Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities (Advances in Intelligent Decision-Making, Systems Engineering, and Project Management)

Cover
Half Title
Series
Title
Copyright
Contents
Preface
About the Editors
Contributors
Chapter 1 Introduction to Industry 4.0
	1.1 Introduction: Industry 4.0—What It Is and Why It Matters
	1.2 The Progression of the Numerous Industrial Revolutions
	1.3 Key Objective of Industry 4.0
	1.4 Key Features of Industry 4.0
	1.5 Technologies Foundational to Industries 4.0
	1.6 Industry 4.0 Significant Implications for Various Industries
	1.7 Response of Manufacturing to Industry 4.0
	1.8 Intelligent Supply Chain and Logistics
	1.9 Customization and Personalization of Products
	1.10 Workforce Transformation and Human-Machine Collaboration
	1.11 Production Benefits from Industry 4.0
	1.12 The Struggles of Manufacturing with Industry 4.0
	1.13 Response of Communication to Industry 4.0
	1.14 Positive Communication Effects of Industry 4.0
	1.15 The Struggles of Communication with Industry 4.0
	1.16 Transportation’s Approach to Industry 4.0
	1.17 Industry 4.0’s Beneficial Effects on Transportation
	1.18 The Struggles of Transportation with Industry 4.0
	1.19 The Response of Healthcare in Industry 4.0
	1.20 Positive Effects of Industry 4.0 on Healthcare
	1.21 The Struggles of Healthcare with Industry 4.0
	1.22 Industry 4.0: The Hurdles and Promises
	1.23 Skills Gap and Workforce Readiness
	1.24 Ethical and Social Implications
	1.25 New Business Models and Market Disruptions
	1.26 Case Studies and Success Stories of Industry 4.0
	1.27 Future Trends and Outlook of Industry 4.0
	1.28 Conclusion
	References
Chapter 2 Security Concerns and Controls of Intelligent Cobots of Industry 4.0
	2.1 Introduction: Background and Driving Forces
	2.2 Industry 4.0 and Industry 5.0
	2.3 Cobots and Intelligent Robots
		2.3.1 Fanuc CRs
		2.3.2 TM-Omron Cobots
		2.3.3 Panda
	2.4 Security Concerns and Threat Landscape
	2.5 Attack Surface and Attack Tree
	2.6 Security Controls and Mechanisms
	2.7 Conclusions
	References
Chapter 3 Big Data Analytics (BDA) for Industry 5.0
	3.1 Introduction
		3.1.1 Role of Big Data and Analytics
	3.2 Big Data and Analytics: Concepts and Principles
		3.2.1 What is Big Data?
		3.2.2 Key Features of Big Data
		3.2.3 Analytics and Understandings from Big Data
		3.2.4 Analytics in Industry 5.0 with Big Data
	3.3 Data Collection and Management in Industry 5.0
		3.3.1 Data Sources and Types
		3.3.2 Data Collection Methods and Technologies
		3.3.3 Data Storage and Integration
		3.3.4 Data Quality and Governance
	3.4 Big Data Analytics Techniques and Technologies
		3.4.1 Hadoop
		3.4.2 Apache Spark
		3.4.3 NoSql Databases
		3.4.4 Machine-Learning Algorithms
		3.4.5 Data Mining
		3.4.6 NLP (Natural Language Processing)
		3.4.7 Data Visualization
		3.4.8 Graph Database
		3.4.9 Time Series Analysis
		3.4.10 Descriptive Analytics
		3.4.11 Diagnostic Analytics
		3.4.12 Predictive Analytics
		3.4.13 Prescriptive Analytics
