Customers are placing ever increasing demands on automotive part manufacturers for high quality parts at low cost. Increasingly, the demand is for zero defects or defect rates in the less than one part per billion. This creates a significant challenge for manufacturers as to how to achieve these low defect levels economically while producing large volumes of parts. Importantly, the presence of infrequent process and measurement (gauge) events can adversely affect product quality. This thesis uses a statistical mixture model that allows one to assume a main production process that occurs most of the time, and secondary rogue events that occur infrequently. Often the rogue events correspond to necessary operator activity, like equipment repairs and tooling replacement. The mixture model predicts that some gauge observations will be influenced by combinations of these rogue events. Certain production applications, like those involving feedback or high-reliability gauging, are heavily influenced by rogue events and combinations of rogue events. A special runtime software profiler was created to collect information about rogue events, and statistical techniques (rogue event analysis) were used to estimate the waste generated by these rogue events. The value of these techniques was successfully demonstrated in three different industrial automotive part production applications. Two of these systems involve an automated feedback application with Computer Numerically Controlled (CNC) machining centers and Coordinate Measuring Machine (CMM) gauges. The third application involves a high-reliability inspection system that used optical, camera-based, machine-vision technology. The original system accepted reject parts at a rate of 98.7 part per million (ppm), despite multiple levels of redundancy. The final system showed no outgoing defects on a 1 million part factory data sample, and a 100 million part simulated data sample. It is expected that the final system reliability will meet the 0.001 ppm specification, which represents a huge improvement. / Doctor of Philosophy (PhD)