SPC Methods for Quality Improvement

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9780471130871: SPC Methods for Quality Improvement

SPC METHODS FOR QUALITY IMPROVEMENT

A comprehensive, applications-oriented guide to classical and cutting-edge SPC tools and techniques

Written by a leading innovator in the field, SPC Methods for Quality Improvement provides a complete blueprint for integrating SPC methods into the manufacturing process. It explains methods for improving existing SPC systems and describes cutting-edge techniques that enable managers to develop full-fledged SPC systems in industries that traditionally were considered off-limits to this type of statistical analysis.

The only guide to SPC geared exclusively to the practical concerns of manufacturing professionals, it translates statistical/mathematical concepts into real-world applications with the help of dozens of case studies and examples drawn from a variety of industries.

SPC Methods for Quality Improvement is also a superb introductory text for students and newcomers to SPC. The author patiently introduces readers to essential SPC concepts and procedures and provides methodical, step-by-step instruction in the proper use of SPC tools and techniques.

In the 1920s and 30s, Walter Shewhart of Bell Telephone Laboratories developed Statistical Process Control (SPC) as a means of analyzing manufacturing processes at the shop-floor level. Shewhart and his disciples—most notably W. Edwards Deming, father of total quality management—realized that SPC provided a sophisticated tool for assessing and improving quality at all levels. SPC, therefore, was the backbone of the quality management revolution of the 1980s and 90s. Yet, until now, there was no comprehensive, practical guide to SPC methods for engineers and managers working in manufacturing.

SPC Methods for Quality Improvement fills that vacuum with complete coverage of SPC concepts, tools, and techniques geared to the practical concerns of manufacturing professionals. Dr. Charles Quesenberry introduces all statistical/mathematical essentials and carefully explains the rationale behind each concept. He employs vivid case studies to show how these concepts translate into real-world applications. Using examples drawn from a broad array of industries—from semiconductors to food processing, biomedical engineering to education—he deftly illustrates how SPC methods can streamline the manufacturing process and improve product quality.

SPC Methods for Quality Improvement provides detailed, step-by-step guidance on the uses of both classical and second-generation SPC methods. Among cutting-edge methods described are those for charting processes without prior data, charting processes from start-up, and charting short runs with known false alarm rates. Readers also learn methods for studying the form of a reference distribution; how to use transformations to Q-statistics for various models; how to treat data from skewed distributions; and new ways of treating regression, multivariate, and autocorrelated data.

An excellent text/primer for students and those new to SPC, SPC Methods for Quality Improvement is also a valuable guide for industrial and production engineers and managers who wish to improve existing SPC systems or to introduce SPC methods into industries where they were once inapplicable.

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From the Publisher:

Written for courses in SPC methods at both the undergraduate and graduate level, this book covers SPC from a statistical mathematical perspective while at the same time addressing cutting-edge problems and technologies.

From the Inside Flap:

In the 1920s and ’30s, Walter Shewhart of Bell Telephone Laboratories developed Statistical Process Control (SPC) as a means of analyzing manufacturing processes at the shop-floor level. Shewhart and his disciples—most notably W. Edwards Deming, father of total quality management—realized that SPC provided a sophisticated tool for assessing and improving quality at all levels. SPC, therefore, was the backbone of the quality management, revolution of the 1980s and ’90s. Yet, until now, there was no comprehensive, practical guide to SPC methods for engineers and managers working in manufacturing. SPC Methods for Quality Improvement fills that vacuum with complete coverage of SPC concepts, tools, and techniques geared to the practical concerns of manufacturing professionals. Dr. Charles Quesenberry introduces all statistical/mathematical essentials and carefully explains the rationale behind each concept. He employs vivid case studies to show how these concepts translate into real-world applications. Using examples drawn from a broad array of industries—from semiconductors to food processing, biomedical engineering to education—he deftly illustrates how SPC methods can streamline the manufacturing process and improve product quality. SPC Methods for Quality Improvement provides detailed, step-by-step guidance on the uses of both classical and second-generation SPC methods. Among cutting-edge methods described are those for charting processes without prior data, charting processes from start-up, and charting short runs with known false alarm rates. Readers also learn methods for studying the form of a reference distribution; how to use transformations to Q-statistics for various models; how to treat data from skewed distributions; and new ways of treating regression, multivariate, and autocorrelated data. An excellent text/primer for students and those new to SPC. SPC Methods for Quality Improvement is also a valuable guide for industrial and production engineers and managers who wish to improve existing SPC systems or to introduce SPC methods into industries where they were once inapplicable.

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Quesenberry, Charles P.
Editore: Wiley (1997)
ISBN 10: 0471130877 ISBN 13: 9780471130871
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Murray Media
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Descrizione libro Wiley, 1997. Hardcover. Condizione libro: New. Never used!. Codice libro della libreria P110471130877

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Quesenberry, Charles P.
Editore: Wiley
ISBN 10: 0471130877 ISBN 13: 9780471130871
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Descrizione libro Wiley. Hardcover. Condizione libro: New. 0471130877 New Condition. Codice libro della libreria NEW7.1111610

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