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Submit a ProposalSmart Analytics for Cold Chains
One Line Description
Discover how integrating AI, real-time IoT sensors, and predictive analytics transforms cold chain operations to safeguard your temperature-sensitive cargo, minimize costly spoilage, and achieve seamless regulatory compliance across your entire supply chain.
Description
With the increasing global demand for temperature-sensitive products such as pharmaceuticals, perishable foods, and biologics, maintaining precise environmental conditions is critical. By leveraging IoT sensors, artificial intelligence, and big data analytics, businesses can gain real-time insights into temperature fluctuations, transportation delays, and storage conditions, ultimately improving decision-making and reducing losses. This book is a comprehensive guide that explores the integration of data-driven technologies in the management and optimization of cold chain logistics. It delves into the role of smart analytics in enhancing efficiency, reducing waste, and ensuring regulatory compliance across the cold supply chain. Providing an in-depth analysis of key technologies driving smart cold chain analytics, including predictive analytics, machine learning algorithms, and blockchain for traceability, the book discusses how AI-powered forecasting models can optimize inventory management, detect anomalies, and mitigate risks in transit. By presenting case studies and real-world applications, the book offers practical insights into how companies can implement these advanced technologies to streamline operations and enhance product quality. Designed for supply chain professionals, researchers, and policymakers, this book serves as a valuable resource for anyone looking to modernize and enhance the efficiency of cold chain logistics through smart analytics.
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With the increasing global demand for temperature-sensitive products such as pharmaceuticals, perishable foods, and biologics, maintaining precise environmental conditions is critical. By leveraging IoT sensors, artificial intelligence, and big data analytics, businesses can gain real-time insights into temperature fluctuations, transportation delays, and storage conditions, ultimately improving decision-making and reducing losses. This book is a comprehensive guide that explores the integration of data-driven technologies in the management and optimization of cold chain logistics. It delves into the role of smart analytics in enhancing efficiency, reducing waste, and ensuring regulatory compliance across the cold supply chain. Providing an in-depth analysis of key technologies driving smart cold chain analytics, including predictive analytics, machine learning algorithms, and blockchain for traceability, the book discusses how AI-powered forecasting models can optimize inventory management, detect anomalies, and mitigate risks in transit. By presenting case studies and real-world applications, the book offers practical insights into how companies can implement these advanced technologies to streamline operations and enhance product quality. Designed for supply chain professionals, researchers, and policymakers, this book serves as a valuable resource for anyone looking to modernize and enhance the efficiency of cold chain logistics through smart analytics.
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Author / Editor Details
Ramesh Babu Damarla, PhD is an Associate Dean in the School of Business at SR University, Warangal. He has published 30 articles in international journals and 58 patents, 18 of which have been granted. His research focuses on controlled atmosphere storage, logistics, cold chain facility design, cold chain management, and project management.
Ramesh Babu Damarla, PhD is an Associate Dean in the School of Business at SR University, Warangal. He has published 30 articles in international journals and 58 patents, 18 of which have been granted. His research focuses on controlled atmosphere storage, logistics, cold chain facility design, cold chain management, and project management.
Usha Desai, PhD is a Professor and Dean of Research and Development for the SEA College of Engineering and Technology, Bengaluru, India. She has presented papers in many reputed conferences and authored more than 50 research publications, as well as several books. Her areas of interest include biomedicine, AI and machine learning in healthcare, and data science for pharmaceuticals.
Chandra Singh is an Assistant Professor in the Department of Electronics and Communication at Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, India with more than six years of experience. He has published nine books, 15 book chapters, and more than 20 research articles in reputed peer-reviewed national and international journals. His areas of interest include optical networking and communication, wireless communication, intelligent sytems, Internet of Things, and robotics.
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Table of Contents
Preface
Part I: Sustainable Cold Chain Systems: Sustainability, Carbon Footprint, Lifecycle Design
1. Smart Analytics for Cold Chains in Building Sustainable and Resilient Supply Chains
