While many books on quality espouse the Taguchi loss function, they do not examine its impact on statistical quality control (SQC). But using the Taguchi loss function sheds new light on questions relating to SQC and calls for some changes. This book covers SQC in a way that conforms with the need to minimize loss. Subjects often not covered elsewhere include: (i) measurements, (ii) determining how many points to sample to obtain reliable control charts (for which purpose a new graphic tool, diffidence charts, is introduced), (iii) the connection between process capability and tolerances, (iv) how to adapt Deming's kp rule to quadratic loss, (v) how to adjust without tampering. We also discuss the efficiency of various statistics and how control chart constants are derived.
It has recently become apparent that "quality" is quickly becoming the single most important factor for success and growth in business. Companies achieving higher quality in their products through effective quality improvement programs enjoy a significant competitive advantage. It is, therefore, essential for engineers responsible for design, development, and manufacture of products to understand the concepts and techniques of quality control. Statistical Quality Control imparts that understanding. Covering the basic steps in quality assurance and control methodologies, this unique text not only sequences, but also integrates the various techniques presented. The chapters, which include Optimum Process Means and Process Setting, are arranged in logical order. This advanced treatment makes Statistical Quality Control an ideal graduate text as well as a reference for practitioners working in design and quality control.
"This book is a desk reference and instructional aid for individuals 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."--BOOK JACKET.
The new edition of the best-selling reference on statistical quality control has been updated to include definitions re-written for a wider audience to grasp the meaning of technical terms. These definitions also parallel national and international standards and are categorized into sections that make it easy to identify by subject matter. Terms have been extensively cross-referenced and alphabetized in one handy reference along with a comprehensive collection of statistical tables that make it easy to access all of the information needed for statistical calculation. New items added to this edition include a guide for control chart selection and g and h control charts. Basic statistical measures and equation examples make this an outstanding resource for every quality professional as well as a great resource for preparing for the Certified Quality Engineer, Certified Mechanical Inspector, and Certified Quality Technician's exams. Preview a sample chapter from this book along with the full table of contents by clicking here. You will need Adobe Acrobat to view this pdf file.
In the 1920's, Walter Shewhart visualized that the marriage of statistical methods and manufacturing processes would produce reliable and consistent quality products. Shewhart (1931) conceived the idea of statistical process control (SPC) and developed the well-known and appropriately named Shewhart control chart. However, from the 1930s to the 1990s, literature on SPC schemes have been "captured" by the Shewhart paradigm of normality, independence and homogeneous variance. When in fact, the problems facing today's industries are more inconsistent than those faced by Shewhart in the 1930s. As a result of the advances in machine and sensor technology, process data can often be collected on-line. In this situation, the process observations that result from data collection activities will frequently not be serially independent, but autocorrelated. Autocorrelation has a significant impact on a control chart: the process may not exhibit a state of statistical control when in fact, it is in control. As the prevalence of this type of data is expected to increase in industry (Hahn 1989), so does the need to control and monitor it. Equivalently, literature has reflected this trend, and research in the area of SPC with autocorrelated data continues so that effective methods of handling correlated data are available. This type of data regularly occurs in the chemical and process industries, and is pervasive in computer-integrated manufacturing environments, clinical laboratory settings and in the majority of SPC applications across various manufacturing and service industries (Alwan 1991).
This 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.
The twenty-three papers in this volume are carefully selected, reviewed and revised for this volume, and are divided into two parts: Part 1: "On-line Control" with subchapters 1.1 "Control Charts" and 1.2 "Surveillance Sampling and Sampling Plans" and Part 2:"Off-line Control".
The intensive use of automatic data acquisition system and the use of cloud computing for process monitoring have led to an increased occurrence of industrial processes that utilize statistical process control and capability analysis. These analyses are performed almost exclusively with multivariate methodologies. The aim of this Brief is to present the most important MSQC techniques developed in R language. The book is divided into two parts. The first part contains the basic R elements, an introduction to statistical procedures, and the main aspects related to Statistical Quality Control (SQC). The second part covers the construction of multivariate control charts, the calculation of Multivariate Capability Indices.
