Quality control is a fundamental pillar of the aerospace industry. Every part produced must meet strict standards to ensure the safety, reliability, and longevity of aircraft. Yet, despite highly regulated processes, many manufacturers struggle to balance quality requirements with economic constraints.
Between high costs, slowed production, limited human resources, and missed detection of critical anomalies, traditional methods are reaching their limits. In response to these challenges, smart sampling, driven by artificial intelligence, is emerging as a solution for the future.
According to a report published by AFNOR, over 80% of industrial companies estimate that costs related to non-quality represent between 0 and 5% of their revenue. For 15% of them, however, this cost exceeds 10%.
These losses are largely due to poor allocation of control resources. Companies devote time and money to checking low-risk parts, while more critical ones slip through the cracks.
Another alarming point: according to Journal du Net, quality control stages can represent up to 70% of the production cycle time in certain industries. This reality is incompatible with the growing demands for responsiveness and competitiveness in the aerospace sector.
Lastly, the increasing complexity of products and processes makes it harder to recruit qualified operators. This further weakens the quality of manual inspections and increases the risk of undetected defects.
Faced with these challenges, smart sampling is proving to be a major technological breakthrough. Instead of inspecting a fixed number of parts based on rigid statistical logic or business rules, this method relies on Machine Learning algorithms capable of assessing, in real time, the risk of non-compliance for each part.
Concretely, our solution analyzes thousands of historical data points from manufacturing orders: part type, raw materials, batch number, machine used, production conditions, operator, etc.
By training the model on historical non-compliance data, the algorithms learn to predict which parts carry the highest risk of defect.
These parts are then automatically selected for inspection, while those deemed reliable are skipped — without compromising overall quality.
Adopting this technology offers several tangible and quickly noticeable advantages:
First, quality control costs are significantly reduced. Fewer parts to inspect means better allocation of operators, more efficient use of equipment, and fewer slowdowns on the line.
Second, inspection reliability improves. Parts most likely to present a defect are prioritized, enabling earlier and more reliable detection of critical anomalies. This drastically reduces the risk of returns, waste, or worse — product recalls.
Finally, the overall cycle time is shortened, allowing production rates to increase without sacrificing quality or traceability.
AI solutions are designed to integrate easily with existing tools (MES, ERP, quality software). The interface highlights the risk factors identified by the algorithms, providing full transparency on the selection of inspected parts.
Moreover, by capitalizing on historical quality data, they enable continuous process improvement. Each inspection campaign becomes smarter than the last.
Smart sampling does more than optimize a cost center. It transforms a constraint into a competitive advantage, by enhancing quality while reducing costs.
At a time when the aerospace industry is under growing pressure regarding deadlines, margins, and regulatory compliance, this AI-powered approach represents a true paradigm shift.
Would you like to assess the impact of this solution on your production line? Our teams can offer a personalized diagnostic to evaluate the feasibility of integrating such a solution into your processes.
