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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.
"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."--
Important text offers lucid explanation of how to regulate variables and maintain control over statistics in order to achieve quality control over manufactured products, crops and data. First inexpensive paperback edition.
Specifically targeted at the food industry, this state-of-the-art text/reference combines all the principal methods of statistical quality and process control into a single, up-to-date volume. In an easily understood and highly readable style, the author clearly explains underlying concepts and uses real world examples to illustrate statistical techniques. This Third Edition maintains the strengths of the first and second editions while adding new information on Total Quality Management, Computer Integrated Management, ISO 9001-2002, and The Malcolm Baldrige Quality Award. There are updates on FDA Regulations and Net Weight control limits, as well as additional HACCP applications. A new chapter has been added to explain concepts and implementation of the six-sigma quality control system.
STATISTICAL QUALITY CONTROL Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectors This book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept. Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide also Focuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the field Discusses aspects of Six Sigma Methodology Teaches readers to use MINITAB, R, JMP and Python to create and analyze charts Requires no previous knowledge of statistical theory Is supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problems Statistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.
This book is a desk reference and instructional aid for those individuals currently involved with, or preparing for involvement with, Six Sigma project teams. As Six Sigma team members, Green Belts help select, collect data for, and assist with the interpretation of a variety of statistical or quantitative tools within the context of the Six Sigma methodology. The second in a four-book series geared specifically for these Green Belt activities, this book provides a thorough discussion of statistical quality control (SQC) tools. These tools are introduced and discussed from the perspective of application rather than theoretical development. From this perspective, readers are taught to consider the SQC tools as statistical “alarm bells” that send signals when there are one or more problems with a particular process. Guidance is also given on the use of Minitab and JMP in doing these various SQC applications. In addition, examples and sample problems from all industries appear throughout the book to aid a Green Belt's comprehension of the material.
The business, commercial and public-sector world has changed dramatically since John Oakland wrote the first edition of Statistical Process Control – a practical guide in the mid-eighties. Then people were rediscovering statistical methods of ‘quality control’ and the book responded to an often desperate need to find out about the techniques and use them on data. Pressure over time from organizations supplying directly to the consumer, typically in the automotive and high technology sectors, forced those in charge of the supplying production and service operations to think more about preventing problems than how to find and fix them. Subsequent editions retained the ‘took kit’ approach of the first but included some of the ‘philosophy’ behind the techniques and their use. The theme which runs throughout the 7th edition is still processes - that require understanding, have variation, must be properly controlled, have a capability, and need improvement - the five sections of this new edition. SPC never has been and never will be simply a ‘took kit’ and in this book the authors provide, not only the instructional guide for the tools, but communicate the management practices which have become so vital to success in organizations throughout the world. The book is supported by the authors' extensive and latest consulting work within thousands of organisations worldwide. Fully updated to include real-life case studies, new research based on client work from an array of industries, and integration with the latest computer methods and Minitab software, the book also retains its valued textbook quality through clear learning objectives and end of chapter discussion questions. It can still serve as a textbook for both student and practicing engineers, scientists, technologists, managers and for anyone wishing to understand or implement modern statistical process control techniques.
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon
Would you like to learn more about using Excele? Are looking for an easier way to solve statistical quality control problems? Statistical Quality Control (SQC) experts Steven Zimmerman and Marjorie Icenogle have updated their guide that combines the power of each into this one integrated book and CD-Rom. While this introduction to SQC is for beginners to either quality or to the software product Excel, those with a more advanced knowledge of SQC and Excel will also find this book valuable to incorporate the two. New versions of Excel make creating quality control spreadsheets easier than ever. This new edition contains spreadsheets for: exponential weighted moving average charts, moving sum of the sample statistic control charts, cumulative sum charts, reliability problems, and information on using the Pivot Table and Pivot Chart functions in Excel. This guide begins with introductions to the concepts of SQC and the use of spreadsheets. a review of Exceles features is followed by explanations of statistical distribution, outliers, and the analysis required for SQC methods. Basic statistical process control methods such as p and np control charts, c and u control charts, and Pareto charts are demonstrated. Descriptions on acceptance sampling methods including binomial and hypergeometric distributions and average outgoing quality curves are also offered. Also new to this package is direction on using Excel in power point presentations. Practice problems, definitions of key terms, detailed graphics, and end-of-chapter summaries help make this book an outstanding tool to combine SQC with Excel software.