MICHELIN, FORVIA & CELONIS at AI for Industry by Artefact – AI for Automotive: State of the art & use cases

The “AI for Automotive” session featured key representatives from Michelin, Forvia, and Celonis, highlighting how AI is revolutionizing the automotive industry. AI is particularly transforming process optimization, predictive maintenance, and quality control, through real-world applications of machine learning, computer vision, and generative AI, reshaping automotive operations.

Prashant Dhanraj from Celonis emphasized AI’s potential to transform business processes such as product development and supply chain management. He explained how digital twins, virtual models of physical processes, allow companies to derive insights from large datasets and optimize operations. An example is using generative AI for cost estimates on new parts by analyzing technical drawings, identifying similar parts, and suggesting suppliers. AI also assists in order management, using LLMs to evaluate credit blocks in sales orders and provide recommendations based on past data, enhancing decision-making.

Jean-Vianney Chiron from Michelin explained how AI improves manufacturing with computer vision and time-series analysis. In tire production, AI-powered cameras detect defects in rubber, which is difficult to inspect manually. The technology ensures higher product quality and streamlines the process. Moreover, AI correlates machine data, such as temperature and pressure, with final product quality, improving efficiency by allowing workers to address critical issues rather than monitoring data manually.

Jean-Vianney Chiron highlighted how generative AI helps capture decades of knowledge from Michelin’s global plants. Maintenance staff can input machinery symptoms and receive diagnostic suggestions based on past cases. The system continuously learns from user feedback, creating a robust, evolving knowledge base accessible worldwide, improving response time to maintenance issues.

Caroline Connan from Forvia shared similar use cases, including AI for predictive maintenance to prevent production line shutdowns. By analyzing historical data and real-time machine parameters, AI alerts operators to potential issues, reducing costly downtime. An LLM-based system generates maintenance plans and helps prioritize actions. Additionally, AI-powered computer vision systems in Forvia’s factories detect quality defects in automotive interiors, reducing errors and improving product quality.

Michelin and Forvia both use AI to track and reduce energy consumption in their plants. Machine learning models analyze real-time sensor data to monitor electricity and water usage. These insights allow companies to set targets, forecast consumption, and adjust operations to reduce their environmental impact and operational costs. AI’s ability to provide real-time feedback is critical for achieving sustainability goals.