Statistical Process Control Analysis - Sheikh Abdul Kadir Hagi, Ibrahim (1999)
This thesis is concerned with the investigation of the two key aspects of statistical process control. The first aspect is maintaining a stable process so that the pattern of variation of process out-put is not changing. In order to maintain a stable process, the study includes an examination of the state of control of the process. A traditional variable control charts, x and R charts and also the x control chart based on sample median and median control chart in conjunction with a chart for sample range were used for both normal and non-normal process. The second aspect depicts the process capability. Assuming that the processes have reached the state of statistical control, capability measurements were proceeded in this study for both normal and non-normal processes. A simulation studies are carried out to compare the performance of the traditional and the robust control chart. Likewise, the classical capability index is compared to two robust capability index. The results of the study indicate that the traditional and the robust control chart are equally good when no contamination in the data. However, the later performs better than the former in the presence of outliers in the data. Similarly, the traditional process capability index are almost as good as the robust capability index as proposed by Clement (1989) and John Kot (1993) in a well behaved data. Nevertheless, the robust capability index were found to be better compared to the traditional index when contamination occurs in the data. The study also carried out an investigation of properties of the three types of bootstrap confidence interval for estimating the process capability index (Cpk)' namely the standard, percentile and bias corrected and accelerated for two processes (normal and skewed). The average lengths displayed a consistent pattern where the longest intervals were the standard intervals, and with the shortest intervals being the percentile and bias corrected and accelerated intervals for both normal and highly skewed processes. The results of the study seem to be consistent for sample size n = 25 to n = 50.