Design for Six Sigma Statistics

Description

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.

CONTENTS:

Part I: The Design for Six Sigma ProcessChapter 1: Developing of DFSSChapter 2: Implementing DFSSPart II: Defining Product RequirementsChapter 3: Processing the Voice of the CustomerChapter 4: Quality Function DeploymentChapter 5: Critical to Quality RequirementsChapter 6: Design for Manufacturability and AssemblyPart III: Making Decisions With DataChapter 7: Selecting Product ConceptsChapter 8: Visualizing DataChapter 9: Estimating Population ParametersChapter 10: Selecting Distribution ModelsChapter 11: Fitting Models to DataChapter 12: Tests for Changes in VariationChapter 13: Tests for Changes in AverageChapter 14: Tests for Changes in ProportionChapter 15: Making Robust DecisionsPart IV: Controlling New Product QualityChapter 16: Designing Efficient Experiments in Five MinutesChapter 17: Two-Level ExperimentsChapter 18: Robust ExperimentsPart V: Predicting New Product QualityChapter 19: Tolerance DesignChapter 20: Measures of Process CapabilityChapter 21: Tolerance Analysis for Mechanical EngineersChapter 22: Tolerance Analysis for Electrical EngineersChapter 23: DFSS ScorecardPart VI: Controlling New Product QualityChapter 24: Stabilizing ProcessesChapter 25: Measurement Systems AnalysisChapter 26: Statistical Process Control