Big Data Analytics in Supply Chain Management: Theory and Applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
Taylor & Francis Group
2020
|
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (211 pages) |
| ISBN: | 9781000326932 |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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Datensatz im Suchindex
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| author | Rahimi, Iman |
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| 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 |
| ctrlnum | (ZDB-30-PQE)EBC6401873 (ZDB-30-PAD)EBC6401873 (ZDB-89-EBL)EBL6401873 (OCoLC)1223095662 (DE-599)BVBBV047694018 |
| dewey-full | 658.7 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.7 |
| dewey-search | 658.7 |
| dewey-sort | 3658.7 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV047694018 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:57:27Z |
| indexdate | 2024-07-10T09:19:21Z |
| institution | BVB |
| isbn | 9781000326932 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033078012 |
| oclc_num | 1223095662 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 online resource (211 pages) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Taylor & Francis Group |
| record_format | marc |
| spelling | Rahimi, Iman Verfasser aut Big Data Analytics in Supply Chain Management Theory and Applications Milton Taylor & Francis Group 2020 ©2021 1 online resource (211 pages) txt rdacontent c rdamedia cr rdacarrier 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 Business logistics Datenanalyse (DE-588)4123037-1 gnd rswk-swf Supply Chain Management (DE-588)4684051-5 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Industrie 4.0 (DE-588)1072179776 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Supply Chain Management (DE-588)4684051-5 s Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s Industrie 4.0 (DE-588)1072179776 s DE-604 Gandomi, Amir H. Sonstige oth Fong, Simon James Sonstige oth Ülkü, M. Ali Sonstige oth Erscheint auch als Druck-Ausgabe Rahimi, Iman Big Data Analytics in Supply Chain Management Milton : Taylor & Francis Group,c2020 9780367407179 |
| spellingShingle | Rahimi, Iman Big Data Analytics in Supply Chain Management Theory and Applications 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 Business logistics Datenanalyse (DE-588)4123037-1 gnd Supply Chain Management (DE-588)4684051-5 gnd Big Data (DE-588)4802620-7 gnd Industrie 4.0 (DE-588)1072179776 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4684051-5 (DE-588)4802620-7 (DE-588)1072179776 (DE-588)4143413-4 |
| title | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_auth | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_exact_search | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_exact_search_txtP | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_full | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_fullStr | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_full_unstemmed | Big Data Analytics in Supply Chain Management Theory and Applications |
| title_short | Big Data Analytics in Supply Chain Management |
| title_sort | big data analytics in supply chain management theory and applications |
| title_sub | Theory and Applications |
| topic | Business logistics Datenanalyse (DE-588)4123037-1 gnd Supply Chain Management (DE-588)4684051-5 gnd Big Data (DE-588)4802620-7 gnd Industrie 4.0 (DE-588)1072179776 gnd |
| topic_facet | Business logistics Datenanalyse Supply Chain Management Big Data Industrie 4.0 Aufsatzsammlung |
| work_keys_str_mv | AT rahimiiman bigdataanalyticsinsupplychainmanagementtheoryandapplications AT gandomiamirh bigdataanalyticsinsupplychainmanagementtheoryandapplications AT fongsimonjames bigdataanalyticsinsupplychainmanagementtheoryandapplications AT ulkumali bigdataanalyticsinsupplychainmanagementtheoryandapplications |