Stellantis Leverages AI to Detect Industrial Defects Early

Quality is no longer limited to final inspection. In the automotive industry, the use of data and algorithms pushes manufacturers to intervene before defects appear on a large scale. At Stellantis, this shift is reflected in a dedicated function at the highest level and a partnership with Mistral AI to accelerate analysis and early detection.

Prevention Becomes the Rule, Customer Trust the Challenge

Companies are increasingly looking to act before problems become visible, relying on data, analytical capabilities, and artificial intelligence. Quality is thus evolving into a capacity for anticipation, surpassing mere final inspection. This transformation affects the entire industry, beyond just the automotive sector. In a context where customer trust is a competitive advantage, avoiding the need for repairs becomes a central objective. This change relies on technology, team organization, and the flow of information. Early detection allows for intervention before a defect impacts a large number of products.

What Quality Now Encompasses for the Customer

The assessment of quality no longer focuses solely on the product but now includes the entire customer journey. This encompasses vehicle reliability, the speed of interventions, downtime, access to spare parts, and the quality of interactions with the network. Quality managers at production sites frequently communicate with dealerships and workshop managers to gather feedback from the field and accelerate the implementation of improvements. Particular attention is paid to the enhanced use of data from the actual use of vehicles to continuously refine products and services. The goal is to promote the use of data instead of multiplying inspections, in order to simplify, streamline, and make the customer journey more predictable.

Connecting Data and Accelerating Analysis with AI

Every vehicle produced generates a wealth of information from production checks, tests, operator feedback, and after-sales service. The challenge lies in connecting these various sources to detect warning signals, such as the convergence of an anomaly during testing, a variation in manufacturing, and after-sales feedback. Artificial intelligence plays a crucial role here as an accelerator: algorithms analyze volumes of data that are inaccessible manually, reveal subtle correlations, and help prioritize alerts. AI does not make decisions for engineers but saves them time in analyzing root causes and identifying weak signals.

Automotive, Governance, and AI: The Stellantis Case

The automotive sector concentrates the constraints that necessitate early defect detection, with electrification, embedded software, and accelerated development cycles. Vehicles combine mechanical elements, electronics, software, and connected services. Quality is now considered from the design phase, throughout the manufacturing process, and after delivery. At Stellantis, the creation of a Chief Quality Officer function within the global executive committee underscores the strategic importance of quality, viewed as a lever for competitiveness. Stellantis, which includes brands such as Alfa Romeo, Citroën, DS Automobiles, Opel, and Peugeot, has partnered with Mistral AI to leverage artificial intelligence models aimed at improving vehicle development, manufacturing quality, and field feedback analysis. The goal is to detect defects before they affect the customer experience. While the logic of final verification remains, it is no longer sufficient in the face of these new challenges.