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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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Big Data Analytics in Supply Chain Management: A Scientometric Analysis
Iman Rahimi Universiti Putra Malaysia Amir H. Gandomi University of Technology Sydney M. Ali Ulku Dalhousie University Simon James Fong University of Macau The study of big data is constantly expanding, and the main characteristics of big data are now subdivided into the “5V” concept, consisting...
(Big Data Analytics in Supply Chain Management: Theory and Applications)
Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method
Mehdi Keshavarz-Ghorabaee Conbad Kavous University Maghsoud Amiri Allameh Tabataba'i University Mohammad Hashemi-Tabatabaei Allameh Tabataba'i University Mohammad Ghahremanloo Shahrood University of Technology Big Data Analytics (BDA) is a term that refers to the processes of investigating large...
(Big Data Analytics in Supply Chain Management: Theory and Applications)
Supply Chain Challenges in Adopting Big Data Analytics
Analytics techniques offer pronounced advantages in the transformation of the economy. But it also raises several challenges with capturing the data, storage of that vast information, and analysis and visualisation of the data. It also incorporates issues with data inconsistency and information incompleteness,...
(Big Data Analytics in Supply Chain Management: Theory and Applications)
Marketing Information System as a Marketing Crisis Management Mechanism Through Big Data Analytics: A Case Study of Algeria Telecom in Bouira
RABAH GHAZI and FATIMA ZOHRA SOUKEUR Laboratory of Globalization, Politics, and Economics, University of Algiers 3, Defy Brahim, Algeria, E-mails: ghazi.rabah@univ-alger3.dz (R. Ghazi), zola_marketing@yahoo.fr (F. Z. Soukeur) ABSTRACT This chapter aims to address the marketing information system...
(Big Data Analytics: Harnessing Data for New Business Models)
Perspectives for Applying Machine Learning and Big Data Analytics to OSH Management Processes
As mentioned in section 6.2.2, the progress observed recently in the field of ICT, and in particular the development of applications based on cloud and edge computing, makes it possible to collect and process more and more data, including that collected through smart PPE and workplace wearables, which...
(New Opportunities and Challenges in Occupational Safety and Health Management)
Examples of Machine Learning and Big Data Analytics Applications to Support OSH Management Processes in Real or Near-Real Time
A review of scientific literature and websites shows that the solutions for OSH process monitoring and management in real or near-real time based on advanced machine learning and big data analytics methods are still often at the stage of conceptual development or pilot testing in laboratory or industrial...
(New Opportunities and Challenges in Occupational Safety and Health Management)
The Concept of OSH Management Based on Smart PPE and Wearable Technologies Combined with the Use of IoT and Big Data Analytics
As demonstrated in previous sections, recent developments in smart PPE systems, workplace wearables, IoT industrial networks and industrial applications of machine learning and big data analytics, as well as the growing number of other smart machines and devices being introduced into digitally transformed...
(New Opportunities and Challenges in Occupational Safety and Health Management)
Big Data Analytics: Innovation Management and Value Creation
Anna Jasiulewicz, Piotr Pietrzak, and Barbara Wyrzykowska Warsaw University of Life Sciences—SCCW Innovations are one of the key areas of enterprise activity (Kotler & Kotler, 2013). Many authors (including Davenport et al., 2012; Chen et al., 2012; McAfee & Brynjolfsson, 2012) believe...
(Management in the Era of Big Data: Issues and Challenges)
The Role of Supply Chain Management in Corporate Strategy
James S. Keebler WHAT IS SUPPLY CHAIN MANAGEMENT? In a Harvard Business Review article published in 1958, Jay Forrester introduced a theory of management that recognized the integrated, interdependent nature of organizational relationships in distribution channels.1 He pointed out that...
(Strategic Management in the 21st Century. The Operational Environment)
WHAT IS SUPPLY CHAIN MANAGEMENT?
In a Harvard Business Review article published in 1958, Jay Forrester introduced a theory of management that recognized the integrated, interdependent nature of organizational relationships in distribution channels.1 He pointed out that system dynamics can influence the activities of various business...
(Strategic Management in the 21st Century. The Operational Environment)