Sajja Suneel, Dankan Gowda V., K.D.V. Prasad, Naveen B. and I. Wayan Budi Sentana
Introduction
Understanding the Cold Chain
The Need for Smart Analytics in Cold Chains
Key Strategies for Implementing Smart Analytics in Cold Chains
Building Sustainable Cold Chains with Smart Analytics
Enhancing Resilience in Cold Chains with Smart Analytics
Case Studies and Success Stories
Challenges and Considerations
The Future of Smart Analytics in Cold Chains
Conclusion
References
2. Reducing Carbon Footprint in Cold Chain Logistics
Tithli Sadhu, Pooja Srivastav and Thokala Saikrishna Reddy
2.1 Introduction
2.2 Carbon Emissions in Agri-Food Cold Chains
2.2.1 Sources of Emissions Across Cold-Chain Stages
2.2.2 Impact of Globalization and Urbanization on Emissions
2.2.3 Role of Energy-Intensive Refrigeration and Transport
2.2.4 Case Examples of Emission Hotspots
2.3 Sustainable Interventions in Cold Chain Logistics
2.3.1 Overview of Sustainability in Cold Chains
2.3.2 Energy-Efficient Refrigeration Technologies
2.3.3 Renewable Energy Integration in Cold Storage and Transport
2.3.4 Smart Packaging Solutions
2.3.5 Internet of Things (IoT) and Real-Time Monitoring
2.3.6 Vehicle Route Optimization through Intelligent Transportation Systems (ITS)
2.4 Policy and Regulatory Frameworks
2.4.1 National and International Climate Goals
2.4.2 Standards and Guidelines for Sustainable Cold Chains
2.4.3 Incentives, Barriers, and Implementation Challenges
2.5 Fuzzy Multi-Criteria Decision-Making (MCDM) Approach
2.5.1 Need for Decision Support Under Uncertainty
2.5.2 Introduction to Fuzzy MCDM Techniques (Fuzzy AHP, Fuzzy TOPSIS)
2.5.3 Development of a Fuzzy MCDM Framework for Cold-Chain
Interventions
2.5.4 Criteria Selection: Environmental, Economic, and Operational
2.5.4.1 Environmental
2.5.4.2 Economic
2.5.4.3 Operational and Quality
2.6 Life Cycle Assessment (LCA) of Cold-Chain Carbon Footprint
2.7 Case Studies and Best Practices
2.7.1 Decarbonization Strategies from Developed and Developing Countries
2.7.2 Lessons from Diverse Agro-Climatic Regions
2.7.3 Sustainable vs. Conventional Cold Chains: What Changes
2.7.4 Putting Decision-Support to Work
2.8 Discussion
2.8.1 Economic–Environmental Trade-Offs
2.8.2 Technological Feasibility and Scalability
2.8.3 Decision-Making Implications for Stakeholders
2.8.4 Challenges and Limitations
2.9 Conclusion
References
3. Green Cold Chain Technology for Fish and Poultry Management: Innovations for Sustainable Food Preservation
Shreya Srivathsa, Prathibha Narayanan and Hadagali Ashoka
Introduction
Conventional Approaches to Cold Chain Management in Fish and Poultry
Emergence of Green Cold Chain Technology
Renewable Energy-Based Cold Storage Systems
Solar-Powered Cold Storage Systems
Vapor Absorption Refrigeration Systems
Eco-Friendly Refrigerants
Digital Intelligence and Real-Time Monitoring in Green Cold Chains
IoT-Enabled Sensor Networks
Blockchain-Enabled Digital Traceability in Green Cold Chains
Case Studies
Low-Emission Transport Refrigeration in Urban Food Cold Chains
Food Loss and Waste in Fish Cold Chains
Discussion
References
Part II: Digital Infrastructure and IoT-enabled Cold Chain Systems: IoT, Blockchain, Edge Computing
4. Intelligent Cold Chain Monitoring with Sensor Networks and Edge Computing
Srinivas Samala, Ch. Rajendra Prasad, Subhashree Mishra and Sudhansu Sekhar Singh
4.1 Introduction
4.1.1 Chapter Contributions
4.1.2 Background
4.1.3 Motivation
4.2 Related Work
4.3 System Architecture and Design
4.3.1 Sensor Network Layer
4.3.2 Edge Computing Layer
4.3.3 Cloud and Backend Layer
4.3.4 Data Flow and Communication
4.4 Methods and Algorithms
4.4.1 Sensor Data Acquisition and Preprocessing
4.4.2 Multi-Sensor Data Fusion
4.4.3 Anomaly Detection and Predictive Analytics
4.4.4 Adaptive Communication and Energy Management
4.4.5 Secure Data Transmission and Integrity
4.5 Implementation and Use Cases
4.5.1 Implementation
4.5.2 Use Cases
4.6 Results and Discussion
4.6.1 Latency of Anomaly Detection
4.6.2 Energy Consumption of Sensor Nodes
4.6.3 Data Transmission Volume
4.6.4 Detection Accuracy
4.6.5 System Uptime and Fault Tolerance
4.6.6 Alert Response Time Under Varying Network Conditions
4.7 Challenges and Future Directions
4.7.1 Challenges
4.7.2 Future Directions
4.8 Conclusion
References
5. Blockchain-Enabled Cold Chain Management Using IoT and Smart Contracts
Usha Desai, T. Sampath Kumar, D. Ramesh Babu, D. Mahesh and Karthik Sripad Rao
5.1 Introduction
5.2 Background
5.3 Literature Review
5.4 Blockchain-Based IoT Architecture for Cold Chain Systems
5.5 Smart Contracts in Cold Chain
5.6 Key Benefits of Blockchain-Based Cold Chain
5.7 Real-World Applications of Blockchain-Enabled Cold Chain Systems
5.7.1 Pharmaceutical Supply Chains
5.7.2 Food and Agriculture Logistics
5.7.3 Blood Banks and Organ Transport
5.7.4 Biotechnology and Laboratory Sample Management
5.7.5 Cold Chain in e-Commerce and Retail
5.7.6 Global Logistics and Cross-Border Shipping
5.7.7 Luxury Goods, Chemicals & High-Value Assets
5.7.8 Sustainability and ESG Reporting
5.8 Implementation Challenges of Blockchain-Enabled System
5.9 Conclusion
References
6. IoT-Enabled Cold Chain Tracking and Monitoring
Srividya P. and Siddharth A.
