Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements

by Harry, Mikel J.; Mann, Prem S.; De Hodgins, Ofelia C.; Hulbert, Richard L.; Lacke, Christopher J.

• ISBN13: 9780470114940
• ISBN10: 0470114940
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-01-19
• Publisher: Wiley
• More Book Details
• Purchase Benefits

Summary

The only textbook of its kind, this authoritative account provides a balanced approach to quantitative and qualitative statistics using the Six Sigma and Lean Sigma methodologies. The emphasis is on applications and the implementation of data analyses as they relate to Six Sigma. The primary purpose of the book is to serve as a reference guide and textbook for both experienced practitioners and new students of Six Sigma. The book includes plentiful examples, exercises, and case studies based on real data and real-life analytical and business problems. It is written by a broad spectrum of academic and industry professionals.

Author Biography

Mikel J. Harry, PhD, is President and Chairman of the Board of the Six Sigma Management Institute. He is considered the principal architect of Six Sigma and one of the world's leading authorities in the field. Dr. Harry also focuses his research on applications of experimental design, inferential statistics, and statistical process control.

Prem S. Mann, PhD, is Professor and Chair of the Department of Economics at Eastern Connecticut State University. Dr. Mann has published numerous articles in the areas of labor economics, microeconomics, and statistics. He is the author of Introductory Statistics, Seventh Edition (Wiley).

Ofelia C. De Hodgins, MS, is a Six Sigma Global Master Black Belt. She has over twenty-five years of consulting experience in manufacturing and finance and has published more than thirty journal articles in the areas of physics, industrial engineering, statistics, and Statistical Process Control (SPC).

Richard L. Hulbert, MBA, is Vice President of Systems and Technology for the Bank of New York Mellon. He has more than thirty-five years of industry experience in the areas of network engineering, installation, implementation, network operations of technology infrastructure, distributed systems, market data, and government telecommunications.

Christopher J. Lacke, PhD, is Associate Professor of Mathematics at Rowan University. He has published numerous journal articles in his areas of research interest, which include decision analysis, Bayesian analysis, and operations research.

Table of Contents

Principles of Six Sigma

Overview

Six Sigma Essentials

Quality Definition

Value Creation

Business, Operations, Process and Individual BOPI Goals

Underpinning Economics

Performance Metrics

Process

Design Complexity

Nature and Purpose of Six Sigma

Needs That Underlie Six Sigma

Why Focusing on The Customer is Essential To Six Sigma

Success Factors

Software applications

References

Glossary

Six Sigma Installation

Overview

Six Sigma Leadership - The Fuel of Six Sigma

Deployment Planning

Applications Projects

Deployment Timeline

Design for Six Sigma [DFSS] Principles

Processing for Six Sigma [PFSS] Principles

Managing for Six Sigma [MPSS] Principles

Project Review

Summary

References

Glossary

Lean Six Sigma Projects

Overview

Introduction

Project Description

Project Guidelines (General)

Project Selection

Project Scope

Project Leadership

Project Teams

Project Financials

Project Management

Project Payback

Project Milestones

Project Roadmap

Project Charters (General)

Six Sigma Projects

Project Summary

References

Glossary

Lean Practices

Overview

Introduction

The Idea of Lean Thinking

Theory of Constraints [TOC]

Lean Concept

Definition of Value Added Activities and Non Value Added Activities

Why Companies Think Lean

Visual Controls - Visual Factory

The Idea of Pull - Kanban

5S - 6S System

The Idea of Perfection - Kaizen

Replicate - Translate

PokaYoke System - Mistake Proofing

SMED System

7W + 1 Approach - Seven + one Deadly Waste(s)

6M Approach

Summary

References

Glossary

Value Stream Mapping

Overview

Introduction

Value Stream Mapping

Focused Brainstorming

Graphical representation of a Process in a Value Stream Map

Effective Working Time

Customer Demand

Takt Time

Pitch Time

Queuing Time

Cycle Time

Total Cycle Time

Calculation of Total Lead Time(s)

Value Added Percentage and Six Sigma Level

Drawing the Current Value Stream Map

Drawing the Value Stream Map?

What Makes a Value Stream Lean?

The Future Value Stream Map

Summary

References

Glossary

Introductory Statistics and Data

Overview

Introduction

Genetic Code of Statistics

Population and Samples

The Idea of Data

Nature of Data

Data Collection

The Importance of Data Collection

Sampling in Six sigma

Sources of Data

Database

Chapter Summary

References

Glossary

Quality Tools

Overview

Introduction

Nature of Six Sigma Variables

Quality Function Deployment (QFD)

