Design for Six Sigma Statistics

Overview

In today’s competitive environment, companies can no longer produce goods and services that are merely good with low defect levels, they have to be near-perfect. Design for Six Sigma Statistics is a rigorous mathematical roadmap to help companies reach this goal. As the sixth book in the Six Sigma operations series, this comprehensive book goes beyond an introduction to the statistical tools and methods found in most books but contains expert case studies, equations and step by step MINTAB instruction for performing: DFSS Design of Experiments, Measuring Process Capability, Statistical Tolerancing in DFSS and DFSS Techniques within the Supply Chain for Improved Results. The aim is to help you better diagnosis and root out potential problems before your product or service is even launched.

Table of contents

Part I: The Design for Six Sigma Process
Chapter 1: Developing of DFSS
Chapter 2: Implementing DFSS
Part II: Defining Product Requirements
Chapter 3: Processing the Voice of the Customer
Chapter 4: Quality Function Deployment
Chapter 5: Critical to Quality Requirements
Chapter 6: Design for Manufacturability and Assembly
Part III: Making Decisions With Data
Chapter 7: Selecting Product Concepts
Chapter 8: Visualizing Data
Chapter 9: Estimating Population Parameters
Chapter 10: Selecting Distribution Models
Chapter 11: Fitting Models to Data
Chapter 12: Tests for Changes in Variation
Chapter 13: Tests for Changes in Average
Chapter 14: Tests for Changes in Proportion
Chapter 15: Making Robust Decisions
Part IV: Controlling New Product Quality
Chapter 16: Designing Efficient Experiments in Five Minutes
Chapter 17: Two-Level Experiments
Chapter 18: Robust Experiments
Part V: Predicting New Product Quality
Chapter 19: Tolerance Design
Chapter 20: Measures of Process Capability
Chapter 21: Tolerance Analysis for Mechanical Engineers
Chapter 22: Tolerance Analysis for Electrical Engineers
Chapter 23: DFSS Scorecard
Part VI: Controlling New Product Quality
Chapter 24: Stabilizing Processes
Chapter 25: Measurement Systems Analysis
Chapter 26: Statistical Process Control

Biographical note

Andrew Sleeper is a DFSS expert and General Manager of Successful Statistics, LLC. He has worked with product development teams for 22 years as an engineer, statistician, project manager, Six Sigma Black Belt, and consultant. Mr. Sleeper holds degrees in electrical engineering and statistics, and is a licensed Professional Engineer. A senior member of the American Society for Quality, he is certified by ASQ as a Quality Manager, Reliability Engineer, and Quality Engineer, and has provided over 1,000 hours of instruction in countries around the world. His client list includes Anheuser-Busch, Intier Automotive Seating, New Belgium Brewing Company, and Ingersoll-Rand.

Back cover copy

THE STATISTICAL TOOL NECESSARY TO IDENTIFY AND SOLVE ANY DFSS PROBLEM

Design for Six Sigma Statistics meticulously details 59 mathematical procedures for executing DFSS programs, isolating and identifying problems, and solving them before the actual product launch. More than an introduction to statistical concepts and methods, this comprehensive resource offers real-world case studies and step-by-step MINTAB instruction for performing:
DFSS Design of Experiments
Measuring Process Capability
Statistical Tolerancing in DFSS
DFSS Techniques within the supply chain

THE STATISTICAL TOOLS YOU NEED TO MAXIMIZE DFSS:
The Design for Six Sigma Process * Defining Product Requirements * Making Decisions with Data * Conducting Efficient Experiments * Predicting New Product Quality * Controlling New Product Quality

Survival in today's competitive environment demands goods and services that truly approximate perfection -- which means pinpointing and solving problems before a product launches. Written by a Six Sigma practitioner with more than two decades of DFSS experience, Design for Six Sigma Statistics provides a detailed, goal-focused roadmap.

Design for Six Sigma Statistics shows quality professionals 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, readers are shown how to select and use appropriate mathematical methods to meet the challenges of today's engineering design for quality. The author covers mathematical tools for planning, interpreting, measuring, correcting, and anticipating product performance and manufacturing parameters. Examples, equations, and MINTAB screen shots facilitate progress through every step toward efficient, effective, and measurable results. In one comprehensive resource, readers will have the formulas they need to fully understand:
Robust Engineering Concepts
Failure Mode and Effects Analysis
Gap Analysis: Audits and Metrics
Design of Experiments

With Andrew Sleeper's Design for Six Sigma Statistics, quality professionals will have the highly specialized, problem-solving mathematical procedures necessary to create products and services that can compete and succeed in today's marketplace.