		3.4.14 Machine Learning and Artificial Intelligence in Analytics
	3.5 Applications of Big Data and Analytics in Industry 5.0
		3.5.1 Smart Cities and Urban Planning
		3.5.2 Healthcare and Personalized Medicine
		3.5.3 Transportation and Traffic Management
		3.5.4 Energy Management and Sustainability
		3.5.5 Education and Learning Analytics
		3.5.6 Case Studies 1: Successful Implementations of Big Data and Analytics in Industry 5.0
	3.6 Ethical Issues and Privacy in Big Data Analytics
		3.6.1 Privacy Protection and Data Security
		3.6.2 Transparency and Explainability in Analytics
		3.6.3 Ethical Use of Big Data and Algorithmic Bias
		3.6.4 Regulatory and Legal Frameworks
	3.7 Challenges and Opportunities in Big Data Analytics
		3.7.1 Data Volume, Velocity, and Variety
		3.7.2 Data Integration and Interoperability
		3.7.3 Research Gap in Skills and Talent
		3.7.4 Trust and Data Sharing
		3.7.5 Data-Driven Decision Making and Governance
	3.8 Future Trends and Emerging Technologies
		3.8.1 Edge Analytics and Real-time Insights
		3.8.2 Internet of Things (IoT) and Big Data Integration
		3.8.3 Federated Learning and Privacy-Preserving Analytics
	3.9 Conclusion
	References
Chapter 4 Machine Learning—Enabled Predictive Analytics for Quality Assurance in Industry 4.0 and Smart Manufacturing: A Case Study on Red and White Wine Quality Classification
	4.1 Introduction: Navigating Industry 4.0 for Wine Quality Assurance
	4.2 Industry Evolutions
		4.2.1 Industry 1.0
		4.2.2 Industry 2.0
		4.2.3 Industry 3.0
		4.2.4 Industry 4.0
	4.3 Industry 4.0 and Smart Manufacturing
	4.4 Quality Assurance within the Industry 4.0 Framework
	4.5 Machine Learning in Industry 4.0 and Smart Manufacturing
		4.5.1 Smart Factories and Data Collection
		4.5.2 Quality Control and Overall Equipment Effectiveness (OEE)
		4.5.3 Predictive Maintenance and Process Optimization
		4.5.4 Challenges and Opportunities
		4.5.5 Increased Productivity and Quality Control
	4.6 Machine Learning Algorithms
		4.6.1 The Decision Tree
		4.6.2 Support Vector Machines (SVM)
		4.6.3 Random Forest
		4.6.4 Linear Regression
		4.6.5 k-Nearest Neighbors
	4.7 Literature Review: Unraveling Insights and Context
	4.8 Methodology
		4.8.1 Dataset Source
	4.9 Experimental Result and Analysis
		4.9.1 Red and White Wine Quality Prediction Results
	4.10 Comparison of Accuracy by Different Algorithm
	4.11 Summary Conclusion Recommendation for Further Work
	References
Chapter 5 Leveraging Clustering Algorithms for Predictive Analytics in Blockchain Networks
	5.1 Introduction
		5.1.1 Brief Overview of Data Science and Its Advancements
		5.1.2 Role of Machine Learning and AI in Extracting Actionable Insights
		5.1.3 Thermochemical Routes for Biomass Conversion to Fuels
	5.2 Predictive Analytics with Clustering
		5.2.1 Understanding K-Clustering and Its Significance
		5.2.2 Using Clustering to Optimize Operations
		5.2.3 Making Data-Driven Decisions Using K-Clustering
	5.3 Blockchain and Data Management
		5.3.1 Introduction to Blockchain Technology
