Control Charts For Efficient Process Monitoring

A timely detection of assignable causes plays a significant role in the performance of any process. Control charts are effective process monitoring tools which help differentiating between natural and assignable causes of variations. It is always desirable to have an effi-cient design structure of control charts for an improved monitoring of process parameters. This thesis contributes some improved control charting structures to be used as add-in for Statistical Process Control (SPC) toolkit. The proposed charting structures are designed for location and scale parameters using the information on some auxiliary characteristics. The performance ability of the proposals is evaluated in terms of some useful measures including average run length (ARL), extra quadratic loss (EQL), average time to signal (ATS), average extra quadratic loss (AEQL), Relative ARL (RARL) and Run Length (RL) properties such as:Median Run Length (MDRL), Standard Deviation of Run Length dis-tribution (SDRL). These performance measures are examined for normal, gamma and t distributed process environments (with and without contaminations) using simple random and double sampling schemes. We have investigated and compare the performance of dif-ferent proposed charting structures using extensive Monte Carlo simulations. We have also included some real situations in order to highlight the practical application of the proposals covered in this study.