Connected intelligent automated: the definitive guide to digital transformation and quality 4.0

Description

About This Book Preface Who This Book Is For Quality 4.0 Is for All Industries Why I Wrote This Book My Industry 4.0/Quality 4.0 Story How This Book Is Structured Moving Forward References Chapter One: Quality 4.0 and the Fourth Industrial Revolution The Fourth Industrial Revolution Why Now? The Impact of Industry 4.0 on Quality Quality 4.0: The Next Generation of Excellence Connectedness Intelligence Automation Discovery: The New Role of Quality Value Propositions for Quality 4.0 Quality Professionals: Leading the Transformation Brave New World The Bottom Line References Chapter Two: Connected Ecosystems Natural and Organizational Ecosystems Infrastructure Connectivity and 5G Networks Internet Protocol Version 6 (IPv6) Cloud Computing Software-Defined Systems Edge Computing Cyber-Physical Systems (CPS) People Protocols and Procedures Social Media and Engagement Chatbots and Intelligent Conversational Agents Random Collisions of Unusual Suspects (RCUS) Objects and Machines Operations Technology (OT) Industrial Internet of Things (IIoT) Internet of Things (IoT) Sensors, Actuators, and Microcontrollers Radio Frequency Identification (RFID) and Beacons Digital Twins Robotics Wearables 3D Printing Smart Materials and 4D Printing Neurotechnologies Data Structured and Unstructured Data Streaming Data and Analysis Pipelines Data Lakes and Data Warehouses Virtual, Augmented, and Diminished Reality (VR/AR/DR) The Bottom Line References Chapter Three: Intelligent Agents and Machine Learning Models Drive Continuous Improvement What Is Learning? Learning Fuels Continuous Improvement Machine Learning Artificial Intelligence The Hype The Reality: Strong and Weak AI Intelligent Agents Examples of AI ML Problem Types Finding Patterns (Unsupervised Learning) Identifying Key Predictors (Data Reduction) Identifying Irregularities (Anomaly Detection) Learning from Examples (Supervised Learning) Learning from Experience (Reinforcement Learning) The Social Context of Intelligent, Connected Systems Silly and Embarrassing Potentially Damaging If Used Incorrectly Potentially Trauma Inducing, Hostile, or Deadly Challenging to Social Norms, Behaviors, and Regulations The Bottom Line References Chapter Four: Automation: From Manual Labor to Autonomy Mechanization and Automation Innovation Happens at the Edges Reasons for Automation Building Blocks for Automation Industrial Control System (ICS) Levels of Automation Where Automation Occurs What Kind of Automation Can Be Done How Humans, Machines, and Computers Automate Examples of Automation Robotic Automation Robotic Process Automation (RPA) Design Automation Automatic Code Generation Test Automation Automation Acceptance Preserving Agency and Countering Resistance Technology Follows Behavior Workforce Implications The Bottom Line References Chapter Five: Quality 4.0 Use Cases across Industries Connected, Intelligent, and Automated Key Themes in Industry 4.0 and Quality 4.0 Patents: 1990–2014 Industry 4.0 Use Cases Literature Review and Text Analysis New Business Models Quality 4.0 Case Studies Case 1: Healthy Operator 4.0 Case 2: Augmented Worker Case 3: Logistics 4.0 Case 4: Leaner Management Case 5: Smart Labels Case 6: Predictive Maintenance/Smart Asset Management Case 7: Rail Accident Prevention Case 8: Olive Oil Supply Chain Case 9: Medical Process Control with Blockchain Case 10: Pharma 4.0 The Bottom Line Acknowledgments References Chapter Six: From Algorithms to Advanced Analytics Analytics and Business Intelligence From Analytics to Advanced Analytics Descriptive, Diagnostic, Predictive, Prescriptive Business Intelligence What Is an Algorithm? Analog Computing and Algorithms Examples of Algorithms and Non-Algorithms Introducing Data Science Quality Considerations Lessons for Quality Assurance The Bottom Line Acknowledgments References Chapter Seven: Delivering Value and Impact through Data Science The Cascadia Story Investigating the Ghost Forest Insights from Historical Records Lessons from Cascadia What Is Data Science? The Death of the Scientific Method The Evolution of Data-Driven Decision Making Why Is Data Science So Popular? What Does a Data Scientist Do? Data Science and Big Data What Is the Data Deluge? Do All Data Scientists Work with Big Data? Solid Statistical Foundations Are Essential Survivorship Bias The Importance of Sampling Lack of Theory Can Be Expensive Statistical Engineering Facilitates Data Science Impact The Bottom Line References Chapter Eight: Data Quality and Data Management Driving Value from Data Assets in Industry 4.0 Organizing Data Management and Governance Data Quality Causes and Consequences of Bad Data Quality Identifying and Measuring Data Quality Dimensions Data Quality in IoT Environments Data Management Master Data Management System of