How Michelin Uses AI to Turn Volatility into Stability
Arnaud Morvan
•
Oct 29, 2025
Introduction
Demand volatility is one of the toughest challenges in global manufacturing. When markets shift overnight, production plans can quickly become obsolete, creating inefficiencies that ripple across the supply chain.
Michelin, one of the world’s leading tire manufacturers, operates in more than 170 countries with hundreds of product models. For them, the stakes are high: any mismatch between demand and production risks tying up capital in excess inventory or leaving customers waiting with shortages.
Traditional forecasting, based largely on historical sales and manual planning, was no longer enough. It was too slow, too reactive, and too dependent on fragmented data.
To address this, Michelin made a bold move: bringing agentic AI for supply chain forecasting into the core of its operations, much like what MyExobrain Decision AI Agents now deliver across industries to automate, anticipate, and orchestrate real-time decisions.
The Challenge: Complexity Meets Volatility
Michelin’s supply chain complexity made forecasting a real struggle:
Diverse product portfolio: from passenger car tires to aviation, agricultural, and specialty products.
Unpredictable demand: consumer behavior, seasonality, and economic cycles caused constant swings.
Inefficient processes: in Poland, planners spent hours each day chasing logistics data, only to end up with mismatches in supply.
The result was predictable: shortages in some areas, excess stock in others, and planners under pressure to “catch up” rather than get ahead.
The Shift: Agentic AI-Powered Forecasting
To regain control, Michelin introduced machine learning and decision AI agents into its forecasting and planning processes.
These agentic AI models:
Analyzed real-time and historical data to uncover hidden demand patterns.
Predicted demand fluctuations with higher accuracy and adaptability.
Generated proactive alerts and recommendations, reducing risk of stock-outs or overproduction.
In North America, Michelin advanced further with AI-driven simulations and digital twins, the same foundation used in MyExobrain’s agent AI for operations and logistics. These simulations empowered planners to run “what-if” scenarios such as:
Sudden demand surges.
Supplier delays.
Transport bottlenecks.
Instead of reacting after the fact, agentic AI for logistics enabled teams to anticipate and orchestrate responses automatically, before disruptions escalated.
Scaling Decision AI Agents Across the Company
What started as targeted pilots quickly expanded. Today, Michelin has more than 200 agentic AI use cases across its global supply chain.
These include:
Forecasting copilots for various product lines.
Predictive maintenance pilots minimizing downtime.
Procurement automation copilots to streamline supplier planning.
The impact is measurable:
Forecast accuracy improved significantly.
Stock shortages and excesses were reduced.
Planners gained time for strategic work instead of manual tracking.
Decision cycles accelerated with fewer errors and higher confidence.
AI has evolved from being a passive tool to becoming a decision-making partner, an intelligent agentic layer similar to MyExobrain, connecting forecasting, procurement, and logistics into one continuous decision loop.
Lessons for Supply Chain Leaders
Michelin’s journey illustrates the next evolution of operations management, one driven by agentic decision intelligence:
Forecasting must evolve: static spreadsheets can’t handle today’s complexity.
Digital twins powered by agentic AI create resilience: enabling proactive scenario testing and faster risk mitigation.
Agentic AI scales beyond forecasting: extending value into procurement, planning, and logistics.
Decision AI agents accelerate responsiveness: bridging data, insight, and execution into a seamless decision flow.
Key Takeaways
Michelin faced volatility across global operations and needed faster, more adaptive forecasting.
Agentic AI and decision agents predicted demand, detected risks, and simulated scenarios for resilience.
Over 200 MyExobrain-like use cases now support forecasting, maintenance, and automation.
The impact: fewer shortages, optimized stock, faster and more aligned decisions.
The future belongs to agentic AI for supply chain, where MyExobrain leads the move from data to autonomous action.
