Shift Detection and Process Adjustments

One of the key characteristics to be monitored is the mean of the quality characteristic of interest. Even though methods such as design of experiments are used to determine the optimum setting of the process parameters to ensure that the mean of the quality characteristic is within the specified levels, the mean may gradually increase/decrease in linear/nonlinear fashion. This phenomenon is quite related to the change-point detection research that has been investigated by others. In this book, we develop statistical procedures to monitor and adjust such trended processes. We develop a new control chart approach for linear trend detection in the process mean. Furthermore, we introduce a procedure to detect the drift time of processes subject to linear and nonlinear trend. The procedure is effective in detecting the drift time as early as possible. Unlike the traditional statistical process control procedure where the process is investigated once the SPC chart signals that the process is out-of-control but no corrective action is determined, we develop a new adjustment procedure based on the maximum likelihood estimate of the drift time to keep the process on target. This work bridges the gap between theory and practice in process control and should be especially useful to process engineers and professionals in SPC field.

Autorentext
Currently works as a Senior Statistician at Merck&Co. Inc., USA. Dr. Fahmy is a Senior Member of American Society for Quality (ASQ) and Institute of Industrial Engineers (IIE). His research interests include statistical process control, multivariate analysis and design of experiments.