Introduction
While it's often said that "introducing AI will increase competitiveness," many people probably feel that "they don't know how to actually use it."
This article will discuss the global tire manufacturerMichelinBased on case studies, this article clearly explains how to integrate AI into business and achieve results. You will gain insights that can be applied to any business, not just manufacturing.
AI is at the core of "value creation," not "cost reduction."
The essence of Michelin's use of AI lies not merely in efficiency, but in "maximizing corporate value."
The company uses AI for the following two purposes:
- Improvement of core business processes
- Creating new business value
In other words, the key feature is that they are positioning AI not as a "partial tool," but as the "core of their management."
In fact, AI is integrated into the entire value chain, including manufacturing, marketing, finance, and supply chain, and is being used in more than 200 different business operations.
Thus, using AI not as a single, isolated element, but as a comprehensive solution is creating a competitive advantage.
Three concrete strategies supporting Michelin's use of AI
The reason Michelin is achieving results lies in its practical use of AI, which has been implemented down to the operational level.
■ Point 1: Dramatically improve on-site productivity
AI is being used in manufacturing in the following ways:
- Image recognition-based tire defect inspection (IRIS)
- Machine failure prediction (predictive maintenance)
- Automation of repetitive tasks
What's particularly noteworthy is that the AI is designed to "support" humans rather than "replace" them.
The final decision is made by a human, while AI plays a role in improving its accuracy and efficiency.
■ Point 2: Optimize supply chain and demand forecasting
AI is also being used to optimize supply chains.
- Improving the accuracy of demand forecasts
- Stockout detection in advance
- Optimization of logistics
This helps prevent excess inventory and lost opportunities, thereby increasing the stability of the business.
■ Point 3: Predictive analytics to enhance decision-making
Michelin uses AI,
- Analysis of complex data
- Future forecasts (demand, performance, customer behavior, etc.)
We are improving the quality of our management decisions by implementing these measures.
AI is not a "substitute for decision-making," but rather a "co-pilot" that improves the accuracy of decision-making.
Key points to avoid failure when implementing AI
What we can learn from the Michelin case is that "AI implementation does not equal technology implementation."
■ How does your work ethic change things?
- AI is not a "tool for improving operational efficiency," but rather a "decision-making infrastructure."“
- Its value only becomes apparent when it's used in both the field and management.
- It is important to accumulate small success stories.
■ Supplementary information that is not overly technical
① AI cannot function without data.
→ This is predicated on the establishment of a high-quality data infrastructure.
② Balancing top-down and bottom-up approaches
→ Management leadership + initiative from the front lines are necessary.
③ Ensuring ethics and transparency
→ Michelin prioritizes the following:
- Human-centered AI
- Explainability (avoiding black boxing)
- Clarification of responsibility
Even more important is managing ROI (Return on Investment).
Michelin is generating hundreds of billions of yen in value annually through AI investments and achieving an annual growth rate of over 30%.
Summary
The essence of AI is not "automation," but "improving the quality of decision-making."
What's important isn't how quickly it's implemented, but continuously using it in the field and making improvements.
Trying things out on a small scale, learning from the experience, and then expanding—that accumulation of experience is what determines a company's competitiveness.
