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"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, provides students with a solid base of conceptual and practical knowledge."--
Revised and expanded, this Second Edition continues to explore the modern practice of statistical quality control, providing comprehensive coverage of the subject from basic principles to state-of-the-art concepts and applications. The objective is to give the reader a thorough grounding in the principles of statistical quality control and a basis for applying those principles in a wide variety of both product and nonproduct situations. Divided into four parts, it contains numerous changes, including a more detailed discussion of the basic SPC problem-solving tools and two new case studies, expanded treatment on variable control charts with new examples, a chapter devoted entirely to cumulative-sum control charts and exponentially-weighted, moving-average control charts, and a new section on process improvement with designed experiments.
This book presents an organized approach to quality management, control, and improvement. Because quality problems usually are the outcome of uncontrolled or excessive variability, statistical tools and other analytical methods play an important role in solving these problems. However, these techniques need to be implemented within a management structure that will ensure success. This text focuses on both the management structure and the statistical and analytical tools. It organizes and presents this material according to many years of teaching, research, and professional practice across a wide range of business and industrial settings.
Master Statistical Quality Control using JMP ! Using examples from the popular textbook by Douglas Montgomery, Introduction to Statistical Quality Control: A JMP Companion demonstrates the powerful Statistical Quality Control (SQC) tools found in JMP. Geared toward students and practitioners of SQC who are using these techniques to monitor and improve products and processes, this companion provides step-by-step instructions on how to use JMP to generate the output and solutions found in Montgomery’s book. The authors combine their many years of experience as passionate practitioners of SQC and their expertise using JMP to highlight the recent advances in JMP’s Analyze menu, and in particular, Quality and Process. Key JMP platforms include: Control Chart Builder CUSUM Control Chart Control Chart (XBar, IR, P, NP, C, U, UWMA, EWMA, CUSUM) Process Screening Process Capability Measurement System Analysis Time Series Multivariate Control Chart Multivariate and Principal Components Distribution For anyone who wants to learn how to use JMP to more easily explore data using tools associated with Statistical Process Control, Process Capability Analysis, Measurement System Analysis, Advanced Statistical Process Control, and Process Health Assessment, this book is a must!
This book covers the foundations of modern methods of quality control and improvement that are used in the manufacturing and service industries. Quality is key to surviving tough competition. Consequently, business needs technically competent people who are well-versed in statistical quality control and improvement. This book should serve the needs of students in business and management and students in engineering, technology, and other related disciplines. Professionals will find this book to be a valuable reference in the field.
The book presents a qualitative and quantitative approach to understand, manage and enforce the integration of statistical concepts into quality control and quality assurance methods. Utilizing a sound theoretical and practical foundation and illustrating procedural techniques through scientific examples, this book bridges the gap between statistical quality control, quality assurance and quality management. Detailed procedures have been omitted because of the variety of equipment and commercial kits used in today's clinical laboratories. Instrument manuals and kit package inserts are the most reliable reference for detailed instructions on current analytical procedures.
Avedis Donabedian's name is synonymous with quality of medical care. He unraveled the mystery behind the concept by defining it in clear operational terms and provided detailed blueprints for both its measurement(known as quality assessment) and its improvement(known as quality assurance). Many before him claimed that quality couldn't be defined in concrete objective terms. He demonstrated that quality is an attribte of a system which he called structure, a set of organized activities whihc he called process, and an outcome which results from both. In this book Donabedian tells the full story of quality assessment and assurance in simple, clear terms. He defines the meaning of quality, explicates its components, and provides clear and systematic guides to its assessment and enhancement. His style is lucid, succinct, systematic and yet personal, almost conversational.