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On the manufacturing shop floor, the principle of "value comes from the production of parts rather than charts" crucially applies when using practical statistical process control (SPC). The production worker should need to enter only a sample’s measurements to get immediately actionable information as to whether corrective action (e.g., as defined by a control plan’s reaction plan) is necessary for an out-of-control situation, and should not have to perform any calculations, draw control charts, or use sophisticated statistical software. This book’s key benefit for readers consists of spreadsheet-deployable solutions with all the mathematical precision of a vernier along with the simplicity of a stone ax. Traditional SPC relies on the assumption that sufficient data are available with which to estimate the process parameters and set suitable control limits. Many practical applications involve, however, short production runs for which no process history is available. There are nonetheless tested and practical control methods such as PRE-Control and short-run SPC that use the product specifications to set appropriate limits. PRE-Control relies solely on the specification limits while short-run SPC starts with the assumption that the process is capable—that is, at least a 4-sigma process, and works from there to set control limits. Cumulative Sum (CUSUM) and exponentially weighted moving average (EWMA) charts also can be used for this purpose. Specialized charts can also track multiple part characteristics, and parts with different specifications, simultaneously. This is often useful, for example, where the same tool is engaged in mixed-model production. Readers will be able to deploy practical and simple control charts for production runs for which no prior history is available and control the processes until enough data accumulate to enable the traditional methods (assuming it ever does). They will be able to track multiple product features with different specifications and also control mixed-model applications in which a tool generates very short runs of parts with different specifications. The methods will not require software beyond readily available spreadsheets, nor will they require specialized tables that are not widely available. Process owners and quality engineers will be able to perform all supporting calculations in Microsoft Excel, and without the need for advanced software.
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.
The essence of any root cause analysis in our modern quality thinking is to go beyond the actual problem. This means not only do we have to fix the problem at hand but we also have to identify why the failure occurred and what was the opportunity to apply the appropriate knowledge to avoid the problem in the future. Essential Statistical Concepts f
SPC METHODS FOR QUALITY IMPROVEMENT A comprehensive, applications-oriented guide to classical and cutting-edge SPC tools and techniques Written by a leading innovator in the field, SPC Methods for Quality Improvement provides a complete blueprint for integrating SPC methods into the manufacturing process. It explains methods for improving existing SPC systems and describes cutting-edge techniques that enable managers to develop full-fledged SPC systems in industries that traditionally were considered off-limits to this type of statistical analysis. The only guide to SPC geared exclusively to the practical concerns of manufacturing professionals, it translates statistical/mathematical concepts into real-world applications with the help of dozens of case studies and examples drawn from a variety of industries. SPC Methods for Quality Improvement is also a superb introductory text for students and newcomers to SPC. The author patiently introduces readers to essential SPC concepts and procedures and provides methodical, step-by-step instruction in the proper use of SPC tools and techniques. In the 1920s and 30s, Walter Shewhart of Bell Telephone Laboratories developed Statistical Process Control (SPC) as a means of analyzing manufacturing processes at the shop-floor level. Shewhart and his disciples—most notably W. Edwards Deming, father of total quality management—realized that SPC provided a sophisticated tool for assessing and improving quality at all levels. SPC, therefore, was the backbone of the quality management revolution of the 1980s and 90s. Yet, until now, there was no comprehensive, practical guide to SPC methods for engineers and managers working in manufacturing. SPC Methods for Quality Improvement fills that vacuum with complete coverage of SPC concepts, tools, and techniques geared to the practical concerns of manufacturing professionals. Dr. Charles Quesenberry introduces all statistical/mathematical essentials and carefully explains the rationale behind each concept. He employs vivid case studies to show how these concepts translate into real-world applications. Using examples drawn from a broad array of industries—from semiconductors to food processing, biomedical engineering to education—he deftly illustrates how SPC methods can streamline the manufacturing process and improve product quality. SPC Methods for Quality Improvement provides detailed, step-by-step guidance on the uses of both classical and second-generation SPC methods. Among cutting-edge methods described are those for charting processes without prior data, charting processes from start-up, and charting short runs with known false alarm rates. Readers also learn methods for studying the form of a reference distribution; how to use transformations to Q-statistics for various models; how to treat data from skewed distributions; and new ways of treating regression, multivariate, and autocorrelated data. An excellent text/primer for students and those new to SPC, SPC Methods for Quality Improvement is also a valuable guide for industrial and production engineers and managers who wish to improve existing SPC systems or to introduce SPC methods into industries where they were once inapplicable.
If you have been frustrated by very technical statistical process control (SPC) training materials, then this is the book for you. This book focuses on how SPC works and why managers should consider using it in their operations. It provides you with a conceptual understanding of SPC so that appropriate decisions can be made about the benefits of incorporating SPC into the process management and quality improvement processes. Today, there is little need to make the necessary calculations by hand, so the author utilizes Minitab and NWA Quality Analyst—two of the most popular statistical analysis software packages on the market. Links are provided to the home pages of these software packages where trial versions may be downloaded for evaluation and trial use. The book also addresses the question of why SPC should be considered for use, the process of implementing SPC, how to incorporate SPC into problem identification, problem solving, and the management and improvement of processes, products, and services.
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.
Designed for the quality professional with a basic understanding of traditional SPC, this book presents solutions for the problems encountered when trying to apply traditional control charting techniques in a complex manufacturing environment. Anyone using SPC who has felt limited by its traditional methods will find this book timely and beneficial. Along with basic SPC topics such as, control chart theories, process capability studies, data collection strategies, and sampling, this book concentrates on describing tools which solve the limitations of traditional SPC techniques. Specifically designed for those who face the challenges of limited data collection opportunities, small production runs, multiple characteristics, and demanding manufacturing situations, Innovative Control Charting will become a favorite, modern SPC reference. Benefits: Discover how SPC can be effectively applied even with complex parts, numerous part dimensions, similar but different characteristics, and small lot sizes. Learn how to overcome the three main limitations of traditional SPC techniques. Explore new SPC techniques in a step-by-step analysis approach using real-life examples.
This revised, expanded best-seller is a powerful new tool kit for the 21st century.
This handbook explains, in detail, each section of the Certified Supplier Quality Professional Body of Knowledge (updated 2023). It is a handy reference for those already working in the field and is an essential text for those working toward a CSQP certification.
Covering CUSUMs from an application-oriented viewpoint, while also providing the essential theoretical underpinning, this is an accessible guide for anyone with a basic statistical training. The text is aimed at quality practitioners, teachers and students of quality methodologies, and people interested in analysis of time-ordered data. Further support is available from a Web site containing CUSUM software and data sets.