Data-Driven Process Optimization Using AI and Statistical Methods in High-Tech Manufacturing

High-tech manufacturing industries—including semiconductor fabrication, automotive assembly, and aerospace component production—face increasing demands for precision, efficiency, and adaptability. Traditional process optimization methods such as Design of Experiments (DOE), Six Sigma, and Measurement System Analysis (MSA) have long provided structured frameworks for improving quality and consistency. However, these statistical approaches are often limited by their static nature and reliance on fixed experimental models, which can fall short in rapidly changing or highly complex production environments.

This research presents a comprehensive, data-driven framework that integrates classical statistical techniques with modern artificial intelligence (AI) methodologies to enable dynamic and continuous process optimization. By leveraging AI algorithms—such as supervised machine learning models, reinforcement learning, and anomaly detection—alongside DOE, Six Sigma, and MSA, manufacturers can achieve real-time adaptability, enhanced process control, and predictive accuracy. The integration allows for a synergistic approach where AI models are trained using data generated from traditional experimental designs and refined through continuous feedback from manufacturing execution systems and IoT-enabled devices.

The effectiveness of the proposed framework is demonstrated through three case studies: (1) semiconductor etching optimization using AI-augmented DOE, (2) torque correction in automotive assembly lines using Six Sigma with machine learning, and (3) defect reduction in aerospace fabrication through predictive maintenance and enhanced MSA protocols. Across these applications, the hybrid approach led to substantial improvements in key performance indicators—yield increases up to 5.2%, defect reductions by as much as 70%, and cycle time decreases of over 15%.

These findings confirm that AI does not replace traditional statistical methods but rather enhances their utility by adding flexibility, speed, and the ability to model complex, nonlinear relationships. As manufacturing becomes more digitized and data-rich, integrating AI into established process improvement frameworks will be essential for maintaining global competitiveness, operational resilience, and product excellence. The paper concludes with recommendations for future research on explainable AI, cross-disciplinary training, and standardized integration protocols for industrial deployment.