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E-grāmata: Robust Quality: Powerful Integration of Data Science and Process Engineering

  • Formāts: 142 pages
  • Sērija : Continuous Improvement Series
  • Izdošanas datums: 03-Sep-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9780429877278
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Robust Quality: Powerful Integration of Data Science and Process EngineeringPalielināt
  • Formāts: 142 pages
  • Sērija : Continuous Improvement Series
  • Izdošanas datums: 03-Sep-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9780429877278
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Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.

Features:











Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution
Foreword xi
Preface xiii
Acknowledgments xv
Author xvii
Chapter 1 The Importance of Data Quality and Process Quality
1(12)
1.1 Introduction
1(1)
1.2 Importance of Data Quality
2(3)
Implications of Data Quality
2(2)
Data Management Function
4(1)
1.3 Importance of Process Quality
5(5)
Six Sigma Methodologies
5(1)
Development of Six Sigma Methodologies
6(3)
Process Improvements through Lean Principles
9(1)
Process Quality Based on Quality Engineering or Taguchi Approach
9(1)
1.4 Integration of Process Engineering and Data Science for Robust Quality
10(3)
Chapter 2 Data Science and Process Engineering Concepts
13(18)
2.1 Introduction
13(1)
2.2 The Data Quality Program
13(1)
Data Quality Capabilities
13(1)
2.3 Structured Data Quality Problem-Solving Approach
14(6)
The Define Phase
15(1)
The Assess Phase
15(2)
Measuring Data Quality
17(1)
Measurement of Data Quality Scores
18(1)
The Improve Phase
19(1)
The Control Phase
19(1)
2.4 Process Quality Methodologies
20(1)
Development of Six Sigma Methodologies
20(1)
Design for Lean Six Sigma Methodology
20(1)
2.5 Taguchi's Quality Engineering Approach
21(5)
Engineering Quality
22(1)
Evaluation of Functional Quality through Energy Transformation
22(1)
Understanding the Interactions between Control and Noise Factors
23(1)
Use of Orthogonal Arrays
23(1)
Use of Signal-to-Noise Ratios to Measure Performance
23(1)
Two-Step Optimization
23(1)
Tolerance Design for Setting up Tolerances
23(1)
Additional Topics in Taguchi's Approach
24(1)
Parameter Diagram
24(1)
Design of Experiments
25(1)
Types of Experiments
26(1)
2.6 Importance of Integrating Data Quality and Process Quality for Robust Quality
26(5)
Brief Discussion on Statistical Process Control
28(3)
Chapter 3 Building Data and Process Strategy and Metrics Management
31(16)
3.1 Introduction
31(1)
3.2 Design and Development of Data and Process Strategies
31(1)
3.3 Alignment with Corporate Strategy and Prioritizing the Requirements
32(2)
3.4 Axiomatic Design Approach
34(7)
Design Axioms
34(1)
Designing through Domain Interplay
35(3)
Functional Requirements-Design Parameters Decomposition---Data Innovation
38(1)
Functional Requirements
38(1)
Design Parameters
38(1)
Functional Requirements--Design Parameters Decomposition---Decision Support
39(1)
Functional Requirements
39(1)
Design Parameters
39(1)
Functional Requirements--Design Parameters Decomposition---Data Risk Management and Compliance
39(1)
Functional Requirements
39(1)
Design Parameters
40(1)
Functional Requirements-Design Parameters Decomposition---Data Access Control
40(1)
Functional Requirements
40(1)
Design Parameters
40(1)
End-to-End Functional Requirements--Design Parameters Matrix
41(1)
3.5 Metrics Management
41(6)
Step 1 Defining and Prioritizing Strategic Metrics
41(1)
Step 2 Define Goals for Prioritized Strategic Metrics
42(1)
Step 3 Evaluation of Strategic Metrics
42(1)
Common Causes and Special Causes
43(2)
Step 4 Discovery of Root Cause Drivers
45(2)
Chapter 4 Robust Quality---An Integrated Approach for Ensuring Overall Quality
47(14)
4.1 Introduction
47(1)
4.2 Define, Measure, Analyze, Improve, and Control-Based Integrated Approach for Robust Quality
47(3)
The Define Phase
47(2)
The Measure Phase
49(1)
The Analyze Phase
50(1)
The Improve Phase
50(1)
The Control Phase
