In the world of industrial manufacturing and supply chain, the lack of real-time visibility and insights into processes poses a significant challenge. However, IoT is set to bring about a profound transformation. This technological revolution promises efficiency gains, operational optimization, and unprecedented business insights. Step into the world of Industry 4.0 and 5.0 with IoT and discover how it revolutionizes production and logistics. Learn about real-time monitoring, predictive maintenance, and quality control while ensuring a secure IoT infrastructure. Explore practical examples in manufacturing, including smart factories, personalized transit, and sustainability practices. Use the potential of AI, predictive analytics, and 3D printing to align your IoT strategies with business goals for enhanced performance. Completing this book equips readers to excel in leveraging IoT for industrial manufacturing and supply chain advancements. They will master IoT concepts, optimize processes, and handle integration challenges. With the acquired knowledge, readers can develop strong IoT strategies, assess project outcomes effectively, and introduce significant improvements to their manufacturing and supply chain operations.

• Cover
• Title Page
• Copyright Page
• Dedication Page
• About the Authors
• About the Reviewers
• Acknowledgement
• Preface
• Table of Contents
• 1. IoT Fundamentals, Architecture, and Protocols
  • Introduction
  • Structure
  • Objectives
  • Overview
  • The digital revolution: From Industry 4.0 to 5.0
    • Dawn of Industry 4.0
    • Key technologies driving Industry 4.0
      • Internet of Things
      • Cyber-physical system
      • Big data analytics
      • Robotics and automation
      • Additive manufacturing
    • Industry 5.0: The human-machine interaction
      • Human-machine coexistence
      • Human-machine cooperation
      • Human-machine collaboration
    • Difference between Industry 4.0 and 5.0
  • Role of IoT in manufacturing
    • Enabling predictive maintenance
    • Optimizing production planning
    • Enabling quality control
  • Role of IoT in supply chain
    • Real-time tracking and tracing
    • Enhancing coordination and collaboration
    • Demand forecasting and inventory management
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 2. Embracing IoT in Manufacturing
  • Introduction
  • Structure
  • Objectives
  • Real-time monitoring and maintenance
    • Need for real-time monitoring
    • Real-time monitoring system architecture
    • IoT and predictive maintenance
    • Case study: Smart predictive maintenance
  • IoT driven production planning
    • IoT for real-time production tracking
      • IoT devices and sensors
    • Case study: IoT in manufacturing
  • Ensuring quality control with IoT
    • Traditional quality control methods
    • IoT-enabled approach
    • IoT for automated inspection and testing
      • Automated in-line inspection
      • Unmanned inspection and hazardous environments
      • Micro and nano-precision testing
      • Consistency and objectivity
      • Proactive testing and predictive maintenance
      • Inspection integration across facilities
    • Data analysis for quality assurance: IoT’s role
      • Data management challenges
    • Case study: Use of IoT in quality control
      • Case study 1
      • Case study 2
      • Case study 3
      • Case study 4
      • Case study 5
      • Case study 6
      • Case study 7
      • Case study 8
      • Case study 9
      • Case study 10
      • Case study 11
      • Case study 12
  • Robotics in manufacturing
    • Innovation and automation with robotics
      • History and evolution of robotics in manufacturing
      • Increased robotics integration in manufacturing
    • IoT-robotics synergy: Ensuring precision and safety
    • IoT for robot maintenance and troubleshooting
    • Case study: Robotic solutions powered by IoT
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 3. The Power of IoT in Supply Chain
  • Introduction
  • Structure
  • Objectives
  • Overview
  • Demand forecasting
    • IoT based demand forecasting
    • Analyzing real-time data for accurate forecasts
    • IoT and AI for predictive demand forecasting
      • Retail
      • Consumer electronics
    • Case studies
  • Inventory management
    • Challenges of inventory management
    • IoT in real-time inventory management
    • IoT and predictive analysis
    • Case study: IoT for inventory management
  • Intelligent logistics: Enhancing delivery with IoT
    • Redefining logistics with IoT
    • Real-time tracking: Role of IoT in transit visibility
    • Enhancing fleet management with IoT
  • Smart warehousing with IoT
    • Concept of a smart warehouse
    • IoT devices in warehousing: RFID, sensors, and more
    • Impact of IoT on warehouse operations
    • Case study: IoT-driven warehouses
  • Reverse logistics with IoT
    • Current challenges with reverse logistics
    • IoT improvements in reverse logistics
    • Reverse logistics and forward logistics
    • Future of reverse logistics
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 4. IoT: Use Cases in Smart Factories
  • Introduction
  • Structure
  • Objectives
  • Case study 1: Smart warehousing insight with IoT
    • Warehouse layout
    • Warehouse operations overview
    • Heatmap
    • Resource indoor positioning
  • Case study 2: Smart delivery insights for ordered items
  • Case study 3: Smart contracts for supply chain
  • Case study 4: Smart packaging and monitoring
    • Smart packaging implementation process
    • Benefits of smart packaging
  • Case study 5: AR in manufacturing
    • Use case 1: AR technology for equipment maintenance
    • Use case 2: AR to transform worker training
  • Case study 6: Smart data entry
    • Data entry accuracy using CPA
    • Difference between CPA and traditional automation
    • Architecture of CPA IoT-driven manufacturing
  • Case study 7: Health and safety compliance monitoring
    • Cloud-based monitoring and analytics