6.1 Introduction
6.2 IoT-Based Monitoring System
6.3 Cold Chain Logistics
6.4 Cold Chain Logistics Services Processes
6.5 Transformations of IoT in Cold Chain Logistics
6.6 Cold Chain vs. Supply Chain Differences
6.7 IoT-Based Cold Chain Monitoring
6.8 IoT-Based Cold Chain Logistics Integrated with Smart GPS
6.9 Visibility and Communication Challenges across Cold Chain Logistics
6.10 Need for IoT-Based Cold Chain Monitoring and Logistics Solutions
6.11 Real-World Use Cases for IoT-Based Cold Chain Monitoring
6.12 Future Trends and Opportunities
6.13 Enhancing Cost-Efficiency through Emerging Cold Chain
Technologies
6.14 Strengthening Risk Mitigation through Technological Advancements in the Cold Chain
6.15 Conclusion
References
7. IoT-Enabled Fruit and Vegetable Cold Chain Tracking, Monitoring, and Alert System
Ch. Rajendra Prasad and Moola Ramu
7.1 Introduction
7.2 Methodology and Methods
7.2.1 Hardware Components
7.2.2 Software
7.3 Results and Discussion
7.3.1 Experimental Setup
7.3.2 Results and Discussion
7.4 Conclusion
References
8. AquaChain: Blockchain-Enabled Water Management and Theft Detection System
Harsh Vardhan Jampani, Kartik Sripad Rao, Nisha N. D., Gagan S. and Usha Desai
8.1 Introduction
8.2 Literature Review
8.2.1 Materials and Methodology
8.2.1.1 Dataset Used
8.2.1.2 Data Processing Funnel
8.2.1.3 Mathematical Model
8.3 Results and Discussion
8.3.1 System Demonstration and Results
8.3.2 Discussion of Findings
8.3.3 System Performance Metrics
8.3.4 Data Analysis and Visualization
8.4 Conclusion
References
Part III: Artificial Intelligence and Machine Learning for Cold Chain Optimization
9. Multi-Staged Deep Learning Approach for Cherry Disease Detection Using DCGAN and YOLOv11 Object Detection
Ankush Vasant Dahat, Mayur Dilip Jakhete, Pallavi H. Dhole,
Renu R. Dandge and Sagar Dhanraj Pande
9.1 Introduction
9.2 Methodology
9.2.1 Dataset Description
9.2.2 Data Preprocessing
9.2.3 You Only Look Once (YOLO –V11)
9.2.4 Power Consumption in Data Centers
9.3 Conclusion
Bibliography
10. Application of Artificial Intelligence and Machine Learning in the Fruit and Vegetable Cold Chain
Bandela Sravanthi, Srinu Banothu and Pooja Srivastav
10.1 Introduction
10.2 Challenges in Cold Chain for Fruits and Vegetables
10.2.1 Harvest and Pre-Cooling
10.2.2 Cold Storage
10.2.2.1 Controlled Atmosphere Storage (CAS)
10.2.2.2 Modified Atmosphere Packaging (MAP)
10.2.2.3 Polymeric Film Applications for MAP of Horticultural Crops
10.2.2.4 Vacuum Packaging
10.2.2.5 Hypobaric/Low-Pressure Storage
10.2.2.6 Ozone Technology
10.2.2.7 Nanotechnology
10.2.3 Transportation
10.2.4 Distribution and Retail
10.2.5 End-User Delivery
10.3 Challenges in Cold Chain Management for Fruits and Vegetables
10.3.1 Economic and Social Challenges
10.3.1.1 Infrastructure Gaps
10.3.1.2 High Operational Costs
10.3.1.3 Lack of Standardization
10.3.1.4 Skill Gaps and Awareness
10.3.1.5 Technological Barriers
10.3.1.6 Regulatory and Policy Limitations
10.3.2 Cold Chain Stages for Fruits and Vegetables
10.3.2.1 The Lack of Real-Time Monitoring
10.3.2.2 Manual Dependency and Human Error
10.3.2.3 Poor Demand Forecasting
10.3.2.4 Inadequate Infrastructure in Rural and Urban Areas
10.3.2.5 Limited Traceability and Visibility Across the Supply Chain
10.4 Role of AI/ML in Cold Chain Operations
10.4.1 Predictive Maintenance and Failure Prevention
10.4.2 Real-Time Monitoring and Environmental Control
10.4.3 Intelligent Transportation and Route Optimization
10.4.4 Automated Sorting and Quality Assessment
10.5 AI Integration with Emerging Technologies
10.5.1 IoT and Edge Computing
10.5.2 Blockchain for Traceability
10.6 Case Studies
10.6.1 Apple Cold Chain in the USA
10.6.2 Mango Export Logistics in India
10.6.3 Tomato Sorting in Processing Units
10.7 Benefits of AI and ML in Cold Chain Management
10.8 Conclusion
Bibliography
11. Federated Learning and Transformer Model-Based Hybrid
AI Model for Predicting Crop Yields
Ankush Vasant Dahat, Pallavi H. Dhole, Rutuja Sagar Deshmukh, Pratik Sheshrao Yawale, Sagar Dhanraj Pande and Usha Desai
11.1 Introduction
11.2 Methodology
11.2.1 Dataset Description
11.2.2 Federated Learning Framework
11.2.3 Contextual Embedding of Text
11.2.4 Improving the Model with a Multi-Objective Genetic Algorithm (MOGA)
11.2.5 Model Interpretability Using Explainable AI (XAI) – Grad-CAM
11.3 Conclusion
References
12. An Intelligent IoT Framework with AI and ML for Predictive Real-Time Monitoring of Vaccine Cold Chains
Ch. Rajendra Prasad, Srinivas Samala and Moola Ramu
12.1 Introduction
12.2 Literature Review
12.2.1 The Cold-Cycle Vulnerability of the Vaccine
12.2.2 The Development of Surveillance Technologies
12.2.3 Large-Scale IoT Architectures
12.2.4 The Interaction of AI and Innovative Technologies
12.3 Intelligent IoT Solution with AI and ML Integration
12.3.1 Sensor/Perception Layer
12.3.2 Gateway Layer
12.3.3 Cloud Layer
12.3.4 Application Layer
12.4 Limitations of Current Studies
12.5 Future Scope
12.6 Conclusion
References
Part IV: Advanced Analytics, Optimization, and Decision Systems
13. Smart Cold Chain Analytics for Fresh Fruits and Vegetables
