Data-Driven Technologies and Artificial Intelligence in Supply Chain: Tools and Techniques

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1: A human-centered approach to artificial intelligence in the supply chain
	1.1 Introduction to human-centered AI
		1.1.1 Evolution of human-machine relationship
		1.1.2 HCAI framework
		1.1.3 Human-centered design approach towards human-centered AI
		1.1.4 Modern supply chain management is driven by artificial intelligence and analytics
	1.2 Human-in-the- l oop m achine l earning, r easoning and p lanning
		1.2.1 Reasoning and planning
			1.2.1.1 Human Collaborators
			1.2.1.2 Crowdsourced planning
		1.2.2 Data analysis
		1.2.3 Designing and prototyping
	1.3 Use cases for AI and analytics to reduce supply chain interruption
		1.3.1 Warehouse supply and demand management with demand forecasting
			1.3.1.1 Need for demand forecasting
			1.3.1.2 Working
			1.3.1.3 Techniques
			1.3.1.4 Demand forecasting
		1.3.2 AI and machine learning for the sustainability of logistics and transportation
		1.3.3 AI improves supply chain loading process portability
		1.3.4 AI for supply chain cost reduction and revenue increase
		1.3.5 Supply chain strategic sourcing based on data analytics
	1.4 Summary and conclusion
	References
Chapter 2: A proposed artificial intelligence and blockchain technique for solving health insurance challenges
	2.1 Introduction
	2.2 Artificial intelligence
	2.3 Blockchain technology
	2.4 The present scenario
	2.5 Challenges with the existing system
	2.6 The solution: artificial intelligence and blockchain technology
	2.7 Proposed idea
	2.8 Proposed idea phases and performance analysis
		2.8.1 Phase I: shop and estimate
		2.8.2 Phase II: apply and enroll
		2.8.3 Phase III: make your first payment
		2.8.4 Phase IV: cards and coverage
		2.8.5 Phase V: claim process
	2.9 Flow diagram of proposed idea
	2.10 Result analysis
	2.11 Flow diagram of output
	2.12 Comparison table
	2.13 Conclusion
	References
Chapter 3: Logistics performance measurement: A data envelopment analysis using the logistics performance index 2018 data
	3.1 Introduction
	3.2 Method
	3.3 Data
	3.4 Results
	3.5 Concluding remarks and future research directions
	Note
	References
Chapter 4: Artificial intelligence to complement Lean approach in the healthcare industry
	4.1 Introduction
	4.2 The Lean concept in healthcare
		4.2.1 Wastes in healthcare
		4.2.2 Variables of Lean implementation in healthcare
		4.2.3 Factors affecting successful Lean implementation
		4.2.4 Benefits of Lean in healthcare
	4.3 Artificial intelligence and Lean integration in healthcare
		4.3.1 Diagnosis and treatment applications
		4.3.2 Applications for patient involvement and adherence
		4.3.3 Administrative applications
		4.3.4 Implications for the healthcare workforce
		4.3.5 Ethical implications
		4.3.6 AI in Healthcare
		4.3.7 The future of AI in healthcare
	4.4 Opportunities and challenges of artificial intelligence and Lean integration in healthcare
	4.5 Conclusion
	4.6 Future scope
	References
Chapter 5: Artificial intelligence as a rescuer of vaccine’s cold chain
	5.1 Introduction
	5.2 The vaccine cold chain and its challenges
		5.2.1 Components of the vaccine cold chain
		5.2.2 Challenges to vaccine cold chain
	5.3 Artificial intelligence in the immunization supply chain
		5.3.1 Applications of AI in SCM and logistics
		5.3.2 Opportunities to AI
		5.3.3 Advantages of AI
		5.3.4 Disadvantages of AI
		5.3.5 Challenges to AI
	5.4 Different technologies in the cold chain
		5.4.1 Internet of Things (IoT)
			5.4.1.1 Benefits of implementing IoT in the cold chain
			5.4.1.2 IoT-based conceptual model
		5.4.2 Big data
			5.4.2.1 Encourage real-time dynamic product information feedback
