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Quality in the Era of Industry 4.0: Integrating Tradition and Innovation in the Age of Data and AI [Kõva köide]

(Wayne State University)
  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 259x185x28 mm, kaal: 726 g
  • Ilmumisaeg: 01-Dec-2023
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119932440
  • ISBN-13: 9781119932444
Teised raamatud teemal:
Quality in the Era of Industry 4.0: Integrating Tradition and Innovation in the Age of Data and AISuurem pilt
  • Formaat: Hardback, 352 pages, kõrgus x laius x paksus: 259x185x28 mm, kaal: 726 g
  • Ilmumisaeg: 01-Dec-2023
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119932440
  • ISBN-13: 9781119932444
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119932444.html
Märksõnad:
"The Industry 4.0 revolution is shifting the way that quality engineers, managers, and product developers must think about quality control. Super connectivity, IoT, and big data have enabled a transition from traditional "voice of the customer" surveys to the "voice of Big Data," which communicates descriptive information from real customers about how they use products. Quality in the Era of Industry 4.0: Harnessing Data Analytics for Quality Engineering Applications guides readers on how that data can be leveraged to optimize products during use, to anticipate and mitigate the consequences of likely failures, and to build better and less expensive products in the future. In a concise, straightforward style, this book offers readers a comprehensive framework for new quality management methods under Industry 4.0. This includes new techniques like using real-world data to improve product fit and performance, leveraging connectivity to make products responsive to changing needs and use cases, and drawing upon modern manufacturing to make cost-effective, bespoke solutions that can be produced more efficiently. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how IoT-enabled product usage data can be used to bench mark the product performances, durability, and detect design vulnerabilities. Automated Product Lifecycle Management, Predictive Quality Control, and defect prevention using technologies like smart factories, IoT, digital twins, machine learning, and sensors are also covered"--

QUALITY IN THE ERA OF INDUSTRY 4.0

Enables readers to use real-world data from connected devices to improve product performance, detect design vulnerabilities, and design better solutions

Quality in the Era of Industry 4.0 provides an insightful guide to harnessing user performance and behavior data through AI and other Industry 4.0 technologies. This transformative approach enables companies to not only optimize products and services in real-time, but also to anticipate and mitigate likely failures proactively. In a succinct and lucid style, the book presents a pioneering framework for a new paradigm of quality management in the Industry 4.0 landscape. It introduces groundbreaking techniques such as utilizing real-world data to tailor products for superior fit and performance, leveraging connectivity to adapt products to evolving needs and use-cases, and employing cutting-edge manufacturing methods to create bespoke, cost-effective solutions with greater efficiency.

Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how these new quality approaches can be used to benchmark the product’s performance and durability, maintain smart manufacturing, and detect design vulnerabilities.

Written by a seasoned expert with experience teaching quality management in both corporate and academic settings, Quality in the Era of Industry 4.0 covers topics such as:

  • Evolution of quality through industrial revolutions, from ancient times to the first and second industrial revolutions
  • Quality by customer value creation, explaining differences in producers, stakeholders, and customers in the new digital age, along with new realities brought by Industry 4.0
  • Data quality dimensions and strategy, data governance, and new talents and skill sets for quality professionals in Industry 4.0
  • Automated product lifecycle management, predictive quality control, and defect prevention using technologies like smart factories, IoT, and sensors

Quality in the Era of Industry 4.0 is a highly valuable resource for product engineers, quality managers, quality engineers, quality consultants, industrial engineers, and systems engineers who wish to make a participatory approach towards data-driven design, economical mass-customization, and late differentiation.

