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Big Data Analytics in Supply Chain Management: Theory and Applications
Big Data Analytics in Supply Chain Management: A Scientometric Analysis
Introduction
Analysis
Data Collection
Scientometric Analysis
An Analysis on Keywords
A Short Analysis on Countries and Affiliations
Co-author Analysis
An Analysis on Sources
Co-citation Analysis
Discussion and Conclusion
References
Supply Chain Analytics Technology for Big Data
Introduction
Introduction to Supply Chain Analytics Technology
Necessity for Supply Chain Analytics for Big Data
Features of Supply Chain Analytics
Opportunities and Applications for Supply Chain Analytics
Opportunities for Supply Chain Analytics
Process Specific applications of Big Data Analytics
Tools for Supply Chain Analytics
Supply Chain Analytics Methods
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Challenges in Adopting Big Data Analytics
Future of Supply Chain Analytics
Conclusion
References
Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method
Introduction to Big Data Analytics
Barriers to BDA: Background
Methodology
The Steps of HBWM
Determining the Consistency Rate
Results and Discussion
Conclusion
References
Big Data in Procurement 4.0: Critical Success Factors and Solutions
Introduction
Macroenvironment
Literature Review
Methodology
Critical Success Factors for Procurement 4.0
Cybernetics
Communication
CONTROLLERSHIP
Collaboration
Connection
Cognition
Coordination
Confidence
Critical Success Factors and Procurement Cycle
Supporting Solutions
Application of the Model
Conclusions, Practical Implications, and Future Research
Abbreviations
References
Recommendation Model Based on Expiry Date of Product Using Big Data Analytics
Introduction
Statement and Objective
Literature Survey
Product Recommendation System
User’s Preferences/ Choices
Keyword Classification
Implementation of Statistical Analysis for Products
One-Sided and Two-Sided T-Test of Data Sets
Linear Regression Model
Experimental Assessment
Effects of Recommendation System
Recommendation for Ratings and Reviews of the Customer of Products
Advantages of the Recommendation System
Conclusion
References
Comparing Company’s Performance to Its Peers: A Data Envelopment Approach
Introduction
Previous Related Research
Methodology Description
Slacks-Based Measure of Efficiency
Multiple Criteria Decision-Making
Empirical Results
Data Description and Preprocessing
Main DEA Results
Discussion on the Best and Worst Ranked Companies
Robustness Checking – MCDM
Further Possible Integrations of DEA and MCDM
Conclusion
Appendix
References
Sustainability, Big Data, and Consumer Behavior: A Supply Chain Framework
Background
Attributes Impacting Consumer’s Purchasing Behavior
Purchase Price
Derived Utility
Product Quality
Product Support Services
Return Policy
Summary
A Bidirectional Supply Chain Framework
Concluding Remarks
References
A Soft Computing Techniques Application of an Inventory Model in Solving Two-Warehouses Using Cuckoo Search Algorithm
Introduction
Inventory Models with Two Warehouses
Cuckoo Behavior and Lévy Flights
Related Works
Assumption and Notations
Mathematical Formulation of Model and Analysis
Cuckoo Search Algorithm
Numerical Analysis
Sensitivity Analysis
Conclusions
References
An Overview of the Internet of Things Technologies Focusing on Disaster Response
Introduction
Artificial Intelligence
Internet of Things
The Use of IoT and AI for Risk and Disaster Management
The IoT Relationship in the Supply Chain During Disaster
Discussion
Future Trends
Conclusions
References
Closing the Big Data Talent Gap
Research Benefits | What’s in It for Me?
The State of Big Data Education
Data Scientist vs Data Analyst
A Qualitative Approach
Dependability and Trustworthiness
Data Analysis
Big Data Initiatives
Years of Big Data Initiatives
Size of Big Data Teams
Big Data Resources Needed
Where Are Organizations Finding Big Data Resources?
Challenges Finding Big Data Resources
Qualities Most Difficult to Find in Candidates
The Ideal Big Data Specialist Candidate
Number of Candidates Interviewed
Easing the Big Data Hiring Process
IT Manager Interviews
Specialist Interviews
Key Analysis & Findings
Theme 1: “ Lacking”
Theme 2: “ Passion”
Theme 3: Soft Skills
Theme 4: Technical Skills
Conclusion
Discussion
References
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