			5.4.2.2 Encourage the improvement and modification of the application approach
		5.4.3 Blockchain
		5.4.4 Cloud computing
		5.4.5 Machine learning
		5.4.6 Artificial neural network (ANN)
	5.5 Conclusion
	5.6 Future scope
	References
Chapter 6: Leveraging the potential of artificial intelligence in healthcare supply chain management
	6.1 Introduction
	6.2 The need and role of artificial intelligence (AI) in the healthcare supply chain (HSC)
	6.3 Common AI techniques in the healthcare supply chain
		6.3.1 Medical supply spend analysis
		6.3.2 Automated inventory management
		6.3.3 Preference card standardization
		6.3.4 Three-way matching
		6.3.5 Prescriptive analytics
		6.3.6 Machine learning models used in vaccine supply
	6.4 Usage of AI in subfields of the healthcare supply chain
		6.4.1 Task automation
		6.4.2 Aids in cost-cutting
		6.4.3 Matching patient care demands
		6.4.4 Substitutes during labor shortage
		6.4.5 Supplier management data
		6.4.6 Optimizing logistics support
		6.4.7 Improved data governance
	6.5 Barriers to the adoption of AI in the healthcare supply chain
	6.6 The impact of COVID-19 on public healthcare supply networks
	6.7 Conclusion
	6.8 Future scope
	References
Chapter 7: Artificial intelligence-assisted telemedicine: A boon in healthcare
	7.1 Introduction
	7.2 Evolution and functioning of telemedicine services
	7.3 The role of AI and DDT in healthcare
	7.4 Challenges to the implementation of AI in the Indian healthcare system
	7.5 Future drivers and prospects of telemedicine in India
	7.6 Development of a strategic HAT model to illustrate successful telemedicine implementation in India
		7.6.1 Strategic-level management
		7.6.2 Organizational-level management
		7.6.3 Policy-level management
		7.6.4 Development of telemedicine service
		7.6.5 Evaluation and optimization
	7.7 Conclusion
	References
Chapter 8: The landscape of Big Data and supply chain management
	8.1 Introduction
	8.2 Classification of big data analytics
		8.2.1 Descriptive analytics
		8.2.2 Diagnostic analytics
		8.2.3 Predictive analytics
		8.2.4 Prescriptive analytics
	8.3 Advantages of big data analytics
	8.4 Big data challenges
	8.5 Benefits of using big data in supply chain management
	8.6 Applications of big data in supply chain management
	8.7 Conclusion
	Suggested readings
Chapter 9: The emerging roles of artificial intelligence in chemistry and drug design
	9.1 Introduction
	9.2 Drug development cycle
	9.3 AI in drug discovery
	9.4 Physiochemical properties prediction
	9.5 Prediction of bioactivity
	9.6 Prediction of toxicity
	9.7 Resources used for developing deep learning application
	9.8 Drug discovery tools
	9.9 DTI-CNN
	9.10 AlphaFold
	9.11 Critical assessment of protein structure prediction (CASP)
	9.12 Conclusion
	References
Chapter 10: The Impact of the data-driven supply chain on quality: Evidence from the medical device manufacturing industry
	10.1 Introduction
	10.2 The medical device industry in developed and developing countries
	10.3 The healthcare supply chain
	10.4 Big data
	10.5 Data-driven supply chain
	10.6 Big data in the medical device manufacturing industry
	10.7 Impact of the data-driven supply chain on quality management in the medical device industry
		10.7.1 Industry 4.0
		10.7.2 Quality 4.0
	10.8 Conclusion
	Suggested readings
Chapter 11: Digital supply chain: Potentials, capabilities and risk management through artificial intelligence
	11.1 Introduction
		11.1.1 Supply chain models
	11.2 Digital supply chain
	11.3 Most advantageous SCM domains for AI application
		11.3.1 Innovations in product development
	11.4 AI-assisted intelligent procurement
		11.4.1 Manufacturing
	11.5 Advantages of an AI-empowered digital supply chain
		11.5.1 Accurate inventory management
