Design for Six Sigma Statistics

Andrew Sleeper





In today's competitive environment, companies can no longer produce goods and services that are merely good with low defect levels but they must be near-perfect. This rigorous mathematical roadmap provides sophisticated Six Sigma practitioners with the statistical tools necessary for rooting out and solving problems associated with product or service design.

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