A machine learning-driven Six Sigma framework for enhancing the quality improvement and productivity in the Aircraft Manufacturing

The aviation industry, a pillar of global transportation, is under constant pressure to increase productivity and efficiency while maintaining strict quality requirements. Airctraft defects in production can result in significant financial losses, lead to costly rework, delays, and even safety risks. This study proposes a framework to improve productivity and efficiency in aircraft manufacturing and analyze quality control using machine learning, Six Sigma, and the QCDSME (Quality-Cost-Delivery-Safety-Morale) method. The DMAIC (Define-Measure-Analyze-Improve-Control) stage is a reference in the implementation steps of the Six Sigma method of the Airbus A320. The sigma value in this study was obtained on average for 40 periods of 4.61 sigma and a DPMO of 1225.69. At the analyze stage, a fishbone diagram is used to find the root cause of the problem. Furthermore, a machine learning analysis was performed using the text mining method to identify the most common product components that frequently have defects in Airbus A320 and identify the main factors causing defects, by the human factor. The enhance stage suggests a rise in overcoming challenges with the QCDSME method. Overall, it was discovered that the number of defects fell while the sigma improved and this method can enhance industry performance.