Exporters in Telangana, India
Kudali Prasanthi, Ramesh Babu Damarla, Veginati Koteswara Rao and Chirra Sricharan
13.1 Introduction
13.2 Current Landscape of Fruits and Vegetables from Telangana
13.2.1 Major Product Exports
13.2.2 Exports, Infrastructure, and Policy Support
13.2.3 Scheme of Cold Chain, Value Addition and Preservation Infrastructure (Telangana)
13.2.4 Export Market and Growth Trend
13.2.5 Gaps and State-Level Data Needs
13.3 Challenges in Traditional Cold Chains
13.3.1 Farm-Level Infrastructure and Aggregation Gaps
13.3.2 Refrigerated Transport Constraints and Logistics Inefficiencies
13.3.3 Weak Real-Time Monitoring and Traceability Practices
13.3.4 Compliance, Skills, and Financing Barriers for Small Actors
13.4 Smart Analytics for Cold Chain Optimization
13.5 Case Studies from Telangana and Comparable Regions
13.5.1 Telangana: Pilot Initiatives in Horticulture Cold Chains
13.5.1.1 Solar-Powered Cold Rooms for Smallholders
13.5.1.2 Pack-House Linked Blockchain Pilots for Traceability
13.5.1.3 Maharashtra: Cold Chain IoT Monitoring for Grapes
13.5.1.4 Gujarat: Pack-House Digitalization through FPOs
13.5.2 Global Comparators with Relevance to Telangana
13.5.2.1 Kenya: IoT-Enabled Cold Hubs for Smallholder Vegetables
13.5.2.2 Netherlands: Digital Twins in Horticulture Logistics
13.5.2.3 China: Blockchain and e-Commerce Integration for Fruits
13.5.3 Key Learnings for Telangana
13.5.3.1 Shared/Collective Cold Chains
13.5.3.2 Blockchain and IoT in Cold Chain Transport
13.5.3.3 Route Optimization Using Artificial Intelligence
13.5.3.4 Digital Systems in Pack-Houses
13.5.3.5 Digital Twins and Simulation Tools
13.6 Sustainable and Energy-Efficient Cold Chains in Telangana
13.6.1 Energy Intensity of Traditional Cold Chains
13.6.1.1 Pre-Rational Costs and Carbon Emission Impacts
13.6.1.2 Sustainable Solutions
13.6.1.3 Optimization Methods
13.6.1.4 Green Cold Chain Technologies
13.6.1.5 Solar-Powered Cold Storage
13.6.1.6 Integration of IoT and AI
13.6.1.7 Blockchain Technology
13.6.1.8 Mathematical Programming Models
13.6.1.9 Eco-Friendly Cold Chain Systems
13.6.1.10 Natural Refrigerants
13.6.1.11 Phase Change Materials (PCMs)
13.6.2 AI-Driven Energy Optimization
13.6.2.1 Load Shifting and Demand Response
13.6.2.2 Predictive Maintenance
13.6.2.3 Hybrid Solar Grid System
13.6.3 Sustainable Transportation Practices
13.6.3.1 Route Optimization
13.6.3.2 Alternative Fuels
13.6.4 Policy and Financing Support for Sustainable Cold Chains
13.6.4.1 Policy Support
13.6.4.2 Financial Assistance
13.6.5 Long-Term Benefits of Sustainable Cold Chains
13.6.5.1 Economic Benefits
13.6.5.2 Environmental Benefits
13.6.5.3 Market Access
13.6 Future Trends and Innovations in Cold Chain Analytics
13.7 Conclusion and Roadmap for the Future of Smart Cold Chains in Telangana
Bibliography
14. A Comprehensive Analysis for Evaluating the Significance of Sustainable Energy in Economic Development
Tripti Sharma, Madhu Shukla, Hetal Jani and Akash Rai
14.1 Introduction
14.2 Related Works
14.3 Methodology
14.3.1 Data Set
14.4 Results and Discussion
14.4.1 Effect in a Direct Manner
14.4.2 Granger Causality Assessment
14.4.3 Effect in an Indirect Manner
14.4.4 Robustness Assessment
14.4.5 Discussion
14.5 Conclusion
References
15. Intelligent Cold Chain Optimization Using AI and Machine Learning
Sandeep Kumar Hegde and Rajalaxmi Hegde
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.4 Experimental Results
15.5 Conclusion
References
16. The Last-Mile Chill: Navigating Cold Chain Delivery
through Smart Systems
Shreya Srivathsa, Prathibha Narayanan and Hadagali Ashoka
16.1 Introduction
16.2 Cold Chain Operational Framework
16.2.1 Storage Infrastructure
16.2.2 Temperature-Controlled Transportation
16.2.3 Regulatory Frameworks
16.3 Last-Mile Structural Framework
16.4 Smart Technological Interventions in Last-Mile Cold Chain
16.5 Case Studies
16.5.1 A Shot in the Dark: Overcoming Last-Mile Cold Chain Hurdles for the COVID-19 Vaccine
16.5.2 A Delicate Catch: Protecting White Shrimp Shelf Life During Home Delivery
16.5.3 Engineering Freshness: A Genetic Algorithm Approach to Cold Chain Distribution
16.6 Discussion
References
Part V: Policy, Governance, and Future Research Directions
17. Sustainable Cold Chain Policy, Governance, and Industry 5.0 Alignment
Pratikshya Bhandari, Bijay Sigdel and Sudarshan Bhandari
Introduction
Literature Review
Pharmaceutical Cold Chain System and Why It Matters in Nepal
Green Chains Sustainability: People, Products, Planet
The Pharmaceutical Cold Chain Governance, Coordination, and Human-Centered Technology
Theoretical Foundation
Methodology
Data Analysis and Findings
Discussion
How Do Policymakers, Healthcare Workers, Technicians, and Logistics Personnel Experience the Alignment of Sustainable Cold-Chain Policy, Governance, and Industry 5.0-Aligned
Technologies in Nepal
How Do these Experiences Shape Their Sense of Responsibility, Confidence, and Ethical Duty in Protecting Temperature-Sensitive Medicines
Conclusion
Limitation
Recommendation
References
