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

✍ Scribed by Amit Kumar Tyagi (editor), Shrikant Tiwari (editor), Sayed Sayeed Ahmad (editor)

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 389
• Edition: 1
• Category: Library

✦ Synopsis

Industry 4.0 is a revolutionary concept that aims to enhance productivity and profitability in various industries through the implementation of smart manufacturing techniques. This book discusses the profound impact of Industry 4.0, which involves the seamless integration of digital technologies into manufacturing processes within the realm of industrial engineering.

Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities thoroughly examines the intricate facets of Industry 4.0 and Smart Manufacturing, offering a comprehensive overview of the challenges and opportunities that this paradigm shift presents to industrial engineers. It provides practical insights and strategies to help professionals navigate the complexities of this evolving landscape. Fundamental components of Industry 4.0 and Smart Manufacturing, ranging from the incorporation of sensors and data analytics to the deployment of cyber-physical systems and the promotion of sustainable practices are covered in detail. The book addresses the obstacles and prospects brought about by Industry 4.0 in the digital age and offers solutions to issues such as data security, interoperability, and workforce preparedness.

The book sheds light on how Industry 4.0 combines various disciplines, including engineering technology, data science, and management. It serves as a valuable resource for researchers, undergraduate and postgraduate students, as well as professionals operating in the field of industrial engineering and related domains.

✦ Table of Contents

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