Control charts are widely used in industry to monitor processes that are far from Zero-Defect (ZD), and their use in a near Zero-Defect manufacturing environment poses many problems. This book presents techniques of using control charts for high-quality processes, and some recent findings and applications of statistical control chart techniques for ZD processes are presented. A powerful technique based on counting of the cumulative conforming (CCC) items between two nonconforming ones is discussed in detail. Extensions of the CCC chart are described, as well as applications of cumulative sum and exponentially weighted moving average techniques to CCC-related data, multivariate methods, economic design of control chart procedures, and modeling and analysis of trended but regularly adjusted processes. Many examples, charts, and procedures, are presented throughout the book, and references are provided for those interested in exploring the details. A number of questions and issues are posed for further investigations. Researchers and students may find many ideas in this book useful in their academic work, as a foundation is laid for the exploration of many further theoretical and practical issues.

Control chart are widely used to establish and maintain statistical control of a process. In other words it is a tool used to monitor processes and to assure that they remain "In Control" or stable The vital function of control chart is to find out the occurrence of assignable causes so that the necessary corrective action can be taken before a huge amount of defective product is produced. The X-bar control chart is preferred most in comparison to any other control chart technique if quality is measured on a regular scale. In the present project, we here develop the economic design of the X-bar control chart using Genetic Algorithm to determine the values of the sample size, sampling interval, width of control limits such that the expected total cost per hour is minimized. The genetic algorithms (GA) are applied to search for the optimal values of the three test parameters of the X-bar chart. In genetic algorithm we use mutation and cross-over technique to get the optimal solution. Finally, a sensitivity analysis will be carried out to investigate the effect of model parameters on the solution of the economic design.