18. Analyzing Barriers to Sustainable Temperature-Controlled Logistics in India’s Healthcare Sector
Manya Sinha, Shubham Sachan, Satyam Kumar, Ashutosh Chaurasia, Akhilesh Barve and Chirra Sricharan
18.1 Introduction
18.1.1 Research Questions
18.1.2 Research Objectives
18.2 Literature Review
18.2.1 Literature Gaps
18.3 Identification of Barriers
18.4 Research Methodology
18.4.1 Selection of Expert Panels and Their Profiles
18.4.2 Step 1: Consensus Building Based on Delphi
18.4.3 Stage 2: Using the Saaty Scale for Quantitative Ranking
18.4.4 Stage 3: Fishbone (Ishikawa) Root-Cause Analysis
18.5 Results
18.5.1 Fishbone (Ishikawa) Causal Structure
18.5.2 Barrier Distribution by Sustainability Dimension
18.6 Discussion
18.7 Implications
18.7.1 Theoretical Implications
18.7.2 Practical Implications
18.8 Conclusion
References
19. Cold Chain Analytics for Sustainable Refrigeration
Maddodi B. S., M. Ponni Bala, Usha Desai, Soumic Sarkar and Ravichander Janapati
19.1 Introduction
19.2 Energy Consumption and Emissions in Conventional Cold Chains
19.3 Green Refrigeration and Cold Chain Technologies
19.4 Analytics and IoT-Enabled Cold Chain Monitoring and Optimization
19.5 Emerging Innovations in Cold Chain Logistics and Transportation
19.6 Policy Frameworks, Standards, and Institutional Enablers
for Sustainable Cold Chains
19.6.1 Sustainable Development Goals and Energy Efficiency
19.6.2 Examples of Energy-Efficient Cold Chain Technologies
19.7 Simulation Framework, Modeling Assumptions, and Comparative Results
19.7.1 Simulation Framework Overview
19.7.2 Modeling Assumptions and Evaluation Metrics
19.7.3 Comparative Simulation Results
19.8 Conclusion and Future Research Directions
References
20. Future Research Directions: Digital Twins, Circular Cold Chain Economies, and Climate Resilience
D. Ramesh Babu and K.V. Narasimha Rao
The Strategic Role of Analytics and Digital Twins in Sustainable Cold Chains
Evolution of Cold Chain Analytics
Digital Technologies Enabling Smart Cold Chain Analytics-Manufacturer’s Perspective
Analytics-Driven Sustainability Outcomes in Cold Supply Chains
Resilient and Adaptive Cold Chains through Predictive Analytics
Policy, Regulation, and Standards for Smart Cold Chains
Advancing Circular Cold Chain Economies (CCCE)
Climate Resilience and Adaptation
Future Directions and Recommendations
Strategic Recommendations
References
Index
Back to Top
Preface
Part I: Sustainable Cold Chain Systems: Sustainability, Carbon Footprint, Lifecycle Design
1. Smart Analytics for Cold Chains in Building Sustainable and Resilient Supply Chains
Sajja Suneel, Dankan Gowda V., K.D.V. Prasad, Naveen B. and I. Wayan Budi Sentana
Introduction
Understanding the Cold Chain
The Need for Smart Analytics in Cold Chains
Key Strategies for Implementing Smart Analytics in Cold Chains
Building Sustainable Cold Chains with Smart Analytics
Enhancing Resilience in Cold Chains with Smart Analytics
Case Studies and Success Stories
Challenges and Considerations
The Future of Smart Analytics in Cold Chains
Conclusion
References
2. Reducing Carbon Footprint in Cold Chain Logistics
Tithli Sadhu, Pooja Srivastav and Thokala Saikrishna Reddy
2.1 Introduction
2.2 Carbon Emissions in Agri-Food Cold Chains
2.2.1 Sources of Emissions Across Cold-Chain Stages
2.2.2 Impact of Globalization and Urbanization on Emissions
2.2.3 Role of Energy-Intensive Refrigeration and Transport
2.2.4 Case Examples of Emission Hotspots
2.3 Sustainable Interventions in Cold Chain Logistics
2.3.1 Overview of Sustainability in Cold Chains
2.3.2 Energy-Efficient Refrigeration Technologies
2.3.3 Renewable Energy Integration in Cold Storage and Transport
2.3.4 Smart Packaging Solutions
2.3.5 Internet of Things (IoT) and Real-Time Monitoring
2.3.6 Vehicle Route Optimization through Intelligent Transportation Systems (ITS)
2.4 Policy and Regulatory Frameworks
2.4.1 National and International Climate Goals
2.4.2 Standards and Guidelines for Sustainable Cold Chains
2.4.3 Incentives, Barriers, and Implementation Challenges
2.5 Fuzzy Multi-Criteria Decision-Making (MCDM) Approach
2.5.1 Need for Decision Support Under Uncertainty
2.5.2 Introduction to Fuzzy MCDM Techniques (Fuzzy AHP, Fuzzy TOPSIS)
2.5.3 Development of a Fuzzy MCDM Framework for Cold-Chain
Interventions
2.5.4 Criteria Selection: Environmental, Economic, and Operational
2.5.4.1 Environmental
2.5.4.2 Economic
2.5.4.3 Operational and Quality
2.6 Life Cycle Assessment (LCA) of Cold-Chain Carbon Footprint
2.7 Case Studies and Best Practices
2.7.1 Decarbonization Strategies from Developed and Developing Countries
2.7.2 Lessons from Diverse Agro-Climatic Regions
2.7.3 Sustainable vs. Conventional Cold Chains: What Changes
2.7.4 Putting Decision-Support to Work
2.8 Discussion
2.8.1 Economic–Environmental Trade-Offs
2.8.2 Technological Feasibility and Scalability
2.8.3 Decision-Making Implications for Stakeholders
2.8.4 Challenges and Limitations
2.9 Conclusion
References
3. Green Cold Chain Technology for Fish and Poultry Management: Innovations for Sustainable Food Preservation
Shreya Srivathsa, Prathibha Narayanan and Hadagali Ashoka
