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### Summary

### Summary

Contains a 700-page guide to the quality tools and statistics that are the foundation for Six Sigma. This book provides an overview of the management goals, training issues involved in a Six Sigma implementation, and the underlying philosophy. It explains the problem-solving techniques and statistical tools most often used in Six Sigma.

### Author Notes

Thomas Pyzdek, Thomas Pyzdek earned his Bachelor's Degree in Liberal Arts from the University of Nebraska at Omaha and earned his Master's in Systems Engineering in 1982, and Management in 1995, from the University of Arizona.

Pyzdek taught management statistics at the University of Arizona and for the American Society for Quality. He is a fellow of ASQ and is certified as both a Quality Engineer and a Reliability Engineer. Pyzdek has consulted for such large companies as Avon, McDonalds and Federal Mogul. He himself has launched two successful companies, Quality America, Inc, that specializes in statistical software, and Quality Publishing, Inc. which specializes in books and multimedia training materials. He also writes a regular column for Quality Digest Magazine.

In 1995, Pyzdek was awarded the ASQ Edwards Medal for outstanding contributions to the practice of quality management. He also received the Hughes Engineering Master's Fellowship in 1980. He was a member of the Board of Examiners for the first Malcolm Baldridge National Quality Award and the Panel of Judges for the first Arizona Govenor's Award for Quality. Pyzdek is listed as an Outstanding Writer and Author by the INternational Who's Who in Quality.