		5.3.2 The Growing Role of Blockchain in Data Management
		5.3.3 Challenges and Opportunities of Integrating Predictive Analytics with Blockchain
	5.4 Implementing Clustering Algorithms in Blockchain Networks
		5.4.1 Rationale for Implementing Clustering Algorithms in Blockchain
		5.4.2 How Nodes in Blockchain Networks Can Execute K-Means Clustering
		5.4.3 Sharing Clustering Results Back to the Blockchain Network
	5.5 Efficient and Effective Clustering Techniques in Blockchain Networks
		5.5.1 Challenges in Implementing Clustering in a Decentralized Environment
		5.5.2 Potential Real-World Applications
	5.6 Conclusion and Future Directions
		5.6.1 Recap of the Key Takeaways
		5.6.2 The Growing Potential of Predictive Analytics in Blockchain Networks
		5.6.3 Potential Future Developments and Research Directions
	References
Chapter 6 Use of Digital Twin and Internet of Vehicles Technologies for Smart Electric Vehicles in the Manufacturing Industry
	6.1 Introduction
		6.1.1 Role of AI in Electric Vehicles
		6.1.2 Motivation and Contribution
		6.1.3 Research Objectives
	6.2 Critical Survey of Existing Methods
	6.3 Proposed Methodology
		6.3.1 Prerequisites
		6.3.2 Methodology
	6.4 Performance and Discussion
	6.5 Conclusion and Future Scope
	References
Chapter 7 AI Applications in Production
	7.1 Introduction
		7.1.1 Objectives of AI Applications in Production
		7.1.2 Organization of the Chapter
	7.2 The Essence of AI Applications in Production
	7.3 AI Models Optimization Techniques and Pipelines
		7.3.1 Machine Learning Approaches
		7.3.2 Machine Learning Algorithms
	7.4 Machine Learning Workflow
		7.4.1 Machine Learning Task
	7.5 AI/ML Applications in Manufacturing
	7.6 Future of AI Applications in Production
	7.7 Case Studies
	7.8 Conclusion
	References
Chapter 8 IoT-Driven Supply Chain Management: A Comprehensive Framework for Smart and Sustainable Operations
	8.1 Introduction: Background
	8.2 Existing System
	8.3 System Architecture
	8.4 Proposed Work
	8.5 Results and Discussions
		8.5.1 Vertical Integration
		8.5.2 Horizontal Integration
	8.6 Conclusion and Future Works
	References
Chapter 9 Supply Chain Management in the Digital Age for Industry 4.0
	9.1 Introduction: Industry 4.0 and Supply Chain Management
	9.2 Digital Transformation of the Supply Chain
	9.3 Impact on Key Supply Chain Functions
	9.4 Managing the Change: Challenges and Opportunities
	9.5 The Future of SCM in Industry 4.0
	9.6 Case Studies
		9.6.1 DHL and Fetch Robotics: A Warehouse Dance with Robots
		9.6.2 Bosch Automotive: Where Machines Waltz with Data and Efficiency Sings
		9.6.3 Unilever: Where Palm Oil Whispers Its Origins and Transparency Takes Center Stage
		9.6.4 Nestlé: Where Supply Chains Dance with Digital Doppelgangers
		9.6.5 Maersk: Where Currents Whisper Secrets and AI Steers the Course
		9.6.6 Siemens: Where Data Dances Across Silos and Innovation Takes Center Stage
	9.7 Conclusion
	References
Chapter 10 Artificial Intelligence, Computer Vision and Robotics for Industry 5.0