Record ISO/TS 8000 Data Governance Elements of a Data Governance Framework Governance for Cyber-Physical Systems The Bottom Line References Chapter Nine: Software Applications and Data Platforms Convergence The ISA-95 Models ISA-95 Level 2: Monitoring and Supervising Distributed Control System (DCS) Historian Human-Machine Interfaces (HMIs) Intelligent Devices Supervisory Control and Data Acquisition (SCADA) Safety Instrumented System (SIS) ISA-95 Level 3: Operations Management Deviations Enterprise Asset Management (EAM) Environment, Health, and Safety (EHS) Laboratory Information Management System (LIMS) Manufacturing Execution System (MES) Measurement Systems Analysis (MSA) Security Incident and Event Management (SIEM) Statistical Process Control (SPC) ISA-95 Level 4: Business Planning and Logistics Advanced Product Quality Planning (APQP) Asset Performance Management (APM) Customer Relationship Management (CRM) Document Control Enterprise Resource Planning (ERP) Good Manufacturing Practice (GMP)/Prerequisite Programs (PRPs) Hazard Analysis and Critical Control Points (HACCP) Product Life-Cycle Management (CAD/CAM, ETO, MTO) Production Part Approval Process (PPAP) Quality Management System (QMS) Risk Management (DFMEA, PFMEA, Control Plans) Supply Chain Management (SCM) Training and Certification Management Level 5: Strategy and Governance Corporate Performance Management (CPM) Governance, Risk Management, and Compliance (GRC) Knowledge Management Organizational Risk Management (ORM) Sustainability Performance Indicators (SPIs) Data Platforms The Bottom Line References Chapter Ten: Blockchain What Is Blockchain? Permissioned versus Permissionless Bitcoin versus Blockchain Growing the Blockchain How Blockchain Works Hashing Algorithms Hash My Cat The Value of Blockchain Case Studies Walmart: Safer Food with Instant Traceability IBM Food Trust: A Hyperledger Exemplar Circular Economy: The Textile Industry Energy Demand Management The Future of Auditing Is a Blockchain Solution Appropriate? The Bottom Line References Chapter Eleven: Performance Excellence What Is Performance Excellence? Core Values Performance Excellence Frameworks ISO 9001:2015 Total Quality Management (TQM) Lean Management Six Sigma/Lean Six Sigma Shingo Model Capability Maturity Model Integration (CMMI) Agile Methods European Foundation for Quality Management (EFQM) Baldrige Excellence Framework (BEF) What Makes Them Work The Bottom Line References Chapter Twelve: Environment, Health, Safety, Quality (EHSQ), and Cybersecurity Human Factors That Influence Quality Environment Compliance Sustainability Health and Safety Ergonomics in Industry 4.0 Personal Protective Equipment (PPE) and Wearables Human-Machine Interaction Cybersecurity SEMA Referential Framework NIST Cybersecurity Framework (NIST CSF) Baldrige Cybersecurity Excellence Builder (BCEB) Quality Improvement in Environment, Health, and Safety (EHS) Quality Improvement in Cybersecurity The Bottom Line References Chapter Thirteen: Voice of the Customer (VoC) The Evolving Voice of the Customer (VoC) A Four-Stage VoC Process Stage I: Identify Customer Needs Stage II: Understand and Prioritize Needs Stage III: Create Meaningful Customer Experiences Stage IV: Anticipate Future Needs Case Study: VoC Architecture at Fuji Xerox The Bottom Line Acknowledgments References Chapter Fourteen: Elements of a Quality 4.0 Strategy The Vision for Quality 4.0 Drivers for Digital Transformation Organizational Drivers External Drivers Workforce Drivers Case Studies: From Drivers to Digital Outcomes Audi: Big Data Analytics Media Corp: Connected Products and Services Nikon: Real-Time Measurement Sweden: Near-Real-Time Dementia Monitoring Kaiser Permanente: Connected Customer Engagement Learning from Success Readiness and Maturity Models Meta-Analyses of Digital Transformation Recipe for a Digital Foundation The Baldrige Excellence Framework (BEF) The Bottom Line References Chapter Fifteen: Playbook for Transformation Planning for Digital Transformation Step 1: Who Are You? Step 2: Why Transform? Why Now? Step 3: How Can You Transform? Brainstorming Contextualization Gap Analysis Step 4: How Should You Transform? Step 5: How Much Should You Transform? Strategic Use of Technology Value Creation Structural Changes Financial Considerations Step 6: How Will You Measure Success? Example The Bottom Line References Appendix A: Index of Statistical and Machine Learning Models Algorithms and Examples Artificial Neural Networks C5.0 Decision Trees Deep Learning Dynamic Time Warping Expert Systems K-Means Clustering K-Nearest Neighbors Latent Dirichlet Allocation (LDA) Logistic Regression Naive Bayes Classification Principal Component Analysis (PCA) Q-Learning Random Forests Support Vector Machine (SVM) Sentiment Analysis References Appendix B: Digital Transformation Planning Checklist Glossary of Terms About the Author