What challenges did Michelin face before AI implementation?

Maintaining in-house Kafka infrastructure was operationally complex, time consuming, and expensive. Michelin needed reliable real-time inventory visibility and a cloud path that could scale across the enterprise.

What was the impact of the AI solution?

Estimated 35% cost savings compared to on-premises operations. 99.99% uptime and faster time to market by 8-9 months. Reduced operational burden and enabled real-time inventory management across multiple departments.

Source

MicrosoftProductionEvidence: Medium65/100

Michelin Accelerates Digital Transformation with Microsoft Azure and Confluent Cloud for Real-Time Inventory Management

Use case typeData platform modernizationUpdated Dec 31, 2024

Michelin faced operational complexity maintaining in-house Kafka infrastructure for real-time inventory and supply chain visibility. The company migrated to Confluent Cloud on Microsoft Azure to gain cloud-native scalability and resilience, reduce operational burden, and support real-time inventory management across the business.

Organization: Michelin
Industry: Automotive
Location: France
Published: December 2024

Reported outcomes

−35%

operational cost savingsCost savings

100%uptime8.5 monthstime to market

Strategic outcomes

Cost efficiencyReduced operational burdenBetter decisions & insightEnabled real-time data-driven operationsInnovation & cultureExpanded data streaming across departments

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Operational cost savings: 35% decrease

ConfluentDec 31, 2024Customer storyExplicit claimMedium evidence strength

Estimated 35% in cost savings from using Confluent Cloud, compared to on-premises operations

Normalized claim

Uptime: 100%

ConfluentDec 31, 2024Customer storyExplicit claimMedium evidence strength

Resilient, highly available architecture with 99.99% uptime

Normalized claim

Time to market: 8.5 months

ConfluentDec 31, 2024Customer storyInferred claimMedium evidence strength

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

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Michelin
Provider: Microsoft
Maturity: Production
Linked source: Confluent

Michelin faced operational complexity maintaining in-house Kafka infrastructure for real-time inventory and supply chain visibility

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Data platform modernization
2Real-time analytics
3Workflow orchestration

Maintaining in-house Kafka infrastructure was operationally complex, time consuming, and expensive.
Michelin needed reliable real-time inventory visibility and a cloud path that could scale across the enterprise.

Michelin implemented Confluent Cloud's fully managed Kafka service in a Microsoft Azure environment.
The company rewrote its orchestrator based on Kafka and Kafka Streams and used the platform for real-time data streaming and inventory management.

Technologies

Microsoft Azure Confluent Cloud Apache Kafka Kafka Streams

Estimated 35% cost savings compared to on-premises operations.
99.99% uptime and faster time to market by 8-9 months.
Reduced operational burden and enabled real-time inventory management across multiple departments.

Architecture

Michelin migrated its Kafka streaming foundation to Confluent Cloud running in a Microsoft Azure environment, replacing self-managed Kafka clusters to support a cloud-native, real-time inventory and supply chain architecture. The article also notes that Michelin rewrote its orchestrator based on Kafka and Kafka Streams.

Implementation partners1

Confluent

Sources & evidence1

Evidence: Medium65/100Evidence strength