The normal or bell curve distribution is far more common in statistics textbooks than it is in real factories, where processes follow non-normal and often highly skewed distributions. Statistical Process Control for Real-World Applications shows how to handle non-normal applications scientifically and explain the methodology to suppliers and custom
The tools and technique used in Statistical Process Control have been used around the world to monitor and measure process variation and allow real positive changes to be made. The majority of engineers and scientists have had some exposure to this important technique but in many cases this has been badly taught and they fail to see the usefulness of it properly applied. This book has been written with the authors 30 years experience in practical Statistical Process Control and is aimed squarely at practising engineers and scientists rather than statisticians and mathematicians. Practical Statistical Process Control takes a graphical approach using a software tool called Minitab. The author concentrates on each step of using the technique with explanations along the way of each decision point. Readers will find this guide both practical and useful, with copious screenshots of the software in use and clear precise explanations. The emphasis is on understanding the technique and being able to use it in real world applications. Key points: * Provides tools and techniques for practical business and process improvement. * Introduces screenshots and explanations for each step of SPC including the importance of assessing the measurement system and constructing control charts. * A worked example, using Minitab sample data with clear explanations of the variables and analyses. This book will be extremely useful to engineers and scientists who want to solve quality, process and manufacturing problems quickly and easily.
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
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.
A comprehensive treatment for implementing Statistical Process Control (SPC) in the food industry This book provides managers, engineers, and practitioners with an overview of necessary and relevant tools of Statistical Process Control, a roadmap for their implementation, the importance of engagement and teamwork, SPC leadership, success factors of the readiness and implementation, and some of the key lessons learned from a number of food companies. Illustrated with numerous examples from global real-world case studies, this book demonstrates the power of various SPC tools in a comprehensive manner. The final part of the book highlights the critical challenges encountered while implementing SPC in the food industry globally. Statistical Process Control for the Food Industry: A Guide for Practitioners and Managers explores the opportunities to deliver customized SPC training programs for local food companies. It offers insightful chapter covering everything from the philosophy and fundamentals of quality control in the food industry all the way up to case studies of SPC application in the food industry on both the quality and safety aspect, making it an excellent "cookbook" for the managers in the food industry to assess and initiating the SPC application in their respective companies. Covers concise and clear guidelines for the application of SPC tools in any food companies' environment Provides appropriate guidelines showing the organizational readiness level before the food companies adopt SPC Explicitly comments on success factors, motivations, and challenges in the food industry Addresses quality and safety issues in the food industry Presents numerous, global, real-world case studies of SPC in the food industry Statistical Process Control for the Food Industry: A Guide for Practitioners and Managers can be used to train upper middle and senior managers in improving food quality and reducing food waste using SPC as one of the core techniques. It's also an excellent book for graduate students of food engineering, food quality management and/or food technology, and process management.
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.
The intention of this book is to teach SPC and its application to specific processes in an integrated fashion. Many SPC training programs are taught by people that are very familiar with statistics but know little about plastics processing technology. However, successful implementation of SPC requires an understanding of SPC as well as process know-how. This book, therefore, aims to teach not only the principles of SPC but also basic injection molding and extrusion process technology.
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 ground-breaking book addresses the critical, growing need among health care administrators and practitioners to measure the effectiveness of quality improvement efforts. Written by respected healthcare quality professionals, Measuring Quality Improvement in Healthcare covers practical applications of the tools and techniques of statistical process control (SPC), including control charts, in healthcare settings. The authors' straightforward discussions of data collection, variation, and process improvement set the context for the use and interpretation of control charts. Their approach incorporates "the voice of the customer" as a key element driving the improvement processes and outcomes. The core of the book is a set of 12 case studies that show how to apply statistical thinking to health care process, and when and how to use different types of control charts. The practical, down-to-earth orientation of the book makes it accessible to a wide readership. "Only authors who have used statistics and control charts to solve real-world healthcare problems could have written a book so practical and timely." - Barry S. Bader, Publisher The Quality Letter for Healthcare Leaders "Many clinicians and other healthcare leaders underestimate the great contributions that better statistical thinking could make toward reducing costs and improving outcomes. This fascinating and timely book is a fine guide for getting started." - Donald M. Berwick, M.D. President and CEO, Institute for Healthcare Improvement Associate Professor of Pediatrics, Harvard Medical School Contents: Planning Your CQI Journey, Preparing to Collect Data, Data Collection, Understanding Variation, Using Run and Control Charts to Analyze Process Variation, Control Chart Case Studies, Developing Improvement Strategies, Using Patient Surveys for CQI, Formulas for Calculating Control Limits
What happens when the sport of Juggling meets a Statistical Process Control class? This book shows a creative approach to teaching data analysis for continuous improvement. Using step by step instructions, including over 65 photos and 40 graphs, traditional continuous improvement topics (design of experiments, reliability functions, and probability) are demonstrated using card illusions and hands-on activities. This book is for anyone that teaches these topics and wants to make them more understandable and sometimes even fun. Every operator, technician, student, manager, and leader can learn data analysis and be inspired to join the next generation of continuous improvement professionals.