### Table of Contents

Preface | p. xiii |

Introduction | p. xvi |

Part I Six Sigma Implementation and Management | p. 1 |

Chapter 1 Building the Six Sigma Infrastructure | p. 3 |

What is Six Sigma? | p. 3 |

Implementing Six Sigma | p. 20 |

Change agent compensation and retention | p. 54 |

Chapter 2 Six Sigma Goals and Metrics | p. 56 |

Attributes of good metrics | p. 56 |

Six Sigma versus traditional three sigma performance | p. 58 |

The balanced scorecard | p. 61 |

Strategy deployment plan | p. 71 |

Information systems requirements | p. 74 |

Dashboard design | p. 79 |

Setting organizational key requirements | p. 89 |

Chapter 3 Creating Customer-Driven Organizations | p. 97 |

Elements of customer-driven organizations | p. 97 |

Surveys and focus groups | p. 102 |

Calculating the value of retention of customers | p. 116 |

Kano model of customer expectations | p. 119 |

Quality function deployment (QFD) | p. 121 |

The Six Sigma process enterprise | p. 125 |

Using QFD to link Six Sigma projects to strategies | p. 132 |

Linking customer demands to budgets | p. 140 |

Chapter 4 Training for Six Sigma | p. 150 |

Training needs analysis | p. 150 |

The strategic training plan | p. 152 |

Chapter 5 Six Sigma Teams | p. 167 |

Six Sigma teams | p. 167 |

Process improvement teams | p. 168 |

Work groups | p. 169 |

Other self-managed teams | p. 170 |

Team dynamics management, including conflict resolution | p. 171 |

Facilitation techniques | p. 178 |

Team performance evaluation | p. 182 |

Team recognition and reward | p. 184 |

Chapter 6 Selecting and Tracking Six Sigma Projects | p. 187 |

Choosing the right projects | p. 188 |

Analyzing project candidates | p. 189 |

Tracking Six Sigma project results | p. 208 |

Part II Six Sigma Tools and Techniques | p. 235 |

Chapter 7 Introduction to DMAIC and Other Improvement Models | p. 237 |

DMAIC, DMADV and learning models | p. 237 |

The Define Phase | |

Chapter 8 Problem Solving Tools | p. 252 |

Process mapping | p. 252 |

Check sheets | p. 255 |

Pareto analysis | p. 259 |

Cause and effect diagrams | p. 261 |

7M tools | p. 264 |

The Measure Phase | |

Chapter 9 Basic Principles of Measurement | p. 277 |

Scales of measurement | p. 277 |

Reliability and validity of data | p. 280 |

Overview of statistical methods | p. 283 |

Principles of statistical process control | p. 318 |

Chapter 10 Measurement Systems Analysis | p. 325 |

R&R studies for continuous data | p. 325 |

Attribute measurement error analysis | p. 346 |

Repeatability and pairwise reproducibility | p. 352 |

The Analyze Phase | |

Chapter 11 Knowledge Discovery | p. 361 |

Knowledge discovery tools | p. 361 |

Establishing the process baseline | p. 385 |

SIPOC | p. 383 |

Chapter 12 Statistical Process Control Techniques | p. 393 |

Statistical process control (SPC) | p. 393 |

EWMA | p. 453 |

Chapter 13 Process Capability Analysis | p. 467 |

Process capability analysis (PCA) | p. 467 |

Estimating process yield | p. 484 |

Chapter 14 Statistical Analysis of Cause and Effect | p. 490 |

Testing common assumptions | p. 490 |

Regression and correlation analysis | p. 496 |

Analysis of categorical data | p. 514 |

Non-parametric methods | p. 528 |

The Improve Phase | |

Chapter 15 Managing Six Sigma Projects | p. 534 |

Useful project management tools and techniques | p. 535 |

Project charter | p. 538 |

Work breakdown structures | p. 541 |

Feedback loops | p. 543 |

Performance measures | p. 544 |

Cost considerations in project scheduling | p. 552 |

Project management implementation | p. 560 |

Chapter 16 Risk Assessment | p. 571 |

Reliability and safety analysis | p. 571 |

Failure mode and effect analysis (FMEA) | p. 596 |

Statistical tolerancing | p. 600 |

Chapter 17 Design of Experiments (DOE) | p. 607 |

Terminology | p. 608 |

Power and sample size | p. 610 |

Design characteristics | p. 610 |

Types of design | p. 611 |

Examples of applying common DOE methods using software | p. 616 |

Empirical model building and sequential learning | p. 624 |

Data mining, artificial neural networks and virtual process mapping | p. 644 |

The Control Phase | |

Chapter 18 Maintaining Control After the Project | p. 649 |

Business process control planning | p. 649 |

Using SPC for ongoing control | p. 652 |

Process control planning for short and small runs | p. 655 |

Preparing the short run process control plain (PCP) | p. 656 |

PRE-Control | p. 661 |

Beyond DMAIC | |

Chapter 19 Design for Six Sigma (DFSS) | p. 665 |

Preliminary steps | p. 665 |

Define | p. 667 |

Measure | p. 670 |

Analyze | p. 671 |

Design | p. 682 |

Verify | p. 703 |

Chapter 20 Lean Manufacturing and Six Sigma | p. 705 |

Introduction to Lean and muda | p. 705 |

What is value to the customer? | p. 706 |

What is the value stream? | p. 708 |

How do we make value flow? | p. 711 |

How do we make value flow at the pull of the customer? | p. 713 |

How can we continue towards perfection? | p. 716 |

Becoming Lean: A tactical perspective | p. 720 |

Six Sigma and Lean | p. 721 |

Appendix | p. 724 |

Table 1 Glossary of basic statistical terms | p. 724 |

Table 2 Area under the standard normal curve | p. 730 |

Table 3 Critical values of the t-distribution | p. 733 |

Table 4 Chi-square distribution | p. 735 |

Table 5 F distribution ([alpha] = 1%) | p. 738 |

Table 6 F distribution ([alpha] = 5%) | p. 740 |

Table 7 Poisson probability sums | p. 742 |

Table 8 Tolerance interval factors | p. 746 |

Table 9 Durbin-Watson test bounds | p. 750 |

Table 10 y factors for computing AOQL | p. 754 |

Table 11 Control chart constants | p. 755 |

Table 12 Control chart equations | p. 757 |

Table 13 Table of d*[subscript 2] values | p. 759 |

Table 14 Power functions for ANOVA | p. 761 |

Table 15 Factors for short run control charts for individuals, X-bar, and R charts | p. 770 |

Table 16 Significant number of consecutive highest or lowest values from one stream of a multiple-stream process | p. 772 |

Table 17 Sample customer survey | p. 773 |

Table 18 Process [sigma] levels and equivalent PPM quality levels | p. 777 |

Table 19 Black Belt effectiveness certification | p. 778 |

Table 20 Green Belt effectiveness certification | p. 791 |

Table 21 AHP using Microsoft Excel | p. 804 |

References | p. 806 |

Index | p. 814 |