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

This handbook is designed to help candidates preparing for the ASQ Six Sigma Green Belt certification exam. Meant for those who already understand the basic concepts of reducing variation and improving processes, it also serves as a helpful reference to the appropriate materials needed to conduct successful Green Belt projects. The layout of the handbook is mapped to the 2022 version of ASQ’s Body of Knowledge (BoK). This revised edition includes new information about: • SMART goals, key process indicators, Takt time, just-in-time processes, and spaghetti diagrams • The Kano model, risk management, business continuity planning, SWOT analysis, and RACI charts • Data collection plans and quality checks • Gap analysis, 5 Whys analysis, and fault tree analysis • Maintaining quality improvements • Document control, audits, training plans, the PDCA cycle, Andon, and Jidoka system

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.

Professor Woodall’s essay shows that this book represents a remarkable contribution, even by today’s standards, because of its contemporary thinking about the relationship between the specific topic of SQC and the broader company context of Quality Management. It also demonstrates the remarkable awareness of at least some young US engineers in the post-war period about the vital role of Statistical Quality Control in establishing and maintaining a competitive position. The book reveals that there was unsuspected knowledge extant immediately post-war, about the importance of Statistical Quality Control when appropriately applied in an industrial setting. It also helps to correct wide-spread historical misconceptions about who specifically was responsible for helping Japanese industry get back on its feet post-war, a task assigned to General Douglas Macarthur by President Truman and how Macarthur was indebted to Sarasohn.

This book, designed for students taking a basic introductory course in statistical analysis, is far more than just a book of tables. Each table is accompanied by a careful but concise explanation and useful worked examples. Requiring little mathematical background, Elementary Statistics Tables is thus not just a reference book but a positive and user-friendly teaching and learning aid. The new edition contains a new and comprehensive "teach-yourself" section on a simple but powerful approach, now well-known in parts of industry but less so in academia, to analysing and interpreting process data. This is a particularly valuable enabler to personnel who are not qualified in traditional statistical methods to actively contribute to quality-improvement projects. The second edition also includes a much-improved glossary of symbols and notation.

Pierre Gy's Sampling Theory and Sampling Practice, Second Edition is a concise, step-by-step guide for process variability management and methods. Updated and expanded, this new edition provides a comprehensive study of heterogeneity, covering the basic principles of sampling theory and its various applications. It presents many practical examples to allow readers to select appropriate sampling protocols and assess the validity of sampling protocols from others. The variability of dynamic process streams using variography is discussed to help bridge sampling theory with statistical process control. Many descriptions of good sampling devices, as well as descriptions of poor ones, are featured to educate readers on what to look for when purchasing sampling systems. The book uses its accessible, tutorial style to focus on professional selection and use of methods. The book will be a valuable guide for mineral processing engineers; metallurgists; geologists; miners; chemists; environmental scientists; and practitioners in chemical, cement, steel, power generation, high performance materials, recycling, cereal, and pharmaceutical industries.