- 771 pages
- English
- PDF
- Available on iOS & Android
Introduction to Statistical Quality Control
About this book
Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.
Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences. A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, and incorporation of Minitab statistics software, provides students with a solid base of conceptual and practical knowledge.
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Key takeaways
Apply the Six Sigma DMAIC framework to systematically define, measure, analyze, improve, and control processes for quality enhancement in diverse settings. This includes utilizing foundational statistical methods to model and make inferences about process quality.
Construct and interpret various statistical process control charts, including those for variables and attributes, to monitor process stability and identify out-of-control conditions. Perform process and measurement system capability analyses to evaluate performance against specifications.
Design and analyze factorial and fractional factorial experiments to optimize processes and achieve quality improvement. Implement advanced statistical process monitoring techniques, such as CUSUM, EWMA, and multivariate control charts, and apply acceptance sampling methods for quality assurance.
Information
Table of contents
- Cover
- Title Page
- Copyright
- About the Author
- Preface
- Contents
- Part 1 Introduction
- Part 2 Statistical Methods Useful in Quality Control and Improvement
- Part 3 Basic Methods of Statistical Process Control and Capability Analysis
- Part 4 Other Statistical Process-Monitoring and Control Techniques
- Part 5 Process Design and Improvement with Designed Experiments
- Part 6 Acceptance Sampling
- Appendix
- Bibliography
- Index
- EULA
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