Inhaltsangabe:
The Deming philosophy of statistical process control has revolutionized quality control in Japan. In the US though, the general thrust has been to attempt some sort of synthesis between the quality assurance "protocols developed during World War II and the quality improvement" paradigm of Deming. The authors' empirical experience indicates that Deming's approach, based as it is on the importance of contamination from assignable causes, conforms more closely to the way that most processes work in industrial service settings. This text provides models which demonstrate the utility of statistical process control. It treats quality control as a technical solution to problems in optimization and productivity, and includes exploratory data analytic techniques, multivariate approaches, and robustness considerations. Developed from a series of short courses given to companies both in the US and in Poland, the first part of the book can be readily handled by persons at all levels of mathematical ability. As the book progresses, more mathematics is employed, creating a work useful to quality control professionals, applied statisticians, and engineers. The well-worked examples and an appendix which develops the statistical methods necessary to make the book a self-contained text assure that the book can be used as a one semester text at the upper level undergraduate and graduate levels. This book should be of interest to quality control professionals, applied statisticians and engineers.
Reseña del editor:
The Deming philosophy of statistical process control has revolutionized quality control in Japan. In the US though, the general thrust has been to attempt some sort of synthesis between the quality assurance "protocols developed during World War II and the quality improvement" paradigm of Deming. The authors' empirical experience indicates that Deming's approach, based as it is on the importance of contamination from assignable causes, conforms more closely to the way that most processes work in industrial service settings. This text provides models which demonstrate the utility of statistical process control. It treats quality control as a technical solution to problems in optimization and productivity, and includes exploratory data analytic techniques, multivariate approaches, and robustness considerations. Developed from a series of short courses given to companies both in the US and in Poland, the first part of the book can be readily handled by persons at all levels of mathematical ability. As the book progresses, more mathematics is employed, creating a work useful to quality control professionals, applied statisticians, and engineers. The well-worked examples and an appendix which develops the statistical methods necessary to make the book a self-contained text assure that the book can be used as a one semester text at the upper level undergraduate and graduate levels. This book should be of interest to quality control professionals, applied statisticians and engineers.
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