A modern definition of quality control and improvement is the reduction of variability in processes and products. The reduced variability can be directly translated into lower costs, better functions and fewer repairs. However, the final quality of processes and products is sometimes derived from other measured variables through some implicit or explicit functional relationships. Sometimes, a tiny uncertainty in a variable can produce a huge uncertainty in a derived quantity. Therefore, the propagation of uncertainty needs to be understood and the individual variables need to be well controlled. More importantly, the critical factors that affect quality the most should be identified and thoroughly investigated. Design of experiments and statistical control plays central roles in finding root cause of failure, reduction of variability and quality improvement.
In this paper, the theories on quality control and improvement are reviewed first with the emphasis on statistical data analysis and uncertainty propagation. Subsequently, two case studies, i.e. weld control and injector fluid flow rate control, are provided to demonstrate how to apply the basic statistical theories and design of experiments to the quality control and improvement of products. It is also demonstrated that Monte Carlo simulation methods play a significant role in variability characterization and quality control/improvement.