Introduction To Engineering Statistics And Lean Six Sigma

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.

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.

The book focuses on the introduction of the basic concepts, processes, and tools used in Lean Six Sigma. A unique feature is the detailed discussion on Design for Six Sigma aided by computer modeling and simulation. The authors present several sample projects in which Lean Six Sigma and Design for Six Sigma were used to solve engineering problems or improve processes based on their own research and development experiences in engineering design and analysis. This book is intended to be a textbook for advanced undergraduate students, graduate students in engineering, and mid-career engineering professionals. It can also be a reference book, or be used to prepare for the Six Sigma Green Belt and Black Belt certifications by organizations such as American Society 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.

This book can be used as a textbook for university instruction or as reading for professionals interested in learning more about statistics and six sigma.

This book is written for the Six Sigma Black Belt who needs an understanding of many statistical methods but does not use all of these methods every day. It is intended to be used as a quick reference, providing basic details, step-by-step instructions, and Minitab statistical software instructions. Six Sigma Black Belts typically use a statistical program such as Minitab to perform calculations, but an understanding of the underlying statistics is still needed. Anybody can type data into a program; a Black Belt must be capable of understanding which hypothesis test is appropriate for a given use, as well as the assumptions that must be met to correctly perform the hypothesis test. The methods presented here are laid out according to the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) phases in which they are typically used. However, these methods can also be applied outside of a Six Sigma project, such as when one simply needs to determine whether there is a difference in the means of two processes producing the same parts. A Six Sigma Black Belt using Statistics for Six Sigma Black Belts will be able to quickly zero in on appropriate methods and follow the examples to reach the correct statistical conclusions.