Preface xiii

Acknowledgments xix

1 Evolution of Quality Through Industrial Revolutions 1

1.1 Quality Before Industrial Revolutions 2

1.2 Quality in the First Industrial Revolution 3

1.3 The Second Industrial Revolution and the Birth of Modern Quality
Management 3

1.3.1 Mass Production System Is a Game Changer 4

1.3.2 The Start of Modern Quality System 6

1.4 The Third Industrial Revolution and the Maturity of Modern Quality
Management System 8

1.4.1 Contributions of Japan to Quality Management 8

1.4.1.1 Total Quality Control 8

1.4.1.2 Taguchi Method 9

1.4.1.3 Quality Function Deployment 9

1.4.1.4 Kano Model 9

1.4.1.5 Affinity Diagram 9

1.4.1.6 Kansei Engineering 9

1.4.1.7 Poka-Yoke 10

1.4.2 Total Quality Management (TQM) 11

1.4.3 The Third Industrial Revolution and Its Impact on Quality Management
11

1.4.4 Lean Six Sigma 12

1.4.4.1 Overview of Lean Six Sigma 12

1.4.4.2 Limitations of Lean Six Sigma 13

1.5 Current Challenges and Difficulties for Quality Management 14

1.5.1 Industry 4.0 Is Coming 14

1.5.2 Customers in Industry 4.0 Age and Their Expectations 15

1.5.3 Challenges for Modern Quality Management Brought by Industry 4.0 16

1.5.3.1 The Limitations of Traditional Quality Management Practices 16

1.5.3.2 Changing Realities in a Connected World 18

1.5.3.3 Smart Producers, Old Quality Management 19

1.5.3.4 Quality and Innovation 19

1.5.3.5 Quality and Risk Management 19

1.6 Summary 20

References 20

2 Evolving Paradigm for Quality in the Era of Industry 4.0 23

2.1 Current Quality Definitions and Paradigms 23

2.1.1 Definitions from Quality Community 24

2.1.2 Quality Definitions and Paradigms from Academic Community 25

2.1.3 Robert M. Pirsigs View on Quality 27

2.1.3.1 Summary of Robert M. Pirsigs View on Quality 27

2.1.3.2 Possible Contributions for New Quality Paradigm 28

2.1.4 Christopher Alexanders View on Quality 29

2.1.4.1 Summary of Christopher Alexanders Work 29

2.1.4.2 Possible Contributions for New Quality Paradigm 33

2.2 Changes Brought by Industry 4.0 34

2.2.1 Smart Manufacturing 34

2.2.2 Smart Enterprise by Superconnectivity 36

2.2.2.1 How Superconnectivity Affects Product Development and Production 37

2.2.3 Other Changes Brought by Industry 4.0 38

2.2.4 Summary: Impact of Industry 4.0 on Quality 39

2.3 Quality 4.0 39

2.3.1 What Is Quality 4.0 39

2.3.2 American Society of Qualitys Descriptions on Quality 4.0 41

2.3.2.1 American Society of Quality Definition of Quality 4.0 41

2.3.2.2 Key Features of Quality 4.0 41

2.3.2.3 Establishing and Implementing Quality 4.0 Principles 42

2.3.2.4 Quality 4.0 Tools 42

2.3.2.5 Quality 4.0 Value Propositions 43

2.3.3 Reflecting on ASQs Quality 4.0 Narratives 43

2.4 Hidden Gems: Lesser Known but Potent Ideas on Quality 43

2.4.1 Quality as Customer Value 44

2.4.2 Individualized Customer Value 46

2.4.3 Peter Druckers View: Good Quality and Poor Quality 47

2.5 Evolving Paradigm for Quality in the Era of Industry 4.0 49

2.5.1 Dual Facets of Quality 50

2.5.2 Customer Value Creation in the New Era 51

2.5.3 Expanded Role of Quality Assurance 52

2.5.4 Evolving Trends 53

References 54

3 Quality by Design and Innovation 57

3.1 The Trend of Quality: Going Upstream 57

3.2 The Journey into Quality by Design 60

3.3 Design for Six Sigma, A Serious Attempt for Quality by Design 61

3.3.1 Samsungs Journey for DFSS and Innovation 62

3.3.1.1 DFSS and TRIZ Greatly Helped Samsungs Innovation Initiatives 63

3.3.1.2 A Dual-Track Innovation Strategy: Technology Push and Market Pull
63

3.3.1.3 Summary of Samsung Experiences 65

3.3.2 Apple Inc.s Innovation Journey Under Steve Jobs 65

3.4 Quality by Design in the Era of Industry 4.0 66

3.4.1 Overviews of Design Quality and Quality by Design 66

3.4.2 Some Significant Changes in Business Ecosystem in Digital Revolution
68

3.4.3 More Changes Expected by Industry 4.0 69