		11.5.2 Warehouse efficiency
		11.5.3 Improved safety
		11.5.4 Less expensive operations
		11.5.5 Prompt delivery
		11.5.6 Strengthening of planning and scheduling activities
		11.5.7 Intelligent decision-making
		11.5.8 End-to-end supply chain visibility in AI
		11.5.9 Realistic analytical insights
		11.5.10 Management of inventory and demand
		11.5.11 Improve operating efficiencies
		11.5.12 Enterprise Resource Planning (ERP) streamlining
	11.6 Risks and issues in the digital supply chain
		11.6.1 Security issues
		11.6.2 System complexities
		11.6.3 The scalability aspect
		11.6.4 Training costs
		11.6.5 The associated operational expenses
	11.7 Digital supply chain risk management
		11.7.1 Establish realistic expectations
		11.7.2 Know the technology utilized by the organization
		11.7.3 Thoroughly consider your data
		11.7.4 AI integration implementation
	11.8 Future of digital supply chains
	11.9 Conclusion
	Suggested readings
Chapter 12: Intelligent location specific crop recommendation system using big data analytics framework
	12.1 Introduction
		12.1.1 Potential of big data analytics
		12.1.2 Big data analytics framework
		12.1.3 Data collection
		12.1.4 Information extraction
		12.1.5 Data analysis
	12.2 An effective big data-based crop recommendation system
		12.2.1 Background work
			12.2.1.1 Agricultural recommendation system
		12.2.2 Big data platforms used
			12.2.2.1 Apache Kafka
			12.2.2.2 Apache Spark
			12.2.2.3 Apache HBase
			12.2.2.4 Elasticsearch
		12.2.3 Proposed framework
			12.2.3.1 Data collection
			12.2.3.2 Data ingestion
			12.2.3.3 Machine learning model
			12.2.3.4 Adam optimizer
			12.2.3.5 Performance metrics
			12.2.3.6 Data storage and indexing
	12.3 Experimental results
	12.4 Discussion
	12.5 Conclusion
	References
Chapter 13: Application of artificial intelligence in small and medium enterprises: An overview
	13.1 Introduction
	13.2 Research methodology
	13.3 Research findings
		13.3.1 Descriptive analysis
		13.3.2 Applications of AI technology in SMEs
			13.3.2.1 Finance
				13.3.2.1.1 Credit evaluations
				13.3.2.1.2 Risk assessments
				13.3.2.1.3 Bookkeeping
			13.3.2.2 Marketing
				13.3.2.2.1 Sales and advertising
				13.3.2.2.2 Customer services
			13.3.2.3 Supply chain management
				13.3.2.3.1 Inventory management
				13.3.2.3.2 Logistics
				13.3.2.3.3 Manufacturing
			13.3.2.4 Human resources
				13.3.2.4.1 Recruitment
				13.3.2.4.2 Training and development
				13.3.2.4.3 Performance management
			13.3.2.5 Business performances
				13.3.2.5.1 Decision-making
				13.3.2.5.2 Business developments
				13.3.2.5.3 Competitive advantage
	13.4 Conclusion, limitations, and future scope
	References
Chapter 14: Industry 4.0 and sustainable supply chain: A review and future research agenda
	14.1 Introduction
		14.1.1 Industry 4.0 and the sustainable supply chain
		14.1.2 Challenges to Industry 4.0 technology in sustainable supply chains
	14.2 Methodology
	14.3 Results
		14.3.1 Basic information about data
		14.3.2 Year-wise publication
		14.3.3 Mean citation structure per year
		14.3.4 Top 10 authors impact
		14.3.5 Top 10 affiliations with the publication
		14.3.6 Top 10 most frequent word
		14.3.7 Most-cited countries
		14.3.8 Word cloud
		14.3.9 Country’s production over time
		14.3.10 Top 10 most relevant journals
	14.4 Conclusion and future implications
	References
Chapter 15: A comprehensive study of artificial intelligence in supply chains
	15.1 Introduction
	15.2 Methodology
		15.2.1 Pilot research
	15.3 Analysis and synthesis
		15.3.1 Statistics and distribution
		15.3.2 Categorical description of SLR
		15.3.3 AI approaches
	15.4 Conclusion
	15.5 Limitations and implications
	15.6 Future scope
	References
Index