	10.1 Role of Artificial Intelligence (AI), Computer Vision and Robotics
		10.1.1 Objectives of the Work
	10.2 Artificial Intelligence (AI) in Industry 5.0
		10.2.1 What is Artificial Intelligence?
		10.2.2 Key Components and Techniques in AI
		10.2.3 Applications of AI in Industry 5.0
		10.2.4 Benefits and Challenges of AI in Industry 5.0
	10.3 Computer Vision in Industry 5.0
		10.3.1 What is Computer Vision?
		10.3.2 Core Concepts and Techniques in Computer Vision
		10.3.3 Applications of Computer Vision in Industry 5.0
		10.3.4 Benefits and Challenges of Computer Vision in Industry 5.0
	10.4 Robotics in Industry 5.0
		10.4.1 What are Robotics and Industrial Automation?
		10.4.2 Types of Robots in Industry 5.0
		10.4.3 Applications of Robotics in Industry 5.0
		10.4.4 Benefits and Challenges of Robotics in Industry 5.0
	10.5 Integration of AI, Computer Vision and Robotics in Industry 5.0
		10.5.1 Synergies and Interplay of Technologies
		10.5.2 Intelligent Robotics and Automation Systems
		10.5.3 Cognitive Vision and Perception
		10.5.4 Human-Robot Collaboration and Interaction
		10.5.5 Case Studies: Successful Implementations in Industry 5.0
	10.6 Impacts and Benefits of AI, Computer Vision and Robotics in Industry 5.0
		10.6.1 Enhanced Productivity and Efficiency
		10.6.2 Improved Quality and Precision
		10.6.3 Safety and Risk Reduction
		10.6.4 Human Workforce Augmentation
		10.6.5 Ethical and Social Issues
	10.7 Challenges and Issues in Adopting AI, Computer Vision and Robotics for Industry 5.0
		10.7.1 Technical Complexity and Integration
		10.7.2 Safety and Security Concerns
		10.7.3 Workforce Adaptation and Skills Development
	10.8 Future Trends and Opportunities
		10.8.1 Advancements in AI, Computer Vision and Robotics
		10.8.2 Human-Centric Robotics and Assistive Technologies
		10.8.3 Explainable AI and Ethical Frameworks
		10.8.4 Collaborative Robotics and Co-robotics
	10.9 Conclusion
	References
Chapter 11 Data Analytics and Decision-Making in Industry 4.0
	11.1 Introduction: Background
	11.2 Data Analytics in Industry 4.0
	11.3 Decision-Making in Industry 4.0
	11.4 Conclusion and Future Directions
	References
Chapter 12 Evolving Landscape of Industrial Engineering in the Modern Era
	12.1 Introduction
		12.1.1 Definition of the Fourth Industrial Revolution
		12.1.2 Historical Background
		12.1.3 Importance of the Fourth Industrial Revolution
	12.2 Key Technologies of the Fourth Industrial Revolution
		12.2.1 Artificial Intelligence
		12.2.2 Internet of Things
		12.2.3 Big Data and Analytics
		12.2.4 Robotics
		12.2.5 Blockchain Technology
	12.3 Impacts of the Fourth Industrial Revolution on Society
		12.3.1 Economic Impacts
		12.3.2 Social Impacts
		12.3.3 Environmental Impacts
		12.3.4 Ethical Considerations
	12.4 Opportunities of the Fourth Industrial Revolution
		12.4.1 Innovation and Growth Opportunities
		12.4.2 Increased Efficiency and Productivity
		12.4.3 Improved Quality of Life
	12.5 Future Outlook
		12.5.1 Predictions and Projections for the Future of the Fourth Industrial Revolution
		12.5.2 Possible Policy Interventions and Regulations
	12.6 Conclusion
	References
Chapter 13 Artificial Intelligence (AI)-Enabled Digital Twin Technology in Smart Manufacturing
	13.1 Introduction
		13.1.1 How Do Digital Twins Work?
		13.1.2 The Confluence of Artificial Intelligence and Digital Twins
		13.1.3 Human-Focused Digital Twin
		13.1.4 Collaboration between Human-Robot
		13.1.5 Digital Twins and Cyber-Physical Systems
	13.2 Related Work
	13.3 Essential Elements of AI-Enabled Digital Twin (DT)
		13.3.1 The Internet of Things (IoT) and Sensors
		13.3.2 Information Analysis and Data Processing
		13.3.3 Various Algorithms for Machine Learning
		13.3.4 Virtualization in the Cloud and Modeling
	13.4 Use Cases in Smart Manufacturing
		13.4.1 Prognostic Maintenance
		13.4.2 Maximizing Efficiency
		13.4.3 Quality Assurance
	13.5 Difficulties and Potential Benefits
		13.5.1 Difficulties
