Michelin runs 200+ AI use cases across manufacturing, supply chain and innovation

Overview

French tire manufacturer Michelin has more than 200 AI use cases in production, led by group chief data and AI officer Ambica Rajagopal. Its in-house IRIS system, protected by over 20 patents, partially automates end-of-line visual tire defect inspection to improve inspector efficiency and workplace ergonomics while operators retain final accountability. Machine learning forecasting tools improve demand forecast accuracy and proactively detect stock shortages in the supply chain. Michelin scans the startup ecosystem and uses tools including Databricks and Dataiku, and has partnerships with Microsoft and Rockwell Automation to codevelop AI solutions. The company reports AI-project ROI exceeding €50 million per year, growing 30-40% annually for three consecutive years, governed by an internal data office and responsible-AI principles (people-centric, explainable, accountable).

The challenge

As a manufacturing company founded in 1889 with 128 production facilities and nearly 130,000 employees in 63 countries, Michelin needed to apply data and AI to transform its business at scale. Core processes such as end-of-line visual tire inspection were time-consuming and costly: 100% of tires were controlled manually and visually, a complex ergonomic task from a physical and concentration point of view.

The solution

Michelin's in-house IRIS system, protected by over 20 patents, partially automates end-of-line visual tire defect inspection to improve inspector efficiency and workplace ergonomics, while operators remain accountable for the final decision on any tire IRIS flags as potentially defective. Machine learning-based forecasting tools help Michelin improve demand forecast accuracy and proactively detect stock outages in its supply chain. Michelin runs external innovation and exploration events to scan the startup ecosystem globally and incorporate best-of-breed AI solutions such as Databricks and Dataiku into its platforms, and is forming strategic partnerships with industry leaders including Microsoft and Rockwell Automation to codevelop AI solutions. Governance is led by group chief data and AI officer Ambica Rajagopal's team and an internal data office, guided by three responsible-AI principles: AI systems should be people-centric, explainable where necessary, and have clear accountability.

Reported business value

Michelin's commitment to AI transformation has led to an ROI from AI projects of over 50 million euros per year, with increases of 30% to 40% annually for the past three years. The company has also received considerable benefits from generative AI projects, including document processing in the tax department, social listening in marketing, and root cause analysis in manufacturing.