Statistical control by monitoring and feedback adjustment
A detailed, practical, accessible guide to efficient statistical control.
Efficient control is a key element in the improvement and maintenance of quality and productivity. This book shows the advantages of bringing together the more commonly used methods of statistical quality control with appropriate techniques of feedback adjustment. It uses recent research and practical experience to provide feedback methods of immediate use in the workplace.
Statistical Control by Monitoring and Feedback Adjustment introduces a new coordinated approach to quality control. The authors' clear and cogent presentation uses extensive graphical explanation supplemented by numerous examples and computational tables. A helpful selection of problems and solutions further facilitates understanding.
Topics covered include:
* A fresh look at process monitoring
* Using feedback adjustment charts
* Minimizing the size of adjustments
* Feedback techniques that minimize costs of adjustment and sampling
* Detection of special causes with Cuscore Statistics
* Efficient monitoring of operating feedback systems
* The roles of models, optimization, and robustness
* A brief review of time series analysis.
Statistical Control by Monitoring and Feedback Adjustment is important reading for quality control engineers and statisticians as well as graduate students in quality control, industrial engineering, and applied statistics.
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Control Charts for Frequencies and Proportions
Control Charts for Measurement Data
Modeling Process Dynamics and Forecasting Using
11 other sections not shown
action limits adjustment equation Analysis Appendix applied appropriate approximately assumption autoregressive model binomial distribution calculated Chapter CM CM CM color index control chart control scheme corresponding cost Cuscore statistic dead band deviation from target discrete discussed disturbance dynamic estimate et-i EWMA example exponentially weighted feedback control first-order forecast errors illustration IMA model increase limit lines linear mean square error measurements method MMSE moving average moving range nonstationary normal distribution observations obtained occur output standard deviation overdispersion parameter particular plotted Poisson distribution probability procedure process adjustment process monitoring produce random reference distribution represented residuals robustness score statistics Second Edition series model Shewhart chart shown in Figure shows signal smoothing constant special causes stationary statistically independent Suppose Table target value temperature tion unit interval variable variance variation variogram white noise white noise series zero zt-i zt+i