Smart quality control: integrating six sigma, machine learning and real-time defect prediction in manufacturing

PurposeThis study aims to develop a smart quality control framework integrating Six Sigma methodology, real-time sensing technologies and machine learning algorithms to enhance manufacturing defect prediction and process optimization. By leveraging predictive capabilities and real-time data analysis, the framework seeks to reduce costs associated with poor quality and improve overall process capability.Design/methodology/approachThe proposed framework uses the Define-Measure-Analyze-Improve-Control methodology to identify and address critical process parameters. A robust Internet of Things (IoT) sensing network is incorporated for continuous process monitoring. At the same time, multiple machines learning models, including decision trees, random forests, boosted decision trees, linear regression and k-star algorithms, are evaluated for predictive defect detection. Implementation was conducted at an electrical conductor manufacturing facility, enabling real-time analysis and intervention to prevent defects.FindingsThe implementation of the framework demonstrated significant improvements in quality and efficiency. The cost of poor quality was reduced from 5% to 1.7%, a 66% improvement. Process capability was enhanced, with sigma levels increasing from 3.14 to 4.3. These results validate the effectiveness of combining traditional quality control techniques with advanced Artificial Intelligence and IoT technologies, delivering predictive capabilities and enabling real-time process optimization.Originality/valueThis study highlights the innovative integration of Six Sigma, machine learning and IoT sensing technologies to transform manufacturing quality control. The smart quality control framework represents a significant advancement in manufacturing intelligence, offering a scalable, data-driven solution that improves efficiency, competitiveness and sustainability across diverse industrial applications.