Process-Monitoring-for-Quality — A Step Forward in the Zero Defects Vision

More than four decades ago the concept of zero defects was coined by Phillip Crosby. At that time it was only a vision, but today with the introduction of Artificial Intelligence in manufacturing it has become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few defects per million of opportunities. Therefore, detecting these rare quality events is one of the modern intellectual challenges posed by this industry. Process Monitoring for Quality is a big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right machine learning, optimization and statistics techniques are applied to develop an effective predictive system. Manufacturing-derived data sets for binary classification of quality tend to be highly/ultra unbalanced, making it very difficult for the learning algorithms to learn the minority (defective) class. In this paper, the learning and deployment paradigm of Process Monitoring for Quality is presented, a discussion of how it interacts with traditional quality philosophies to enable the development of zero defect processes is provided, followed by a contrastive analysis of the two paradigms. Finally, a case study from one of the processes of the Chevy Volt in which 100% of the defects are detected is demonstrated to support this vision.