Kooperativer Bibliotheksverbund

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Format: 1 online resource (211 pages)

ISBN: 9781000326932

Note: Description based on publisher supplied metadata and other sources , 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

Additional Edition: Erscheint auch als Druck-Ausgabe Rahimi, Iman Big Data Analytics in Supply Chain Management Milton : Taylor & Francis Group,c2020 ISBN 9780367407179

Language: English

Subjects: Economics

Keywords: Supply Chain Management ; Big Data ; Datenanalyse ; Industrie 4.0 ; Aufsatzsammlung

UID:

Format: 1 online resource

Edition: First edition.

ISBN: 9780367816384 , 0367816385 , 9781000326932 , 1000326934 , 9781000326918 , 1000326918 , 9781000326925 , 1000326926

Content: "In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Business and Engineering students, scholars, and professionals, this book is a collection of the state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge on emerging supply chain problems"--

Additional Edition: Print version: Big data analytics in supply chain management Boca Raton : CRC Press / Taylor & Francis Group, 2021 ISBN 9780367407179

Language: English

Keywords: Electronic books. ; Electronic books.

URL: https://www.taylorfrancis.com/books/9780367816384