Introduction
Conventional Approaches to Cold Chain Management in Fish and Poultry
Emergence of Green Cold Chain Technology
Renewable Energy-Based Cold Storage Systems
Solar-Powered Cold Storage Systems
Vapor Absorption Refrigeration Systems
Eco-Friendly Refrigerants
Digital Intelligence and Real-Time Monitoring in Green Cold Chains
IoT-Enabled Sensor Networks
Blockchain-Enabled Digital Traceability in Green Cold Chains
Case Studies
Low-Emission Transport Refrigeration in Urban Food Cold Chains
Food Loss and Waste in Fish Cold Chains
Discussion
References
Part II: Digital Infrastructure and IoT-enabled Cold Chain Systems: IoT, Blockchain, Edge Computing
4. Intelligent Cold Chain Monitoring with Sensor Networks and Edge Computing
Srinivas Samala, Ch. Rajendra Prasad, Subhashree Mishra and Sudhansu Sekhar Singh
4.1 Introduction
4.1.1 Chapter Contributions
4.1.2 Background
4.1.3 Motivation
4.2 Related Work
4.3 System Architecture and Design
4.3.1 Sensor Network Layer
4.3.2 Edge Computing Layer
4.3.3 Cloud and Backend Layer
4.3.4 Data Flow and Communication
4.4 Methods and Algorithms
4.4.1 Sensor Data Acquisition and Preprocessing
4.4.2 Multi-Sensor Data Fusion
4.4.3 Anomaly Detection and Predictive Analytics
4.4.4 Adaptive Communication and Energy Management
4.4.5 Secure Data Transmission and Integrity
4.5 Implementation and Use Cases
4.5.1 Implementation
4.5.2 Use Cases
4.6 Results and Discussion
4.6.1 Latency of Anomaly Detection
4.6.2 Energy Consumption of Sensor Nodes
4.6.3 Data Transmission Volume
4.6.4 Detection Accuracy
4.6.5 System Uptime and Fault Tolerance
4.6.6 Alert Response Time Under Varying Network Conditions
4.7 Challenges and Future Directions
4.7.1 Challenges
4.7.2 Future Directions
4.8 Conclusion
References
5. Blockchain-Enabled Cold Chain Management Using IoT and Smart Contracts
Usha Desai, T. Sampath Kumar, D. Ramesh Babu, D. Mahesh and Karthik Sripad Rao
5.1 Introduction
5.2 Background
5.3 Literature Review
5.4 Blockchain-Based IoT Architecture for Cold Chain Systems
5.5 Smart Contracts in Cold Chain
5.6 Key Benefits of Blockchain-Based Cold Chain
5.7 Real-World Applications of Blockchain-Enabled Cold Chain Systems
5.7.1 Pharmaceutical Supply Chains
5.7.2 Food and Agriculture Logistics
5.7.3 Blood Banks and Organ Transport
5.7.4 Biotechnology and Laboratory Sample Management
5.7.5 Cold Chain in e-Commerce and Retail
5.7.6 Global Logistics and Cross-Border Shipping
5.7.7 Luxury Goods, Chemicals & High-Value Assets
5.7.8 Sustainability and ESG Reporting
5.8 Implementation Challenges of Blockchain-Enabled System
5.9 Conclusion
References
6. IoT-Enabled Cold Chain Tracking and Monitoring
Srividya P. and Siddharth A.
6.1 Introduction
6.2 IoT-Based Monitoring System
6.3 Cold Chain Logistics
6.4 Cold Chain Logistics Services Processes
6.5 Transformations of IoT in Cold Chain Logistics
6.6 Cold Chain vs. Supply Chain Differences
6.7 IoT-Based Cold Chain Monitoring
6.8 IoT-Based Cold Chain Logistics Integrated with Smart GPS
6.9 Visibility and Communication Challenges across Cold Chain Logistics
6.10 Need for IoT-Based Cold Chain Monitoring and Logistics Solutions
6.11 Real-World Use Cases for IoT-Based Cold Chain Monitoring
6.12 Future Trends and Opportunities
6.13 Enhancing Cost-Efficiency through Emerging Cold Chain
Technologies
6.14 Strengthening Risk Mitigation through Technological Advancements in the Cold Chain
6.15 Conclusion
References
7. IoT-Enabled Fruit and Vegetable Cold Chain Tracking, Monitoring, and Alert System
Ch. Rajendra Prasad and Moola Ramu
7.1 Introduction
7.2 Methodology and Methods
7.2.1 Hardware Components
7.2.2 Software
7.3 Results and Discussion
7.3.1 Experimental Setup
7.3.2 Results and Discussion
7.4 Conclusion
References
8. AquaChain: Blockchain-Enabled Water Management and Theft Detection System
Harsh Vardhan Jampani, Kartik Sripad Rao, Nisha N. D., Gagan S. and Usha Desai
8.1 Introduction
8.2 Literature Review
8.2.1 Materials and Methodology
8.2.1.1 Dataset Used
8.2.1.2 Data Processing Funnel
8.2.1.3 Mathematical Model
8.3 Results and Discussion
8.3.1 System Demonstration and Results
8.3.2 Discussion of Findings
8.3.3 System Performance Metrics
8.3.4 Data Analysis and Visualization
8.4 Conclusion
References
Part III: Artificial Intelligence and Machine Learning for Cold Chain Optimization
9. Multi-Staged Deep Learning Approach for Cherry Disease Detection Using DCGAN and YOLOv11 Object Detection
Ankush Vasant Dahat, Mayur Dilip Jakhete, Pallavi H. Dhole,
Renu R. Dandge and Sagar Dhanraj Pande
9.1 Introduction
9.2 Methodology
9.2.1 Dataset Description
9.2.2 Data Preprocessing
9.2.3 You Only Look Once (YOLO –V11)
9.2.4 Power Consumption in Data Centers
9.3 Conclusion
Bibliography
10. Application of Artificial Intelligence and Machine Learning in the Fruit and Vegetable Cold Chain
Bandela Sravanthi, Srinu Banothu and Pooja Srivastav
10.1 Introduction
10.2 Challenges in Cold Chain for Fruits and Vegetables
10.2.1 Harvest and Pre-Cooling
10.2.2 Cold Storage
10.2.2.1 Controlled Atmosphere Storage (CAS)
10.2.2.2 Modified Atmosphere Packaging (MAP)