3.4.3.1 Summary: Benefits of Industry 4.0 Technologies for Quality by Design
71

3.4.4 The Objective of Quality by Design in Industry 4.0: Cultivating
Customer Value 71

3.4.5 Identifying Customer Needs in the Era of Industry 4.0 72

3.4.5.1 Voice of Customer (VoC) 4.0 73

3.4.5.2 Mining Customer Needs with IoT (Internet of Things) 73

3.4.5.3 Mining Customer Needs with IoB (Internet of Behaviors) 74

3.4.5.4 Social Listening 74

3.4.6 Evaluating Customer Value and Analyzing Value Proposition 75

3.4.6.1 Willingness to Pay (WTP) as a Customer Value Indicator 79

3.4.6.2 Survey-Based Customer Value Evaluation Methods 79

3.5 Customer Value Creation by Innovation 80

3.5.1 Blue Ocean Strategy 80

3.5.2 Medici Effect 83

3.5.3 Design Thinking 85

3.5.4 Co-creation with Customers and Stakeholders 87

3.5.5 Design for Individualized Customer Value 90

3.5.6 Emotional, Psychological, and Culture Value Creation for Stakeholders
91

3.5.7 Design for Quality of Experience 93

3.6 Quality Management and Assurance in Early Product Life Cycle 99

3.6.1 Quality in Product Development: Crafting Customer Value and
Controlling Quality Loss 99

3.6.1.1 Dual Responsibilities in Quality Management 99

3.6.2 Whose Responsibilities for Quality? 101

3.6.2.1 Emergence of the Quality Department 101

3.6.2.2 Realignment of Quality Management Functions: Integration and Deep
Collaboration 102

3.6.3 Quality Assurance in the Early Stage of Product Life Cycle 104

3.6.3.1 Is the Separation of Value Creation and Quality Assurance a Good
Idea? 104

3.6.3.2 Quality and Standards: An Interconnected Relationship 105

3.6.4 Overview of Risk Management for New Product Development 108

3.6.4.1 Framework for Risk Management in New Product Development 109

3.6.4.2 New Content Risk Analysis and Management 111

3.6.4.3 Robust Technology Development 112

3.6.4.4 Risk Management by Complexity Theory 112

References 113

4 Quality Management in the Era of Industry 4.0 119

4.1 Introduction 119

4.2 Smart Factory 120

4.2.1 What Is a Smart Factory? 120

4.2.2 Several New Quality Control Methods in Smart Factory 124

4.2.2.1 Real-Time Monitoring and Control 124

4.2.2.2 Predictive Quality Assurance (PQA) 126

4.2.2.3 Electronic/Digital Poka Yoke Methods 126

4.2.2.4 Teslas Giga Press 127

4.2.3 Collaboration of Manufacturing, Engineering, and Quality in Smart
Factory 129

4.2.4 Predictive Maintenance in Smart Factory 130

4.3 Quality Management for Smart Supply Chain 130

4.3.1 Understanding the Smart Supply Chain 130

4.3.2 Overview of Supplier Quality Management and Capabilities Brought by
Industry 4.0 133

4.3.3 Contemporary Collaboration Models Between Producers and Suppliers in
Quality Management 135

4.3.3.1 APQP and PPAP 135

4.3.3.2 Integrated Product Development (IPD) 136

4.3.4 Leveraging Industry 4.0 for Supply Quality Management Enhancement 137

4.3.4.1 Early Supplier Involvement During the Product Development Stage 137

4.3.4.2 Upgrading the Supplier Quality Validation Process Via Industry 4.0
Technology 138

4.4 Quality Management in After-Sale Customer Service 139

4.4.1 Introduction 139

4.4.1.1 Regular After-Sale Customer Service 139

4.4.1.2 Users Feedback Management 140

4.4.1.3 Product Innovation 140

4.4.2 Upgrading After-Sale Customer Services with Industry 4.0 141

4.4.3 Upgrading User Feedback Management with Industry 4.0 143

4.4.4 Upgrading User Feedback Management with Social Listening 144

4.4.5 Upgrading User Feedback Management with Quality of Experience Mining
and Analysis 144

4.4.6 Improving After-Sale Customer Service Teams Contribution in Product
Innovation by Industry 4.0 145

4.5 Quality Management for Service Industry 146

4.5.1 What Are the Differences in Quality Management Between Service and
Manufacturing Industry 146

4.5.2 What Industry 4.0 Can Help in Service Quality Management 147