50(1)
4.3 Design for Six Sigma (Define, Measure, Analyze, Design, and Verify)-Based Integrated Approach for Robust Quality
50(4)
The Define Phase
52(1)
The Measure Phase
52(1)
The Analyze Phase
53(1)
The Design Phase
53(1)
The Verify Phase
54(1)
4.4 Taguchi-Based Integrated Approach for Robust Quality
54(4)
The Define Stage
56(1)
The Planning Stage
56(1)
The Execute Stage
57(1)
4.5 Measuring Robust Quality
58(3)
Chapter 5 Robust Quality for Analytics
61(18)
5.1 Introduction
61(1)
5.2 Analytics Requirements
61(1)
5.3 Process of Executing Analytics
62(3)
5.4 Analytics Execution Process in the Define, Measure, Analyze, Improve, and Control Phases
65(6)
The Define Phase
65(1)
The Measure Phase
66(1)
The Analyze Phase
67(1)
Correlation Analysis
68(1)
Association Analysis
68(1)
Regression Analysis
68(1)
Stepwise Regression
69(1)
Test of Additional Information (Rao's Test)
69(1)
Discrimination and Classification Method
69(1)
Principal Component Analysis
70(1)
Artificial Neural Networks
70(1)
Artificial Intelligence and Machine Learning Techniques
70(1)
The Improve Phase
70(1)
The Control Phase
70(1)
5.5 Purposeful Analytics
71(3)
Individualized Analytics versus Population-Based Analytics
73(1)
Examples of Individualized Insights
73(1)
5.6 Accelerated Six Sigma for Problem-Solving
74(2)
The Define Phase
75(1)
The Measure Phase
75(1)
The Analyze Phase
75(1)
Improve Phase
76(1)
The Control Phase
76(1)
5.7 Measuring Analytics Quality
76(1)
5.8 Model Performance Risk Management Using Analytics Robust Quality Index
77(2)
Chapter 6 Case Studies
79(28)
6.1 Improving Drilling Operation
79(8)
Drilling Defects
79(1)
Measurement System Analysis
79(3)
Experiment Design Description
82(1)
Selection of Levels for the Factors
83(1)
Designing the Experiment
84(1)
Data Collection and Ensuring Data Quality
85(1)
Data Analysis
85(2)
Confirmation Experiment
87(1)
Improvement in Robust Quality Index
87(1)
6.2 Improving Plating Operation
87(6)
Validating the Measurement System
88(1)
Anode Area and Position
89(1)
Clamping Position
90(1)
Designing the Experiment
90(1)
Data Collection and Data Quality
91(1)
Data Analysis
91(1)
Calculating Robust Quality Index
92(1)
6.3 Data Quality Improvement Practice to Achieve Robust Quality
93(5)
Critical Data Element Rationalization Matrix
93(1)
Correlation and Association Analysis
94(2)
Signal-to-Noise Ratios
96(1)
Impact on the Analytics Quality
96(2)
6.4 Monitoring and Controlling Data Quality through Statistical Process Control to Achieve Robust Quality
98(4)
Analysis Details
99(2)
Out-of-Control Situations
101(1)
Root Cause Identification and Remediation
101(1)
Impact on Process Quality
102(1)
6.5 Analysis of Care Gaps
102(5)
Calculating Analytics Robust Quality Index
105(2)
Appendix I Control Chart Equations and Selection Approach 107(4)
Appendix II Orthogonal Arrays 111(4)
Appendix III Mean Square Deviation (MSD), Signal-to-Noise Ratio (SNR), and Robust Quality Index (RQI) 115(4)
References 119(2)
Index 121
Rajesh Jugulum, PhD, is the Informatics Director at Cigna. Prior to joining Cigna, he held executive positions in the areas of process engineering and data science at Citi Group and Bank of America. Rajesh completed his PhD under the guidance of Dr. Genichi Taguchi. Before joining the financial industry, Rajesh was at Massachusetts Institute of Technology where he was involved in research and teaching. He currently teaches at Northeastern University in Boston. Rajesh is the author/co-author of several papers and four books including books on data quality and design for Six Sigma. Rajesh is an American Society for Quality (ASQ) Fellow and his other honors include ASQs Feigenbaum medal and International Technology Institutes Rockwell medal. Rajesh has delivered talks as the keynote speaker at several conferences, symposiums, and events related to data analytics and process engineering. He has also delivered lectures in several universities/companies across the globe and participated as a judge in data-related competitions.
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