    • Edge computing for real-time response
    • IoT solution strategies for OHS management
  • Case study 8: Smart material handling
  • Case study 9: Last-mile delivery optimization
  • Case study 10: Smart waste management
    • Inefficient waste collection
    • Illegal dumping
    • Recycling contamination
  • Case study 11: Smart Product Lifecycle Management
  • Case study 12: Real-time production performance
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 5. Business Factors and Optimization for IoT Implementation
  • Introduction
  • Structure
  • Objectives
  • Strategic objectives for IoT implementation
    • Aligning IoT initiatives with business strategy
  • Mapping IoT solutions to business cases, benefits, and KPIs
    • Use cases, benefits, and KPIs for embracing IoT in manufacturing
    • Use cases and benefits of IoT in supply chain
  • Mitigating risks in IoT implementation
    • Regulatory and compliance risks
    • Device reliability and maintenance risks
    • Technical risks
    • Operational risks
    • Ethical and social risks
  • Project planning for IoT implementation
  • Change management strategies for IoT adoption
  • Impact of IoT on organizational culture
  • Facilitating employee adoption and training
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 6. Challenges and Solutions
  • Introduction
  • Structure
  • Objectives
  • IoT cybersecurity concerns
    • Identifying security vulnerabilities in IoT
    • Manufacturing and supply chain IoT security threats
    • Solution strategies for mitigating security risks
    • IoT security in medical devices
  • Overcoming integration challenges
    • Challenges of IoT integration
    • IoT integration in supply chain and manufacturing
    • IoT integration in connected supply chain
    • Future of IoT integration in manufacturing
  • Ensuring reliable connectivity
    • Reliable connectivity for IoT applications
    • Common connectivity challenges and their impact
    • IoT connectivity in healthcare systems
    • Future of IoT connectivity
  • IoT and data privacy
    • Understanding IoT data privacy concerns
    • Balancing data utilization and privacy
    • Strategic approach to IoT and data privacy
    • Responsible data innovation
  • Architecting scalable and interoperable IoT
    • Scalability challenges in IoT implementations
    • Interoperability considerations for IoT systems
    • Strategies for scalable and interoperable IoT solutions
      • Maximizing ROI through design principles
  • Addressing latency issues with edge computing
    • Introduction to edge computing and its benefits in IoT
    • Edge computing in manufacturing and supply chain
    • Implementing edge computing for IoT
      • Edge computing in action: Optimized delivery
  • Data analytics and Machine Learning
    • Leveraging data analytics in IoT for predictive insights
    • ML for anomaly detection and optimization
    • Data analytics and Machine Learning in IoT
  • Energy efficiency and sustainability in IoT
    • Optimizing IoT energy use
    • Green IoT practices for sustainable supply chains
    • Energy efficiency and sustainability goals with IoT
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 7. Artificial Intelligence in Manufacturing
  • Introduction
  • Structure
  • Objectives
  • Introduction to AI systems
    • International perspective on AI adoption
  • Applications of AI in manufacturing
    • Enhancing worker safety using AI
      • Real-time monitoring using vision cameras
      • Hazard recognition powered by AI
      • Case study: Monitoring beyond human capacity
    • Supply chain optimization using AI
      • Demand forecasting using AI
      • Inventory management and procurement
    • Quality control and inspection using AI
      • Automated visual inspection using AI
      • Defect detection and classification
      • Enhancing product quality and reducing defects
    • Process optimization and production planning
      • AI-driven process optimization
      • Adaptive production planning with AI insights
      • Just-in-time manufacturing and resource utilization
  • Case studies and success stories
    • AI in automotive manufacturing
      • Automotive design using AI
      • Challenges of using AI
      • AI use cases in the automobile industry
    • AI in electronics manufacturing
      • LG CNS case study
      • Samsung case study
    • AI in pharmaceutical manufacturing
      • Drug discovery using AI-powered language models
      • Regulatory compliance and documentation improvement
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 8. The Future of IoT
  • Introduction
  • Structure
  • Objectives
  • AI predictive analysis using IoT
    • IoT predictive analytics with data analysis
      • Predictive modeling process overview
      • Data cleaning, processing, and transformation
      • Manufacturing use cases
    • Predictive models for IoT
    • Case study: AI-driven predictive analytics
    • Future directions for predictive analytics in IoT
  • Intersection of blockchain and IoT
    • Blockchain with IoT
    • Case study: Leveraging blockchain and IoT
    • Future of IoT blockchain
  • IoT sustainability in the supply chain
    • Case study: Sustainability efforts powered by IoT
    • Benefits and challenges of green supply chain
      • Benefits
      • Challenges
    • Future of sustainable IoT supply chain
  • 3D printing and IoT
    • Case study: Utilization of IoT in 3D printing
    • Benefits and challenges of IoT and 3D printing
      • Benefits
      • Challenges
    • Future trends of IoT and 3D printing
  • Big data analytics from IoT-generated data
    • Big data analytics for business insights
    • Case study: Big data analytics in healthcare
    • Future trends in big data analytics for IoT
  • Conclusion
  • Points to remember
  • Multiple choice questions
  • Answer key
  • Questions
  • Key terms
• 9. Key Takeaways
  • Introduction
  • Structure
  • Overview
  • Future trends of IoT
  • Role of emerging technologies in IoT
  • Conclusion
• Index