10.2.2.3 Polymeric Film Applications for MAP of Horticultural Crops
10.2.2.4 Vacuum Packaging
10.2.2.5 Hypobaric/Low-Pressure Storage
10.2.2.6 Ozone Technology
10.2.2.7 Nanotechnology
10.2.3 Transportation
10.2.4 Distribution and Retail
10.2.5 End-User Delivery
10.3 Challenges in Cold Chain Management for Fruits and Vegetables
10.3.1 Economic and Social Challenges
10.3.1.1 Infrastructure Gaps
10.3.1.2 High Operational Costs
10.3.1.3 Lack of Standardization
10.3.1.4 Skill Gaps and Awareness
10.3.1.5 Technological Barriers
10.3.1.6 Regulatory and Policy Limitations
10.3.2 Cold Chain Stages for Fruits and Vegetables
10.3.2.1 The Lack of Real-Time Monitoring
10.3.2.2 Manual Dependency and Human Error
10.3.2.3 Poor Demand Forecasting
10.3.2.4 Inadequate Infrastructure in Rural and Urban Areas
10.3.2.5 Limited Traceability and Visibility Across the Supply Chain
10.4 Role of AI/ML in Cold Chain Operations
10.4.1 Predictive Maintenance and Failure Prevention
10.4.2 Real-Time Monitoring and Environmental Control
10.4.3 Intelligent Transportation and Route Optimization
10.4.4 Automated Sorting and Quality Assessment
10.5 AI Integration with Emerging Technologies
10.5.1 IoT and Edge Computing
10.5.2 Blockchain for Traceability
10.6 Case Studies
10.6.1 Apple Cold Chain in the USA
10.6.2 Mango Export Logistics in India
10.6.3 Tomato Sorting in Processing Units
10.7 Benefits of AI and ML in Cold Chain Management
10.8 Conclusion
Bibliography
11. Federated Learning and Transformer Model-Based Hybrid
AI Model for Predicting Crop Yields
Ankush Vasant Dahat, Pallavi H. Dhole, Rutuja Sagar Deshmukh, Pratik Sheshrao Yawale, Sagar Dhanraj Pande and Usha Desai
11.1 Introduction
11.2 Methodology
11.2.1 Dataset Description
11.2.2 Federated Learning Framework
11.2.3 Contextual Embedding of Text
11.2.4 Improving the Model with a Multi-Objective Genetic Algorithm (MOGA)
11.2.5 Model Interpretability Using Explainable AI (XAI) – Grad-CAM
11.3 Conclusion
References
12. An Intelligent IoT Framework with AI and ML for Predictive Real-Time Monitoring of Vaccine Cold Chains
Ch. Rajendra Prasad, Srinivas Samala and Moola Ramu
12.1 Introduction
12.2 Literature Review
12.2.1 The Cold-Cycle Vulnerability of the Vaccine
12.2.2 The Development of Surveillance Technologies
12.2.3 Large-Scale IoT Architectures
12.2.4 The Interaction of AI and Innovative Technologies
12.3 Intelligent IoT Solution with AI and ML Integration
12.3.1 Sensor/Perception Layer
12.3.2 Gateway Layer
12.3.3 Cloud Layer
12.3.4 Application Layer
12.4 Limitations of Current Studies
12.5 Future Scope
12.6 Conclusion
References
Part IV: Advanced Analytics, Optimization, and Decision Systems
13. Smart Cold Chain Analytics for Fresh Fruits and Vegetables
Exporters in Telangana, India
Kudali Prasanthi, Ramesh Babu Damarla, Veginati Koteswara Rao and Chirra Sricharan
13.1 Introduction
13.2 Current Landscape of Fruits and Vegetables from Telangana
13.2.1 Major Product Exports
13.2.2 Exports, Infrastructure, and Policy Support
13.2.3 Scheme of Cold Chain, Value Addition and Preservation Infrastructure (Telangana)
13.2.4 Export Market and Growth Trend
13.2.5 Gaps and State-Level Data Needs
13.3 Challenges in Traditional Cold Chains
13.3.1 Farm-Level Infrastructure and Aggregation Gaps
13.3.2 Refrigerated Transport Constraints and Logistics Inefficiencies
13.3.3 Weak Real-Time Monitoring and Traceability Practices
13.3.4 Compliance, Skills, and Financing Barriers for Small Actors
13.4 Smart Analytics for Cold Chain Optimization
13.5 Case Studies from Telangana and Comparable Regions
13.5.1 Telangana: Pilot Initiatives in Horticulture Cold Chains
13.5.1.1 Solar-Powered Cold Rooms for Smallholders
13.5.1.2 Pack-House Linked Blockchain Pilots for Traceability
13.5.1.3 Maharashtra: Cold Chain IoT Monitoring for Grapes
13.5.1.4 Gujarat: Pack-House Digitalization through FPOs
13.5.2 Global Comparators with Relevance to Telangana
13.5.2.1 Kenya: IoT-Enabled Cold Hubs for Smallholder Vegetables
13.5.2.2 Netherlands: Digital Twins in Horticulture Logistics
13.5.2.3 China: Blockchain and e-Commerce Integration for Fruits
13.5.3 Key Learnings for Telangana
13.5.3.1 Shared/Collective Cold Chains
13.5.3.2 Blockchain and IoT in Cold Chain Transport
13.5.3.3 Route Optimization Using Artificial Intelligence
13.5.3.4 Digital Systems in Pack-Houses
13.5.3.5 Digital Twins and Simulation Tools
13.6 Sustainable and Energy-Efficient Cold Chains in Telangana
13.6.1 Energy Intensity of Traditional Cold Chains
13.6.1.1 Pre-Rational Costs and Carbon Emission Impacts
13.6.1.2 Sustainable Solutions
13.6.1.3 Optimization Methods
13.6.1.4 Green Cold Chain Technologies
13.6.1.5 Solar-Powered Cold Storage
13.6.1.6 Integration of IoT and AI
13.6.1.7 Blockchain Technology
13.6.1.8 Mathematical Programming Models
13.6.1.9 Eco-Friendly Cold Chain Systems
13.6.1.10 Natural Refrigerants
13.6.1.11 Phase Change Materials (PCMs)
13.6.2 AI-Driven Energy Optimization
13.6.2.1 Load Shifting and Demand Response
13.6.2.2 Predictive Maintenance
13.6.2.3 Hybrid Solar Grid System
13.6.3 Sustainable Transportation Practices
13.6.3.1 Route Optimization
