Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains: Volume 1―Theory, Challenges, and Opportunity (Environmental Footprints and Eco-design of Products and Processes)

Foreword
Preface
Acknowledgement
Contents
Digital Supply Chain Paradigm
	1 Introduction
	2 What Supply Chain Management Is All About?
		2.1 Upgradation to Digital Supply Chain Management
		2.2 Seven Steps to a Digital Supply Chain Management
	3 Digital Supply Chain Management (DSCM)—The Trend
	4 Enhancing Digital Supply Chain Through Industry 4.0 (I 4.0)
		4.1 Big Data
		4.2 Data Analysts
		4.3 IoT
		4.4 IIoT
		4.5 Sensors
		4.6 Robotics
		4.7 Blockchain
		4.8 Cloud
		4.9 ERP
		4.10 Predictive Analytics
		4.11 Automated Warehousing
		4.12 Forecasting
		4.13 Automated Product Delivery System
		4.14 AI-Drones and Robots
		4.15 Machine Learning
	5 Industry 4.0: How Digitization Makes the Supply Chain More Efficient and More Effective
	6 Value Drivers for Digital Supply Chain Management (DSCM)
	7 Conclusion and Future Work
	References
Supply Chain Decision-Making Using Artificial Intelligence and Data Analytics
	1 Introduction
	2 Sustainable Manufacturing
	3 Sustainable Supply Chain Management
	4 AI and Data Analytics in Supply Chain Management
		4.1 Potentials of AI and Data Analytics in Supply Chain Management
	5 Conclusion
	References
Using Blockchain for Agro-Food Traceability: A Case Study from Olive Oil Industry
	1 Introduction
	2 Methodology
	3 Results and Discussion
	4 Conclusions
	References
Logistics 4.0 for Sustainable Manufacturing Supply Chain
	1 Introduction
	2 Sustainable Manufacturing Supply Chain
	3 Logistics 4.0
		3.1 Technologies of Logistics 4.0
		3.2 Principles of Logistics 4.0
	4 Logistics 4.0 and Sustainable Manufacturing
		4.1 Logistics 4.0 Human–Machine Interface for Sustainable Supply Chains
	5 Conclusions
	References
Industry 4.0 in Resource Efficient and Cleaner Production: A Case Study from the Food Sector in Jordan
	1 Background
	2 RECP Demonstration in Jordan
	3 Applying Industry 4.0 in RECP
	4 Conclusions
	References
Applications of Machine Learning in Supply Chain Management—A Review
	1 Introduction
		1.1 Machine Learning (ML) Techniques
		1.2 Supply Chain Management (SCM)
	2 Challenges in SCM
	3 Machine Leaning and Supply Chain Management
		3.1 Need of ML in SCM
		3.2 Various ML Techniques Available for SCM
	4 Recent Applications of ML in SCM
	5 Summary
	References
The Benefits of Using Industry 4.0 in the Manufacturing Sector
	1 Introduction
	2 Aim, Objectives, and Research Questions
	3 Rationale
	4 Significance of the Research
	5 Literature Review
		5.1 Industry 4.0 in Manufacturing
		5.2 Factors Behind the Growing Importance of Using Industry 4.0
		5.3 Benefits of Adopting Industry 4.0
	6 Research Methodology
		6.1 Data Collection Methods
	7 Data Analysis and Discussion
		7.1 Profile of the Respondents
		7.2 Findings
	8 Conclusion
	9 Recommendations
	10 Future Research Direction
	Annexure
	References
Application of Digital Twin for Efficient Supply Chain: Analysis of Opportunities and Challenges
	1 Introduction
	2 Definitions of Digital Twin
	3 Applications of a Digital Twin for an Efficient Supply Chain Across Different Industries
		3.1 Agri-Food
		3.2 Aerospace and Aeronautical
		3.3 Automobile
		3.4 Health Care
		3.5 Construction
		3.6 Oil and Gas
	4 Benefits of Digital Twin Applications for an Efficient Supply Chain
		4.1 Supply Chain Planning
		4.2 Managing Risks and Building Supply Chain Resilient
		4.3 Mapping the Supply Chain Network and Ensuring Visibility
		4.4 Analyzing Demand and Supply Signals
		4.5 Testing the Contingency Plans
		4.6 Accurate Forecasting and Testing Operations
		4.7 Predictive Maintenance
		4.8 Boosting Innovations
		4.9 Cost Savings
		4.10 Contributes to Sustainability Assessments
		4.11 Optimizes and Supports Strategic Decisions
		4.12 Accelerating Product and Process Developments
		4.13 Inventory Management
		4.14 Enhancing the Product and Process Quality
		4.15 Improving Supply Chain Performances
	5 Challenges of Adopting a Digital Twin
	6 Conclusion
	References
Industry 4.0 Technologies: Opportunities in the Sustainable Supply Chain Management
	1 Introduction
	2 Industry 4.0
	3 Sustainable Supply Chain Management
	4 Industry 4.0 and Sustainable Supply Chain Management
		4.1 Opportunities and Applications of Prominent Industry 4.0 Technologies in SSC Management
		4.2 SWOT Analysis
	5 Summary
	References
A Bibliometric Analysis of Smart Manufacturing and Way Forward
	1 Introduction
	2 Literature Review
	3 Methods
		3.1 Search String
		3.2 Database Selection and Collection of Data
	4 Analysis
		4.1 Sources
		4.2 Highly Prominent Authors and Keywords
	5 Findings
	6 Contributions and Implications
	7 Future Research
	8 Limitations
	References
Performance Metrics in Digital Supply Chain Paradigm
	1 Introduction
	2 Performance Metrics
		2.1 Internal Supply Chain Performance Measurement
		2.2 External Supply Chain Performance Metrics
		2.3 Requirements for Performance Measurement Metrices
	3 Classification of Performance Metrics
		3.1 Delivery Time
		3.2 Time Cycle in Cash-to-Cash
		3.3 Inventory Movement
		3.4 Gross Margin Return on Investment (GMROI)
		3.5 Absolute Order Rate
		3.6 Day’s Sales Outstanding (DSO)
		3.7 Cycle Time for the Supply Chain
		3.8 Customer Order Cycle Time
		3.9 Fill Rate
		3.10 Warehousing Costs
		3.11 Cost of Shipping per Unit
		3.12 Average Delivery Time
		3.13 Cycle Time for Pick and Pack
		3.14 Ratio of Inventory to Sales
		3.15 Inventory Velocity
		3.16 Return Ground
		3.17 Costs in a Supply Chain
		3.18 Sales Versus Costs in Supply Chain
		3.19 On-Time Shipping
		3.20 Use of Packing Material
	4 Conclusion and Scope for Future Work
	References
Challenges for the Adoption of Industry 4.0 in the Sustainable Manufacturing Supply Chain
	1 Industry 4.0 and Sustainable Manufacturing Supply Chains
	2 Emerging Technologies Powering the Industry 4.0 Revolution in Manufacturing
	3 Challenges of Industry 4.0 Technologies in Sustainable Manufacturing Supply Chains
	4 Technology-Level Challenges of Industry 4.0 Adoption for Sustainable Manufacturing Supply Chain Management
	5 Conclusion and Implication
	References