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 text provides the reader with a general and widely-applicable problem solving strategy for use in quality improvement. It covers a variety of statistical and "non-statistical" problem-solving tools, and discusses techniques that are useful when problems are solved by groups or teams of people. It also shows how the success of problem solving is influenced by the style of management and the type of management-employee interaction.
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. Anyone involved in the production foods will find this book a valuable guide for assuring the safety and uniformity of food production through application of the latest techniques in process quality control. Specifically, this text can be used effectively by those skilled in the field for reference; by entry level technicians as a training aid; and by upper management to enhance their understanding of this highly specialized field. It can also be studied by operating and service departments to assist them in total quality control efforts.
"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).
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.
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".
Report of a Research and Training Project Conducted by the Syracuse University Institute of Industrial Research, in Cooperation with the Syracuse Chapter of the American Society for Quality Control ...
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.
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.
Hans-Joachim Lenz,G. Barry Wetherill,Peter-Th. Wilrich
Author: Hans-Joachim Lenz,G. Barry Wetherill,Peter-Th. 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.
Provides the methods and tools for the manufacturing manager to improve quality, increase productivity, and enhance the competitive position of the manufacturing line. Proposes potentially controversial methods of performance appraisals, operation certification, line qualification, vendor certification, and just-in-time manufacturing. The organization of this book takes the reader logically from the basics of statistics through the fundamentals of statistical quality control, to the manufacturing applications and accompanying manufacturing strategies of statistical quality control (SQC). This book is the first written specifically for manufacturing management. Examples throughout the book demonstrate how the manufacturing manager can successfully implement SQC in the manufacturing process. Real-life manufacturing situations described illustrate situations managers are likely to find in their own line.
This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-art concepts and applications. The objective is to give the reader a sound understanding of the principles and the basis for applying them in a variety of both product and nonproduct situations. While statistical techniques are emphasized throughout, the book has a strong engineering and management orientation. Guidelines are given throughout the book for selecting the proper type of statistical technique to use in a wide variety of product and nonproduct situations. By presenting theory, and supporting the theory with clear and relevant examples, Montgomery helps the reader to understand the big picture of important concepts. Updated to reflect contemporary practice and provide more information on management aspects of quality improvement.
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.
This basic, step-by-step guide for SPC has been revised to include the most up-to-date information on control charts, capability analysis, and statistical problem solving. Practice problems and answers allow the reader to work through SPC exercises. This second edition contains new information on the following topics: process control planning, short-run charts for variables and attributes, gage R & R, regression and correlation analysis, testing variances, and problem-solving tools.
For courses in Statistical Process Control, and for use in Six Sigma and Certified Quality Improvement Associate training programs. Utilizing a practical how-to approach, this text shows students how to apply the principles of SPC to the making of business decisions and better quality products. It integrates examples that use Microsoft Excel functions and Minitab.