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Effectively Execute Lean Six Sigma Projects using SigmaXL and Minitab Written by a Six Sigma Master Black Belt and a Ph.D., this practical guide to Lean Six Sigma project execution follows the DMAIC (Define, Measure, Analyze, Improve, and Control) roadmap. The many real-world examples used in the book offer in-depth theoretical analyses and are implemented using the two most popular statistical software suites--SigmaXL and Minitab. This expert resource covers Lean topics ranging from basic data analysis to complex design of experiments and statistical process control. Harness the power of SigmaXL and Minitab and enable sustained positive operational results throughout your organization with help from this authoritative guide. Lean Six Sigma Using SigmaXL and Minitab explains how to: Define the project goals, project manager, value statement, stakeholders, and risk Schedule tasks using the Gantt chart, critical path analysis, and program evaluation and review technique Capture the voice of internal and external customers Assess the cost of quality Gather data and measure process performance Perform process capabilities analysis Apply Lean Six Sigma metrics to determine baseline performance Implement analysis techniques such as Pareto analysis, value stream mapping, failure mode and effect analysis (FMEA), and regression analysis Identify constraints via factorial experiments, and implement process improvements Monitor production performance using statistical process control
Here is a chapter from Six Sigma Statistics with Excel and MINITAB. This is a comprehensive and easy-to-use guide for understanding and using Excel and MINITAB programs for Six Sigma statistical data analysis. Each chapter includes relevant theory and technique, step-by-step exercises, case studies, graphical illustrations and screen shots for performing the techniques in both Excel and MINITAB.
Need to learn Minitab? Problem Solved! Get started using Minitab right way with help from this hands-on guide. Minitab Demystified walks you through essential Minitab features and shows you how to apply them to solve statistical analysis problems. Featuring coverage of Minitab 16, this practical guide explores the Minitab interface and the full range of Minitab graphics, Distribution models, statistical intervals, hypothesis testing, and sample size calculations are clearly explained. The book covers modeling tools of regression and the design of experiments (DOE) as well as the industrial quality tools of measurement systems analysis, control charts, capability analysis, acceptance sampling, and reliability analysis. Detailed examples and concise explanations make it easy to understand the material, and end-of-chapter quizzes and a final exam help reinforce key concepts. It's a no-brainer! You'll learn about: Accessing powerful Minitab functions with the Minitab assistant Confidence, prediction, and tolerance intervals Designing and analyzing experiments with hard-to-change variables Statistical process control (SPC), Six Sigma applications, and quality control Predicting the economic impact of sampling Analyzing life data with additional variables Simple enough for a beginner, challenging enough for an advanced student, and thorough enough for a Six Sigma professional, Minitab Demystified is your shortcut to statistical analysis success!
This reference manual is designed to help those interested in passing the American Society for Quality’s (ASQ) certification exam for Six Sigma Green Belts, those who may need to train future green belts, and others who want a handy reference to the appropriate materials needed to conduct successful green belt projects. This latest entry in ASQ's popular series of handbooks explicitly follows the certification's Body of Knowledge and addresses each topic to exactly the level green belts need. From lean and project management to statistics and data analysis, the DMAIC process is explained in a clear and insightful manner. Figures, tables, references, and useful appendices only add to this book's comprehensiveness. The CD-ROM has more than just sample questions! The authors have included dozens of useful files, such as case studies, articles, and blank forms, as well as a hot link to the Web site listed below. Anything except exam questions can be printed out and used in the ASQ certification exam.
Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which has far greater accuracy than conventional signal averaging methods. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach describes the research and development of a fluid level measurement system for dynamic environments. The measurement system is based on a single ultrasonic sensor. A Support Vector Machines (SVM) based signal characterization and processing system has been developed to compensate for the effects of slosh and temperature variation in fluid level measurement systems used in dynamic environments including automotive applications. It has been demonstrated that a simple ν-SVM model with Radial Basis Function (RBF) Kernel with the inclusion of a Moving Median filter could be used to achieve the high levels of accuracy required for fluid level measurement in dynamic environments. Aimed toward graduate and postgraduate students, researchers, and engineers studying applications of artificial intelligence, readers will learn about a measurement system that is based on a single ultrasonic sensor which can achieve the high levels of accuracy required for fluid level measurement in dynamic environments.
What happens when one of the most widely used quality improvement methodologies meets the world’s leading statistical software for quality improvement? Packed with case studies in a variety of sectors, including health care, manufacturing, airlines, and fast food restaurants, Six Sigma Case Studies with Minitab® shows you how to maximize the quality analysis and improvement tools available in Minitab® for your Six Sigma projects. Highly illustrated, the book includes detailed steps and more than 380 screenshots that explain how to use: Confidence Interval Estimation Hypothesis Testing Chi-Square Analysis Process Capability Analysis Binary Logistic Regression Item Analysis Cluster Analysis Mixture Design and Analysis of Experiments Multivariate Analysis Pareto Charts Cause-and-Effect Diagram Gage Repeatability and Reproducibility Analysis Taguchi Design and Analysis of Experiments Factorial Design and Analysis of Experiments Statistical Control Charts The case studies demonstrate the wide range of sectors and uses for Six Sigma and Minitab®. The screenshots provide exceptional detail and the book includes explanations for many Six Sigma terms and an appendix with the contents of the Minitab® worksheets that are referred to in most of the chapters. These features and more give you the tools to meet the challenges of continuous improvement expected in today’s marketplace.
The latest experimental design techniques for quality improvement "The methods taught in this book are a major contribution to statistical methods as an aid to engineers, as well as to those in industry, education, or government who are trying to understand the meaning of fi gures derived from comparisons or experiments." -- W. EDWARDS DEMING Co-written by three recipients of the Deming Medal awarded by the American Society for Quality (ASQ), Quality Improvement through Planned Experimentation, Third Edition discusses the principles and methodologies for planning and conducting experiments to improve products, processes, or systems. Fully revised with up-to-date case studies and incorporating new software, this authoritative guide fosters the sequential building of knowledge essential for implementing effective improvements. End-of-chapter exercises reinforce what you've learned, and forms for designing planned experiments help you to integrate the methods in the book into your daily work. The methods of planned experimentation provide an opportunity to better meet the needs of customers, reduce costs, and increase productivity by effecting verifiably beneficial changes. COVERAGE INCLUDES: * Improvement of quality * Principles for design and analysis of planned experiments * Experiments with one factor * Experiments with more than one factor * Reducing the size of experiments * Evaluating sources of variation * Sequential experimentation * Using a time series response variable * Designs with factors at more than two levels * Applications in health care * New product design NEW: Study-it software available for download!

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