Big Data Analytics in Supply Chain Management: Theory and Applications - 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