for day-to-day Kafka operations and management to the Confluent platform. By spring of 2022, they had successfully launched several exploratory use cases in the cloud, including one of their most critical projects—online order management. This was a big win for the manufacturer, since real-time inventory visibility would streamline the flow of materials throughout their supply chain.
“Kafka enables us to unlock real-time data throughout our organization. But Confluent goes way beyond Kafka to offer a platform for data in motion that’s truly cloud-native and re-imagined for the cloud, while offloading Kafka management and removing operational burden.” - Yves Caseau, Group Chief Digital & Information Officer, Michelin
As a result of adopting Confluent Cloud and undergoing a successful migration to an Azure cloud environment, Michelin anticipates a significant reduction in operational complexity and increased cash flow. They project an estimated 35% cost savings compared to on-prem operations. The company now plans to redirect their Kafka-dedicated teams away from tedious infrastructure management and toward higher value activities, such as innovating new solutions that will bring value to end customers and streamline the flow of data throughout the organization’s many lines of business.
Michelin is also seeing the benefits of increased reliability since adopting Confluent Cloud. With Confluent Cloud’s 99.99% uptime SLA, the manufacturer now has confidence that its architecture is resilient enough to handle the rapid scaling and processing of large volumes of data that often accompanies increased demand. And with real-time data fueling their inventory management system, Michelin’s customers can enjoy a seamless ordering experience with less stock inaccuracies.
Michelin is also quick to note that Confluent’s commitment to unparalleled technical support has had a significant impact on the company’s ability to eliminate barriers to cloud adoption and smooth the migration process. Olivier Jauze, IT Architect at Michelin, says it best: “One of the biggest assets Confluent has is knowledge of Kafka and streaming technology. It’s quite unique, in my 15 years of experience…When you are supported by people like those at Confluent, adoption happens on its own without barriers.”
“We decided Confluent was necessary when we realized the limitations of self-managing open source Kafka on our own…We estimate that for the last two years, Confluent has helped us to gain eight or nine months in terms of time to market to deploy the technology.” - Olivier Jauze, Michelin
The future is bright for Michelin’s data-driven ambitions. Several departments in the company now use Confluent Cloud to break down data silos within key areas like the supply chain, customer services, manufacturing, and R&D. The manufacturer anticipates that adoption of real-time data streaming throughout the organization will only increase as Confluent projects continue to demonstrate a high ROI.
For many years, Michelin’s purpose has been “to create a better way forward.” And while this commitment is clearly demonstrated by the French company’s ongoing efforts to provide increasingly sophisticated mobility solutions, it’s just as evident in their approach to their digital transformation journey. Onward, Michelin!
To learn more about how Confluent Cloud is helping Michelin become a data-first, digital business, read the full case study and watch the customer films here.
You can also check out Confluent’s data streaming resources hub for the latest explainer videos, case studies, and industry reports on data streaming.
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