4.5.3 Industry 4.0 and Individualized Services 147

4.6 Digital Quality Management System Under Industry 4.0 149

4.6.1 Introduction 149

4.6.1.1 Structure 150

4.6.1.2 Functionalities and Features 150

4.6.2 Cloud-Based Master Platforms that Integrate eQMS with Other Business
Applications 152

4.6.3 Collaborative Work on Quality Through Product Life Cycle 153

4.6.4 Enhance Digital Quality Management System by Industry 4.0 Technologies
154

4.6.5 Unified Quality Management System 155

4.6.6 Collaborations of Professionals in Unified Quality Management System
156

4.6.6.1 Collaboration Among Quality Professionals in Different Sectors 156

4.6.6.2 Collaboration Between Quality Professionals and Others 157

References 157

5 Predictive Quality 161

5.1 Introduction 161

5.1.1 Definition and Importance 162

5.1.1.1 Definition 162

5.1.1.2 Importance 162

5.1.2 Historical Perspective 162

5.1.3 Current Trends 163

5.2 Elements of Predictive Quality 164

5.2.1 Data Collection 164

5.2.2 Data Quality 164

5.2.3 Data Analysis 166

5.2.4 Predictive Models 167

5.3 Exploration of Predictive Quality Models 168

5.3.1 Regression Models 168

5.3.2 Time Series Model 170

5.3.3 Machine Learning Model 171

5.3.4 Deep Learning Models 175

5.4 Performance Metrics in Predictive Modeling 176

5.4.1 Accuracy 177

5.4.2 Precision 177

5.4.3 Recall 178

5.4.4 F1 Score 178

5.4.5 Auc-roc 179

5.5 Application of Predictive Quality in Various Industries 180

5.5.1 Manufacturing 180

5.5.2 Healthcare 190

5.5.3 Retail 190

5.5.4 Finance 191

5.5.5 Information Technology 192

5.6 The Challenges and Limitations of Predictive Quality 193

5.6.1 Data Privacy and Security Issues 193

5.6.2 Model Interpretability 193

5.6.3 Overfitting and Underfitting 193

5.6.4 Need for High-Quality and Relevant Data 194

5.7 The Future of Predictive Quality 194

References 194

6 Data Quality 199

6.1 Introduction 199

6.2 Data and Data Quality 200

6.2.1 Overview 200

6.2.1.1 Data Involved in Data Quality Study 200

6.2.1.2 Definition of Data Quality 200

6.2.2 Categories of Data 201

6.2.3 Causes of Poor Data Quality 205

6.2.4 Cost of Poor Data Quality 205

6.3 Data Quality Dimensions and Measurement 206

6.3.1 Data Quality Dimensions 206

6.3.2 Measurement of Data Quality 207

6.3.2.1 Measuring Accuracy in Data Quality 207

6.3.2.2 Measure Completeness in Data Quality 208

6.3.2.3 Measure Consistency in Data Quality 209

6.3.2.4 Measure Timeliness in Data Quality 210

6.3.2.5 Measure Validity in Data Quality 211

6.3.2.6 Measure Uniqueness in Data Quality 211

6.3.2.7 Measuring Integrity in Data Quality 212

6.3.2.8 Measuring Relevance 213

6.3.2.9 Measuring Reliability 214

6.4 Data Quality Management 216

6.4.1 Reactive Versus Proactive Data Quality Management 217

6.4.2 Data Quality Assessment 218

6.4.3 Data Cleansing 219

6.4.4 Data Integration 220

6.4.5 Data Validation 221

6.4.6 Data Monitoring 222

6.4.7 Technology, Tools, and Software on Data Quality Management 223

6.4.7.1 Technologies and Tools 223

6.4.7.2 Data Quality Management Software 224

6.5 Data Governess 225

6.5.1 Data Governance Strategy 226

6.5.1.1 Fundamentals 226

6.5.1.2 Objectives 226

6.5.1.3 Winning Strategy 226

6.5.2 Data Governance Framework 227

6.5.3 Data Stewardship 230

6.5.4 Data Life Cycle Management 231

6.5.5 Data Governess Tools and Technology 231

6.6 The Role of Quality Professionals 232

6.7 Future Trends in Data Quality 234

References 235

7 Risk Management in the 21st Century 237

7.1 Introduction 237

7.1.1 Overview of Risk Management 238

7.1.2 Redefining Risk Management in the 21st Century 239

7.1.3 The Paramountcy of Risk Management in the Contemporary Context 240

7.2 Deciphering the Nature of Risk 241

7.2.1 Definition of Risk 242

7.2.2 Types of Risks 243

7.2.3 Risk Assessment and Analysis 244