13.6.3.2 Alternative Fuels
13.6.4 Policy and Financing Support for Sustainable Cold Chains
13.6.4.1 Policy Support
13.6.4.2 Financial Assistance
13.6.5 Long-Term Benefits of Sustainable Cold Chains
13.6.5.1 Economic Benefits
13.6.5.2 Environmental Benefits
13.6.5.3 Market Access
13.6 Future Trends and Innovations in Cold Chain Analytics
13.7 Conclusion and Roadmap for the Future of Smart Cold Chains in Telangana
Bibliography
14. A Comprehensive Analysis for Evaluating the Significance of Sustainable Energy in Economic Development
Tripti Sharma, Madhu Shukla, Hetal Jani and Akash Rai
14.1 Introduction
14.2 Related Works
14.3 Methodology
14.3.1 Data Set
14.4 Results and Discussion
14.4.1 Effect in a Direct Manner
14.4.2 Granger Causality Assessment
14.4.3 Effect in an Indirect Manner
14.4.4 Robustness Assessment
14.4.5 Discussion
14.5 Conclusion
References
15. Intelligent Cold Chain Optimization Using AI and Machine Learning
Sandeep Kumar Hegde and Rajalaxmi Hegde
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.4 Experimental Results
15.5 Conclusion
References
16. The Last-Mile Chill: Navigating Cold Chain Delivery
through Smart Systems
Shreya Srivathsa, Prathibha Narayanan and Hadagali Ashoka
16.1 Introduction
16.2 Cold Chain Operational Framework
16.2.1 Storage Infrastructure
16.2.2 Temperature-Controlled Transportation
16.2.3 Regulatory Frameworks
16.3 Last-Mile Structural Framework
16.4 Smart Technological Interventions in Last-Mile Cold Chain
16.5 Case Studies
16.5.1 A Shot in the Dark: Overcoming Last-Mile Cold Chain Hurdles for the COVID-19 Vaccine
16.5.2 A Delicate Catch: Protecting White Shrimp Shelf Life During Home Delivery
16.5.3 Engineering Freshness: A Genetic Algorithm Approach to Cold Chain Distribution
16.6 Discussion
References
Part V: Policy, Governance, and Future Research Directions
17. Sustainable Cold Chain Policy, Governance, and Industry 5.0 Alignment
Pratikshya Bhandari, Bijay Sigdel and Sudarshan Bhandari
Introduction
Literature Review
Pharmaceutical Cold Chain System and Why It Matters in Nepal
Green Chains Sustainability: People, Products, Planet
The Pharmaceutical Cold Chain Governance, Coordination, and Human-Centered Technology
Theoretical Foundation
Methodology
Data Analysis and Findings
Discussion
How Do Policymakers, Healthcare Workers, Technicians, and Logistics Personnel Experience the Alignment of Sustainable Cold-Chain Policy, Governance, and Industry 5.0-Aligned
Technologies in Nepal
How Do these Experiences Shape Their Sense of Responsibility, Confidence, and Ethical Duty in Protecting Temperature-Sensitive Medicines
Conclusion
Limitation
Recommendation
References
18. Analyzing Barriers to Sustainable Temperature-Controlled Logistics in India’s Healthcare Sector
Manya Sinha, Shubham Sachan, Satyam Kumar, Ashutosh Chaurasia, Akhilesh Barve and Chirra Sricharan
18.1 Introduction
18.1.1 Research Questions
18.1.2 Research Objectives
18.2 Literature Review
18.2.1 Literature Gaps
18.3 Identification of Barriers
18.4 Research Methodology
18.4.1 Selection of Expert Panels and Their Profiles
18.4.2 Step 1: Consensus Building Based on Delphi
18.4.3 Stage 2: Using the Saaty Scale for Quantitative Ranking
18.4.4 Stage 3: Fishbone (Ishikawa) Root-Cause Analysis
18.5 Results
18.5.1 Fishbone (Ishikawa) Causal Structure
18.5.2 Barrier Distribution by Sustainability Dimension
18.6 Discussion
18.7 Implications
18.7.1 Theoretical Implications
18.7.2 Practical Implications
18.8 Conclusion
References
19. Cold Chain Analytics for Sustainable Refrigeration
Maddodi B. S., M. Ponni Bala, Usha Desai, Soumic Sarkar and Ravichander Janapati
19.1 Introduction
19.2 Energy Consumption and Emissions in Conventional Cold Chains
19.3 Green Refrigeration and Cold Chain Technologies
19.4 Analytics and IoT-Enabled Cold Chain Monitoring and Optimization
19.5 Emerging Innovations in Cold Chain Logistics and Transportation
19.6 Policy Frameworks, Standards, and Institutional Enablers
for Sustainable Cold Chains
19.6.1 Sustainable Development Goals and Energy Efficiency
19.6.2 Examples of Energy-Efficient Cold Chain Technologies
19.7 Simulation Framework, Modeling Assumptions, and Comparative Results
19.7.1 Simulation Framework Overview
19.7.2 Modeling Assumptions and Evaluation Metrics
19.7.3 Comparative Simulation Results
19.8 Conclusion and Future Research Directions
References
20. Future Research Directions: Digital Twins, Circular Cold Chain Economies, and Climate Resilience
D. Ramesh Babu and K.V. Narasimha Rao
The Strategic Role of Analytics and Digital Twins in Sustainable Cold Chains
Evolution of Cold Chain Analytics
Digital Technologies Enabling Smart Cold Chain Analytics-Manufacturer’s Perspective
Analytics-Driven Sustainability Outcomes in Cold Supply Chains
Resilient and Adaptive Cold Chains through Predictive Analytics
Policy, Regulation, and Standards for Smart Cold Chains
Advancing Circular Cold Chain Economies (CCCE)
Climate Resilience and Adaptation
Future Directions and Recommendations
Strategic Recommendations
References
Index
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