Robust quality : powerful integration of data science and process engineering

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- Author -- Chapter 1: The Importance of Data Quality and Process Quality -- 1.1 Introduction -- 1.2 Importance of Data Quality -- Implications of Data Quality -- Data Management Function -- 1.3 Importance of Process Quality -- Six Sigma Methodologies -- Development of Six Sigma Methodologies -- Process Improvements through Lean Principles -- Process Quality Based on Quality Engineering or Taguchi Approach -- 1.4 Integration of Process Engineering and Data Science for Robust Quality -- Chapter 2: Data Science and Process Engineering Concepts -- 2.1 Introduction -- 2.2 The Data Quality Program -- Data Quality Capabilities -- 2.3 Structured Data Quality Problem-Solving Approach -- The Define Phase -- The Assess Phase -- Measuring Data Quality -- Measurement of Data Quality Scores -- The Improve Phase -- The Control Phase -- 2.4 Process Quality Methodologies -- Development of Six Sigma Methodologies -- Design for Lean Six Sigma Methodology -- 2.5 Taguchi's Quality Engineering Approach -- Engineering Quality -- Evaluation of Functional Quality through Energy Transformation -- Understanding the Interactions between Control and Noise Factors -- Use of Orthogonal Arrays -- Use of Signal-to-Noise Ratios to Measure Performance -- Two-Step Optimization -- Tolerance Design for Setting up Tolerances -- Additional Topics in Taguchi's Approach -- Parameter Diagram -- Design of Experiments -- Types of Experiments -- 2.6 Importance of Integrating Data Quality and Process Quality for Robust Quality -- Brief Discussion on Statistical Process Control -- Chapter 3: Building Data and Process Strategy and Metrics Management -- 3.1 Introduction -- 3.2 Design and Development of Data and Process Strategies.