Introduction to statistical quality control/ Douglas C. Montgomery

Includes bibliography and index

Chapter 1: Quality improvement in the modern business environment -- Part 1: Statistical methods useful in quality improvement -- Chapter 2: Modeling process quality -- Chapter 3: Inferences about process quality -- Part 2: Statistical process control -- Chapter 4: Methods and philosophy of statistical process control -- Chapter 5: Control charts for attributes -- Chapter 6: Control charts for variables -- Chapter 7: Cumulative-sum and exponentially weighted moving- average control charts -- Chapter 8: Other statistical process- control techniques -- Chapter 9: Process- capability analysis -- Chapter 10: Economic design of control charts -- Part III: Process improvement with designed experiments -- Chapter 11: The fundamentals of experimental design -- Chapter 12: Factorial experiments and methods for process improvement -- Part IV: Acceptance sampling -- Chapter 13: Lot-by-lot acceptance sampling for attributes -- Chapter 14: Acceptance sampling by variables -- Chapter 15: Other acceptance-sampling procedures

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.