Process-Monitoring-for-Quality — A Step Forward in the Zero Defects Vision 2020-01-1302
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.
Carlos Escobar, Jorge Arinez, Ruben Morales-Menendez
General Motors LLC, Tecnologico de Monterrey