Here is a chapter from Design for Six Sigma Statistics, written by a Six Sigma practitioner with more than two decades of DFSS experience who provides a detailed, goal-focused roadmap. It shows you how to execute advanced mathematical procedures specifically aimed at implementing, fine-tuning, or maximizing DFSS projects to yield optimal results. For virtually every instance and situation, you are shown how to select and use appropriate mathematical methods to meet the challenges of today's engineering design for quality.
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.
This book contains precise descriptions of all of the many related six sigma methods. It also includes many case studies that detail how these methods have been applied in engineering and business to achieve millions of dollars of savings. This book will help readers to determine exactly which methods to apply in which situations and to predict how and when the methods might not be effective. Illustrative examples are provided for all the methods presented and exercises based on the case studies help build associations between techniques and industrial problems.
This book provides an accessible one-volume introduction to Lean Six Sigma and statistics in engineering for students and industry practitioners. Lean production has long been regarded as critical to business success in many industries. Over the last ten years, instruction in Six Sigma has been linked more and more with learning about the elements of lean production. Building on the success of the first and second editions, this book expands substantially on major topics of increasing relevance to organizations interested in Lean Six Sigma. Each chapter includes summaries and review examples plus problems with their solutions. As well as providing detailed definitions and case studies of all Six Sigma methods, the book uniquely describes the relationship between operations research techniques and Lean Six Sigma. Further, this new edition features more introductory material on probability and inference and information about Deming's philosophy, human factors engineering, and the motivating potential score – the material is tied more directly to the Certified Quality Engineer (CQE) exam. New sections that explore motivation and change management, which are critical subjects for achieving valuable results have also been added. The book examines in detail Design For Six Sigma (DFSS), which is critical for many organizations seeking to deliver desirable products. It covers reliability, maintenance, and product safety, to fully span the CQE body of knowledge. It also incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on experiment design, and includes practical experiments that will help improve students’ intuition and retention. The emphasis on lean production, combined with recent methods relating to DFSS, makes this book a practical, up-to-date resource for advanced students, educators and practitioners.
More than an introduction to statistical concepts and methods; this comprehensive resource provides sophisticated Six Sigma practitioners with the statistical tools necessary for rooting out and solving problems associated with product or service design. --
Here is a chapter from Design for Six Sigma Statistics, written by a Six Sigma practitioner with more than two decades of DFSS experience who provides a detailed, goal-focused roadmap. It shows you how to execute advanced mathematical procedures specifically aimed at implementing, fine-tuning, or maximizing DFSS projects to yield optimal results. For virtually every instance and situation, you are shown how to select and use appropriate mathematical methods to meet the challenges of today's engineering design for quality.
Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. With Six Sigma, we are solving problems and improving processes using as a basis one of the most powerful tools of human development: the scientific method. For the analysis of data, Six Sigma requires the use of statistical software, being R an Open Source option that fulfills this requirement. R is a software system that includes a programming language widely used in academic and research departments. Nowadays, it is becoming a real alternative within corporate environments. The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.
The following is a chapter from Kai Yang's Design for Six Sigma for Service. This comprehensive handbook aggressively tackles the difficulties involved in applying rigorous Six Sigma statistical methods to service environments. It delivers solid, effective solutions that can help your organization achieve measurable gains in customer satisfaction, cost reduction, value improvement, change management, and process performance. Featuring detailed design guidance and valuable tips, this book provides the specifics you need to create product value through improved service practices.
This book contains precise descriptions of all of the many related six sigma methods. It also includes many case studies that detail how these methods have been applied in engineering and business to achieve millions of dollars of savings. This book will help readers to determine exactly which methods to apply in which situations and to predict how and when the methods might not be effective. Illustrative examples are provided for all the methods presented and exercises based on the case studies help build associations between techniques and industrial problems.
Here is a chapter from an updated Design for Six Sigma, Second Edition, which has extensive new chapters and learning modules on innovation, lean product development, computer simulation, and critical parameter management--plus new thread-through case studies. This updated edition provides unrivalled real-world product development experience and priceless walk-throughs that help you choose the right design tools at every stage of product and service development. The book includes detailed directions, careful comparisons, and work-out calculations that make every step of the Design for Six Sigma process easier.