Scales of Measurement

Diagnostic Tools

Analytical Methods

Graphical Tools

Graphical Representation of a Process

SIPOC Diagram

IPO Diagram - General Model of a Process System

Force Field Analysis

Matrix Analysis - The Importance of Statistical Thinking

Check Sheets

Score Cards

Affinity Diagram

Concept Integration

Reference

Glossary

Making Sense of Data in Six Sigma and Lean

Overview

Summarizing Quantitative Data: Graphical Methods

Summarizing Quantitative Data: Numerical Methods

Organizing and Graphing Qualitative Data

Summarizing Bivariate Data

Glossary

Fundamentals of Capability and Rolled Throughput Yield

Overview

Introduction

Why Capability

Six Sigma Capability Metric

Discrete Capability

Continuous Capability - Example

Fundamentals of Capability

Short versus Long Term Capability

Capability and Performance

Indices of Capability

Shift - Calibrating the Shift

Applying the 1.5Concept

Yield

Hidden Factory

Glossary

References

Probability

Overview

Experiments, Outcomes, and Sample Space

Calculating Probability

Combinatorial Probability

Marginal and Conditional Probabilities

Union of Events

Intersection of Events

Glossary

Discrete Random Variables and Their Probability Distributions

Overview

Six Sigma Performance Variables

Six Sigma Leverage Variables

Random Variable

Probability Distributions of a Discrete Random Variable

Mean of a Random Variable

Standard Deviation of a Discrete Random Variable

The Binomial Distribution

The Poisson Probability Distribution

The Geometrical Distribution

The Hypergeometric Probability Distribution

Glossary

Continuous Random Variables and Their Distributions

Overview

Continuous Probability Distributions

The Normal Distribution

The Exponential Distribution

Glossary

Sampling Distributions

Overview

Sampling Distribution of a Sample Mean

Sampling Distribution of a Sample Proportion

The Central Limit Theorem

Glossary

Single Population Estimation

Overview

What Does a Confidence Interval Mean?

Estimating a Population Mean

Estimating a Population Proportion

Estimating a Population Variance

Glossary

Control Methods

Overview

Introduction

Control Logic

Statistical Control Systems

Statistical Control

Prevention VS Detection

What is a Process Control System?

Variation

Process Out-of-Control

Fundamentals of Process Control

Continuous Statistical Process Control (SPC) Tools

Interpreting Process Control

Statistical Process Control and Statistical Process Monitoring

The Foundation of Statistical Process Control (SPC)

Tools for Process Controls - Control Charts

Control Limits

Process Out-of-Control

Western Electric Rules

Why Control Charts and How are These Used

Pre-Control Method

Control Charts for Variables

Control Chart for Attributes

Glossary

References

Single Population Hypothesis Tests

Overview

Introduction to Hypothesis Testing

Testing a Claim About a Population Mean

Hypothesis Test About a Population Proportion

Glossary

Estimation and Hypothesis Tests: Two Populations

Overview

Inferences About the Differences Between Two Population Means for Independent Samples

Inferences About the Differences Between Two Population Means for Paired Samples

Inferences About the Differences Between Two Population Proportions

Glossary

Chi-Square Tests

Overview

A Goodness-of-Fit Test

Contingency Tables

Tests of Independence and Homogeneity

Glossary

Analysis of Variance

Overview

The F- Distribution

One-Way Analysis of Variance

One-Way Analysis of Variance

Pairwise Comparisons

Multi-Factor Analysis of Variance

What Do We Do When the Assumptions Are Unreasonable?

Glossary

Linear and Multiple Regression

Overview

Simple Regression Model

Linear Regression

Coefficient of Determination and Correlation

Multiple Regression

Regression Analysis

Using the Regression Model

Residual Analysis

Cautions in Using Regression

Glossary

Measurement Analysis

Overview

Introduction

Measurement

Measurement Error

Accuracy and Precision

Measurement System as a Process

Categories of Measurement Error Which Affect the Location

Categories of Measurement Which Affect the Spread

Gage-Accuracy and Precision

Exploring Linearity Error

Gage R & R [Repeatability & Reproducibility]

Gage R&R - Variable

Gage R&R - Crossed

Attribute Gage R & R Study

ANOVA Method versus R Method

ANOVA/Variance Component Analysis

Rules of Thumb

Acceptability Criteria

Chapter Review

References

Glossary

Fundamentals of Design of Experiments

Overview

Introduction

What is Design of Experiments (DOE)?

Role of Experimental Design in Process Improvement

Experiment Design Tools

Principles of an Experimental Design

Different Types of Experiments

Introduction to Factorial Designs

Features of Factorial Designs - Orthogonality

Full Factorial Designs

Residual Analysis 22

Modeling 22

Multi-Factor Experiment

Fractional Factorial Designs

The ANOVA Table

Normal Probability Plot of the Effects

Main Effects Plot

Blocking Variable

Statistically Significant

Practically Significant

Fundamentals of Residual Analysis

Center Points

Noise Factors

Strategy of Good Experimentation

Selecting the Variable Levels

Selecting the Experimental Design

Replication

Analyzing the data [ANOVA]

Recommendations

Achieving the Objective

Chapter Summary

Chapter Example

References

Glossary

Design for Six Sigma [DFSS], Simulation, and Optimization

Overview

Introduction

Six Sigma as Stretch Target

Producibility

Statistical Tolerances

Design Application

Design Margin

Design Qualification

Design for Six Sigma (DFSS) Principles

Decision Power

Experimentation

Experiment Design

Response Surface Designs

Factorial Producibility

Toolbox Overview

Monte Carlo Simulations

Design for Six Sigma Project Selection Example

Defining Simulation Inputs

Defining Outputs and Running a Simulation

Stochastic Optimization: Discovering the Best Portfolio with the Least Risk

Conclusions

References

Glossary

Survey Methods and Sampling Techniques

Overview

The Sample Survey

The Survey System

Clear Goals

Target Population and Sample Size

Interviewing Method

Response Rate, Respondents and Non-respondents

Survey Methods

Sources of Information and Data

The Order of the Questions

Pilot Test the Questionnaire

Biased Sample or Response Error

Sampling - Random and Non-Random Sample

Population Distribution

Sampling Distribution

Sampling and Non-sampling Errors

References

Glossary

Statistical Tables

Table of Binomial Probabilities

Standard Normal Distribution Table

The t Distribution Table

Chi-Square Distribution Table

The F Distribution Table

Critical Values for the Mann-Whitney Test

Critical Values for the Wilcoxon Signed-Rank Test

Sigma Conversion Table

Answers to Selected Odd-Numbered Exercises

Index

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