		13.5.2 Potential Benefits
	13.6 Conclusion
	References
Chapter 14 Smart Manufacturing: Navigating Challenges, Seizing Opportunities, and Charting Future Directions—A Comprehensive Review
	14.1 Introduction
	14.2 System Overview: Understanding the Foundation
		14.2.1 Framework of Smart Manufacturing Systems
		14.2.2 Application of IoT in Process Industries
	14.3 Navigating the Landscape of Smart Manufacturing Standards
	14.4 Exploring the Characteristics and Challenges
		14.4.1 Addressing Security Challenges in the Era of Smart Manufacturing
		14.4.2 System Integration in Smart Manufacturing
		14.4.3 Interoperability Challenges in Smart Manufacturing
		14.4.4 Ensuring Safety in Human-Robot Collaboration
		14.4.5 Multilingual Capabilities in Smart Manufacturing Systems
		14.4.6 Return on Investment Analysis for New Technology Adoption
	14.5 Prospects and Emerging Trends in Smart Manufacturing
		14.5.1 SMEs Embrace Intelligent Manufacturing
		14.5.2 Automation Potential in Key Sectors
		14.5.3 Intent to Upgrade Manufacturing Systems
		14.5.4 Disparity between Current and Future Systems
		14.5.5 Enhancing Industrial Automation
		14.5.6 Transition to Intelligent Manufacturing
	14.6 Conclusion
	References
Chapter 15 Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare
	15.1 Introduction
		15.1.1 Manufacturing
		15.1.2 Communication
		15.1.3 Transportation
		15.1.4 Healthcare 4.0
	15.2 Literature Survey
	15.3 Smart Communication: Empowering Connectivity in Industry 4.0
		15.3.1 Communication Revolution: Industry 4.0’s Influence on the Way We Connect
		15.3.2 Intelligent Communication: Harnessing Industry 4.0 Technologies for Enhanced Connectivity
		15.3.3 The Future of Communication: Industry 4.0’s Transformational Impact
	15.4 The Digital Transformation of Healthcare
		15.4.1 Harnessing the Power of Industry 4.0 in Healthcare
		15.4.2 Integration of Technology in the Healthcare Sector
		15.4.3 The Role of Industry 4.0 in Revolutionizing Healthcare
		15.4.4 Advancements in Healthcare through Industry 4.0 Technologies
		15.4.5 Industry 4.0: Shaping the Future of Healthcare Delivery
		15.4.6 Smart Healthcare Systems and Industry 4.0 Innovations
		15.4.7 Transforming Patient Care with Industry 4.0 Technologies
	15.5 Industry 4.0 in Manufacturing
	15.6 Industry 4.0 in Transportation
		15.6.1 Resource Management
		15.6.2 Warehouse Management
		15.6.3 Intelligent Transportation Systems
		15.6.4 Information Sharing and Security
	15.7 Conclusion
	References
Chapter 16 Artificial Intelligence-Based Anomaly Detection for Industry 4.0: A Sustainable Approach
	16.1 Introduction
	16.2 Related Work
	16.3 Evolution of Industry 4.0
		16.3.1 Characteristics of a Smart Factory
		16.3.2 Industry Revolution
		16.3.3 Key Technologies
	16.4 Anomaly Detection
	16.5 AI Models for Anomaly Detection
		16.5.1 Anomaly Detection in Industry 4.0
		16.5.2 The Advantages of AI
		16.5.3 Sustainability
	16.6 Conclusion
	References
Chapter 17 Future of Industry 5.0 in Society 5.0: Human-Computer Interaction-Based Solutions for Next Generation
	17.1 Introduction
	17.2 Literature Review
	17.3 Industry 5.0—Emergence, Role of Ethics in Industry 5.0, Human-Centric Smart Machine (HSM Approach)
	17.4 Society 5.0
	17.5 Society 5.0, Industry 5.0, and HCI Integration
	17.6 Future Trends
	17.7 Conclusion
	References
Chapter 18 The Future of Manufacturing and Artificial Intelligence: Industry 6.0 and Beyond
	18.1 Introduction
	18.2 Literature Review
	18.3 Evolution of Industry Revolution
	18.4 Necessity of Smart Manufacturing
	18.5 Benefits and Limitations Towards Automated/Human Machine Collaboration Towards Industry Automation
		18.5.1 Benefits
		18.5.2 Limitations
	18.6 Open Issues and Challenges Towards Industry 6.0 and Beyond
	18.7 Future Research Opportunities Towards Industry 6.0 and Beyond
	18.8 A Future with Emerging Technologies for Effective and Sustainable Industry 6.0-Based Environment
	18.9 Conclusion
	References
Index