In Canada, acceptance sampling has been used in legal metrology applications for nearly four decades. One of its principal uses has been in the quality control of utility meters that measure electricity or natural gas supplied to consumers. By law, such meters must be inspected for conformance to specification requirements prior to use and be periodically inspected while huse. With few exceptions, due to the numerous utility companies in the country and their varied practices, the meters exist in the form of isolated lots for inspection purposes. The proportion of nonconforming meters in a lot has traditionally defined lot quality for utility meter sampling inspection purposes. Another principal application of acceptance sampling has been in the quality control of the net contents of packaged products sold in the marketplace. Such products include those sold on the basis of such measures as weight, volume, length, and area. In this particular application, products are also usually inspected on an isolated-lot basis for regulatory purposes. However, lot quality is usually measured on the basis of two criteria for such products: the proportion of nonc- forming packages in the lot and the lot mean quantity. This section reviews Canadian quality control practices in these two areas of application, highlighting some of the deficiencies and issues. Three-class s- pling plans are proposed as a possible solution to some of these deficiencies and issues.
Primarily intended for the undergraduate students of industrial, production, mechanical and manufacturing engineering, and postgraduate students of industrial, quality engineering and management and industrial engineering and management, this book fills the gap between theory and practice of tools and techniques of quality control and quality improvement. In this book, the principles and concepts are presented clearly and logically with necessary numerical illustrations to reinforce the understanding of the subject matter. The book is organized in two parts. Part I deals with statistical quality control. It starts with the fundamentals of statistics and quality followed by elaborate discussion on statistical process control, process and gauge capability studies with emphasis on their practical application. It also covers detailed discussion on the various types of control charts used to monitor and control quality of processes and products. It includes acceptance sampling inspection procedures and standard sampling systems. Part II deals with quality improvement techniques/methods. It is a data driven approach that discusses the application of Design of Experiments and Taguchi Methods for improving quality of processes and products. A comprehensive discussion on total quality management is also presented. KEY FEATURES • Provides a well structured procedure for the application of all the tools and techniques. • Includes Shainin DOE tools widely used in Six sigma projects. • Demonstrates the application of quality improvement techniques through real life case studies.
Statistical process control is a tool which enables both manufacturers and suppliers to achieve control of product quality by applying statistical methods to controlling processes. This guide provides an introduction to the concept.
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.
Hans-Joachim Lenz,G. Barry Wetherill,Peter-Theodor Wilrich
Author: Hans-Joachim Lenz,G. Barry Wetherill,Peter-Theodor Wilrich
Publisher: Springer Science & Business Media
Like the first three volumes, published in 1981, 1984 and 1987 and met with a lively response, the present volume is collecting contributions stressed on methodology or successful industrial applications. The papers are classified under three main headings; sampling inspection, process quality control and experimental design. In the first group there are nine papers on acceptance sampling. The second large group of papers deal with control charts and process control and the third group of papers includes contributions on experimental design.
The trusted guide to the statistical methods for quality control. Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Introduction to Statistical Quality Control, Sixth Edition gives you a sound understanding of the principles of statistical quality control (SQC) and how to apply them in a variety of situations for quality control and improvement. With this text, you′ll learn how to apply state–of–the–art techniques for statistical process monitoring and control, design experiments for process characterization and optimization, conduct process robustness studies, and implement quality management techniques. You′ll appreciate the significant updates in the Sixth Edition including: ∗ In–depth attention to DMAIC, the problem–solving strategy of Six Sigma. It will give you an excellent framework to use in conducting quality improvement projects. ∗ New examples that illustrate applications of statistical quality improvement techniques in non–manufacturing settings. Many examples and exercises are based on real data. ∗ New developments in the area of measurement systems analysis ∗ New features of Minitab V15 incorporated into the text ∗ Numerous new examples, exercises, problems, and techniques to enhance your absorption of the material
The main focus of this edited volume is on three major areas of statistical quality control: statistical process control (SPC), acceptance sampling and design of experiments. The majority of the papers deal with statistical process control, while acceptance sampling and design of experiments are also treated to a lesser extent. The book is organized into four thematic parts, with Part I addressing statistical process control. Part II is devoted to acceptance sampling. Part III covers the design of experiments, while Part IV discusses related fields. The twenty-three papers in this volume stem from The 11th International Workshop on Intelligent Statistical Quality Control, which was held in Sydney, Australia from August 20 to August 23, 2013. The event was hosted by Professor Ross Sparks, CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia and was jointly organized by Professors S. Knoth, W. Schmid and Ross Sparks. The papers presented here were carefully selected and reviewed by the scientific program committee, before being revised and adapted for this volume.
Maintaining the reader-friendly features of its popular predecessor, the Second Edition illustrates fundamental principles and practices in statistical quality control for improved quality, reliability, and productivity in the management of production processes and industrial and business operations. Presenting key concepts of statistical quality control in a simple and straightforward manner, this reference will provide a solid foundation in statistical quality control theory, background, and applications. Moving from elementary topics to sampling by variables, sound tolerancing, and relationships between variables, this reference