Description

Six Sigma is a management program that provides tools that help manufacturers obtain efficient, stream-lined production to coincide with ultimate high quality products. Lean Six Sigma will show how the well-regarded analytical tools of Six Sigma quality control can be successfully brought into the well-established models of “lean manufacturing,” bringing efficient, stream-lined production and high quality product readily together. This book offers a thorough, yet concise introduction to the essential mathematics of Six Sigma, with solid case examples from a variety of industrial settings, culminating in an extended case study. Various professionals will find this book immensely useful, whether it be the industrial engineer, the industrial manager, or anyone associated with engineering in a technical or managing role. It will bring about a clear understanding of not only how to implement Six Sigma statistical tools, but also how to do so within the bounds of Lean manufacturing scheme. It will show how Lean Six Sigma can help reinforce the notion of “less is more,” while at the same time preserving minimal error rates in final manufactured products.

Key Features

*Reviews the essential statistical tools upon which Six Sigma rests, including normal distribution and mean deviation and the derivation of 1 sigma through six sigma *Explains essential lean tools like Value-Stream Mapping and quality improvement tools like Kaizen techniques within the context of Lean Six Sigma practice *Extended case study to clearly demonstrate how Six Sigma and Lean principles have been actually implemented, reducing production times and costs and creating improved product quality

Readership

Industrial Engineers; Quality Control and Reliability Engineers; Mechanical Engineers; Chemical Engineers; Electrical Engineers; Managers in manufacturing and Process Industries; Graduate Students; Graduate Students in Industrial Engineering in courses on Quality Control, Reliability, Advances Production Control, Statistical Quality Control and Advanced Manufacturing Graduate Business Students concentrating in Manufacturing and Industrial Management

Table of Contents

PART I: Statistical Theory and Concepts Chapter 1. Introduction to Essentials of Lean Six Sigma (6)Strategies 1.1 Lean Six Sigma (6 ) Concepts Review 1.1.1 The Philosophy 1.1.2 Lean/Kaizen Six Sigma Engineering 1.2 Six Sigma Background 1.3 Some Six Sigma Successes Chapter 2. Statistical Theory of Lean Six Sigma (6 ) Strategies 2.1 Normal Distribution Curve 2.2 Six Sigma Process Capability Concepts 2.2.1 Six Sigma Short Term Capability 2.2.2 Estimation of Six Sigma Long Term Capability Chapter 3. Mathematical Concepts of Lean Six Sigma Engineering Strategies 3.1 Process Modeling (The heart of Lean Six Sigma) 3.2 The Normal Distribution 3.3 The Standard Normal Distribution 3.4 t-Distribution 3.4.1 Confidence interval for difference of two mean 3.5 Binomial Distribution 3.6 Poisson Distribution 3.7 Exponential Distribution 3.8 Hypegeometric Distribution 3.9 Normality Test 3.9.1 Kurtosis 3.9.2 Anderson Darling 3.10 Reliability Engineering and Estimation 3.11 Quality Cost PART II Six Sigma Engineering and Implementation Chapter 4. Six Sigma Continuous Improvement 4.1 Six Sigma Continuous Improvement Principles 4.2 Six Sigma Systems 4.2 Six Sigma Improvement and Training Model Chapter 5. Design for Six Sigma 5.1 Design for Six Sigma (DFSS) Principles 5.2 Design for Six Sigma Steps 5.3 Tools and Techniques 5.4 Pro

Details

No. of pages:
304
Language:
English
Copyright:
© 2006
Published:
Imprint:
Butterworth-Heinemann
Print ISBN:
9780123705020
Electronic ISBN:
9780080462325

About the author

Salman Taghizadegan

Chemical Engineer & Lean Six Sigma Master Black Belt Certified Hunter Industries, Inc. San Marcos, CA

Affiliations and Expertise

Chemical Engineer & Lean Six Sigma Master Black Belt Certified Hunter Industries, Inc. San Marcos, CA

Reviews

“… a textbook for academics and manufacturers that covers statistical theory and concepts, as well as the how-to’s of engineering and implementing Six Sigma.” — Automation World, October 2006