7.3 Risk Management Frameworks 246

7.3.1 Traditional Risk Management Approaches 247

7.3.1.1 Risk Identification 247

7.3.1.2 Risk Analysis 248

7.3.1.3 Risk Treatment 249

7.3.1.4 Risk Monitoring 250

7.3.1.5 Pros and Cons of Traditional Risk Management Approaches 250

7.3.2 Contemporary Risk Management Models 251

7.3.2.1 Enterprise Risk Management (ERM) 251

7.3.2.2 Operational Risk Management (ORM) 252

7.3.2.3 Strategic Risk Management (SRM) 253

7.3.2.4 Integrated Risk Management (IRM) 254

7.3.2.5 Pros and Cons of Contemporary Risk Management Models 255

7.3.3 Integrating Risk Management with Strategic Planning 255

7.4 Risk Management Techniques 259

7.4.1 Techniques for Risk Identification 260

7.4.2 Techniques for Risk Assessment 261

7.4.3 Quantitative and Qualitative Risk Analysis 262

7.5 Technology and Risk Management 263

7.5.1 Role of Technology in Risk Management 264

7.5.1.1 Current Role of Technology in Risk Management 264

7.5.2 Automation and Artificial Intelligence in Risk Assessment 266

7.5.2.1 State of the Art as of Now 266

7.5.3 Data Analytics for Risk Prediction and Management 268

7.6 Resilience and Business Continuity 270

7.6.1 Cultivating Resilience in Organizations 271

7.6.1.1 Historical Context and Evolution 271

7.6.1.2 Current Approaches to Building Resilience 271

7.6.1.3 Building Resilience through Complexity Theory 273

7.6.2 Business Continuity Planning 274

7.6.3 Disaster Recovery and Emergency Response 275

References 276

8 Emerging Organizational Changes in the 21st Century 281

8.1 The Continuously Shifting Landscape of Organizational Structures 282

8.1.1 Evolution from Traditional Pyramid to Contemporary Organizational
Structures 282

8.1.1.1 Traditional Pyramid Structures 283

8.1.1.2 The Move to Matrix Structures 283

8.1.1.3 The Flat and Horizontal Organizations 283

8.1.1.4 Contemporary Organizational Structures 283

8.1.2 The Emergence of Flexible and Flat Structures 284

8.2 Impact of Technological Advances on Organizational Structures 288

8.2.1 Impact of Artificial Intelligence 288

8.2.1.1 AI Technologies and Their Impact 288

8.2.2 The Role of Big Data 290

8.2.2.1 Applications of Big Data and Their Impact 291

8.2.3 Effects of Industry 4.0 291

8.2.3.1 Industry 4.0 Technologies and Their Impact 291

8.3 Emerging Organizational Models in the 21st Century 292

8.3.1 The Networked Organization 292

8.3.1.1 Structure of a Networked Organization 292

8.3.1.2 Reasons for Adopting a Networked Structure 293

8.3.2 The Holacracy Model 294

8.3.3 The Agile Organization 295

8.3.4 Virtual and Remote Organizations 296

8.3.5 The Platform Model 297

8.3.5.1 Assigning Roles and Responsibilities 297

8.3.6 Rendanheyi Model 298

8.4 Future of Organizational Structures 299

8.4.1 Predicted Trends and Patterns 300

8.4.2 Potential Challenges and Solutions 301

8.4.3 Impact of Future Technologies 302

8.5 The Impact on Quality Professionals 303

8.5.1 Role Shifts and Adaptation 304

8.5.2 New Quality Management Approaches 305

8.5.3 Impact of Remote Working on Quality Management 306

8.6 Required Skills and Knowledge for Quality Professionals in the Future
307

8.6.1 Emphasizing Data Literacy 307

8.6.2 Proficiency in AI and Machine Learning 308

8.6.3 Understanding of Agile and Lean Methodologies 309

8.6.4 Understanding the Human Side of Quality 310

8.6.5 Understanding Holistic View of Quality 311

References 312

Index 315
Kai Yang, Ph.D., is a Professor in the Department of Industrial and Systems Engineering at Wayne State University. He is a Fellow of both the American Society of Quality and the Institute of Industrial and Systems Engineers, and he was awarded the Cecil C. Craig Lifetime Achievement Award by the Automotive Division of American Society of Quality in 2016. Dr. Yang has consulted on quality control projects for General Motors, Ford, and Siemens.