Big Data Analytics in Supply Chain Management: Theory and Applications

✍ Scribed by Amir H. Gandomi, Iman Rahimi, S. Fong, M. Ali Ülkü

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 211
• Category: Library

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Editors
Contributors
Chapter 1 Big Data Analytics in Supply Chain Management: A Scientometric Analysis
1.1 Introduction
1.2 Analysis
1.2.1 Data Collection
1.3 Scientometric Analysis
1.3.1 An Analysis on Keywords
1.3.2 A Short Analysis on Countries and Affiliations
1.3.3 Co-author Analysis
1.3.4 An Analysis on Sources
1.3.5 Co-citation Analysis
1.3 Discussion and Conclusion
References
Chapter 2 Supply Chain Analytics Technology for Big Data
2.1 Introduction
2.1.1 Introduction to Supply Chain Analytics Technology
2.1.2 Necessity for Supply Chain Analytics for Big Data
2.2 Features of Supply Chain Analytics
2.3 Opportunities and Applications for Supply Chain Analytics
2.3.1 Opportunities for Supply Chain Analytics
2.3.2 Process Specific applications of Big Data Analytics
2.4 Tools for Supply Chain Analytics
2.5 Supply Chain Analytics Methods
2.5.1 Descriptive Analytics
2.5.2 Predictive Analytics
2.5.3 Prescriptive Analytics
2.6 Supply Chain Challenges in Adopting Big Data Analytics
2.7 Future of Supply Chain Analytics
2.8 Conclusion
References
Chapter 3 Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method
3.1 Introduction to Big Data Analytics
3.2 Barriers to BDA: Background
3.3 Methodology
3.3.1 The Steps of HBWM
3.3.2 Determining the Consistency Rate
3.4 Results and Discussion
3.5 Conclusion
References
Chapter 4 Big Data in Procurement 4.0: Critical Success Factors and Solutions
4.1 Introduction
4.2 Macroenvironment
4.3 Literature Review
4.4 Methodology
4.5 Critical Success Factors for Procurement 4.0
4.5.1 Cybernetics
4.5.2 Communication
4.5.3 Controllership
4.5.4 Collaboration
4.5.5 Connection
4.5.6 Cognition
4.5.7 Coordination
4.5.8 Confidence
4.6 Critical Success Factors and Procurement Cycle
4.7 Supporting Solutions
4.8 Application of the Model
4.9 Conclusions, Practical Implications, and Future Research
Abbreviations
References
Chapter 5 Recommendation Model Based on Expiry Date of Product Using Big Data Analytics
5.1 Introduction
5.1.1 Statement and Objective
5.1.2 Literature Survey
5.2 Product Recommendation System
5.2.1 User’s Preferences/ Choices
5.2.2 Keyword Classification
5.3 Implementation of Statistical Analysis for Products
5.3.1 One-Sided and Two-Sided T-Test of Data Sets
5.3.2 Linear Regression Model
5.3.3 Experimental Assessment
5.4 Effects of Recommendation System
5.4.1 Recommendation for Ratings and Reviews of the Customer of Products
5.4.2 Advantages of the Recommendation System
5.5 Conclusion
References
Chapter 6 Comparing Company’s Performance to Its Peers: A Data Envelopment Approach
6.1 Introduction
6.2 Previous Related Research
6.3 Methodology Description
6.3.1 Slacks-Based Measure of Efficiency
6.3.2 Multiple Criteria Decision-Making
6.4 Empirical Results
6.4.1 Data Description and Preprocessing
6.4.2 Main DEA Results
6.4.3 Discussion on the Best and Worst Ranked Companies
6.4.4 Robustness Checking – MCDM
6.4.5 Further Possible Integrations of DEA and MCDM
6.5 Conclusion
Appendix
References
Chapter 7 Sustainability, Big Data, and Consumer Behavior: A Supply Chain Framework
7.1 Background
7.2 Attributes Impacting Consumer’s Purchasing Behavior
Purchase Price
Derived Utility
Product Quality
Product Support Services
Return Policy
Summary
7.3 A Bidirectional Supply Chain Framework
7.4 Concluding Remarks
References
Chapter 8 A Soft Computing Techniques Application of an Inventory Model in Solving Two-Warehouses Using Cuckoo Search Algorithm
8.1 Introduction
8.1.1 Inventory Models with Two Warehouses
8.1.2 Cuckoo Behavior and Lévy Flights
8.2 Related Works
8.3 Assumption and Notations
8.4 Mathematical Formulation of Model and Analysis
8.5 Cuckoo Search Algorithm
8.6 Numerical Analysis
8.7 Sensitivity Analysis
8.8 Conclusions
References
Chapter 9 An Overview of the Internet of Things Technologies Focusing on Disaster Response
9.1 Introduction
9.2 Artificial Intelligence
9.3 Internet of Things
9.4 The Use of IoT and AI for Risk and Disaster Management
9.5 The IoT Relationship in the Supply Chain During Disaster
9.6 Discussion
9.7 Future Trends
9.8 Conclusions
References
Chapter 10 Closing the Big Data Talent Gap
10.1 Research Benefits | What’s in It for Me?
10.2 The State of Big Data Education
10.3 Data Scientist vs Data Analyst
10.4 A Qualitative Approach
10.5 Dependability and Trustworthiness
10.6 Data Analysis
10.7 Big Data Initiatives
10.8 Years of Big Data Initiatives
10.9 Size of Big Data Teams
10.10 Big Data Resources Needed
10.11 Where Are Organizations Finding Big Data Resources?
10.12 Challenges Finding Big Data Resources
10.13 Qualities Most Difficult to Find in Candidates
10.14 The Ideal Big Data Specialist Candidate
10.15 Number of Candidates Interviewed
10.16 Easing the Big Data Hiring Process
10.17 IT Manager Interviews
10.18 Specialist Interviews
10.19 Key Analysis & Findings
10.19.1 Theme 1: “ Lacking”
10.19.2 Theme 2: “ Passion”
10.19.3 Theme 3: Soft Skills
10.19.4 Theme 4: Technical Skills
10.20 Conclusion
10.21 Discussion
References
Index