Data Analytics and Business Intelligence

Table of contents

1. Cover Page
2. Title Page
3. Copyright Page
4. Preface
5. Contents
6. List of Contributors
7. Section I: Operations and Supply Chain Analytics
   1. 1 Wholesale Price Strategy of a Manufacturer under Collusion of Downstream Channel Members A Game-Theoretic Approach
      1. 1.1 Introduction
      2. 1.2 Related Literature
      3. 1.3 Research Model
      4. 1.4 Equilibrium Analysis of Collusion (CL) Game
      5. 1.5 Results and Discussion Through Numerical Experiment
         1. 1.5.1 Profit of the BM-Store
         2. 1.5.2 Profit of the E-store
         3. 1.5.3 Profit of the Manufacturer
      6. 1.6 Discussions and Managerial Implications
      7. 1.7 Conclusions and Directions for Future Research
      8. References
   2. 2 AI and ML in Supply Chain Decision Making A Pragmatic Discussion
      1. 2.1 Introduction
      2. 2.2 Literature Review
      3. 2.3 AI Technologies for Supply Chain Planning
         1. 2.3.1 Artificial Intelligence
         2. 2.3.2 Machine Learning
         3. 2.3.3 Deep Learning
         4. 2.3.4 Fuzzy Logic
         5. 2.3.5 Agent-based Systems
      4. 2.4 Data Analytics Applications in Supply Chain Processes
         1. 2.4.1 Warehouse Management
         2. 2.4.2 Improving Inventory Planning
         3. 2.4.3 Network Design and Transportation
         4. 2.4.4 AI Implications in Purchasing Management
         5. 2.4.5 Customer Relationship Management
      5. 2.5 Challenges in AI and ML Implementation
      6. 2.6 Conclusion, Limitation, and Future Research
      7. References
   3. 3 Assessing Relations of Lean Manufacturing, Industry 4.0 and Sustainability in the Manufacturing Environment
      1. 3.1 Introduction
      2. 3.2 Literature Review
         1. 3.2.1 Review of LM and Sustainability
         2. 3.2.2 Review of Industry 4.0 and Sustainability
         3. 3.2.3 Research Gap
      3. 3.3 Methodology
         1. 3.3.1 Framework of the Research
         2. 3.3.2 Research Method
         3. 3.3.3 Survey Structure
         4. 3.3.4 Sample and Data Collection Strategy
         5. 3.3.5 Research Model of this Study
         6. 3.3.6 The Hypothesis of this Study
         7. 3.3.7 Measure of the Study
      4. 3.4 Results
         1. Analysis of Survey Outcomes
         2. Outcomes
      5. 3.5 Conclusion
      6. 3.6 Implications, Limitations, and Future Work
      7. References
   4. 4 Role of Artificial Intelligence in Supply Chain Management
      1. 4.1 Introduction
      2. 4.2 Applications and Challenges in the Adoption of Artificial Intelligence
      3. 4.3 Literature Review
         1. 4.3.1 Forecast Demand
         2. 4.3.2 Supplier Relations
         3. 4.3.3 Smart Manufacturing
         4. 4.3.4 Transportation and Warehousing
         5. 4.3.5 Smart Retailing
         6. 4.3.6 Customer Satisfaction
      4. 4.4 Research Hypothesis
      5. 4.5 Research Methodology
      6. 4.6 Data Analysis
      7. 4.7 Conclusion
      8. 4.8 Practical Implications and Future Scope
      9. References
   5. 5 Impact of Blockchain in Creating a Sustainable Supply Chain
      1. 5.1 Introduction
      2. 5.2 Review of Literature
         1. 5.2.1 Blockchain
         2. 5.2.2 Sustainable Supply Chains
      3. 5.3 Research Methodology
      4. 5.4 Challenges in Supply Chain
         1. Challenges in Low Carbon Emission Supply Chains
         2. Challenges in Audits and Certification
         3. Challenges in the Carbon C redit Market
      5. 5.5 Used Cases on Blockchain for Sustainable Practices
         1. Blockchain in the administration of Carbon Credit Market
      6. 5.6 Conclusion
      7. References
   6. 6 Exploring Adoption of Blockchain Technology for Sustainable Supply Chain Management
      1. 6.1 Introduction
      2. 6.2 Overview of Blockchain Technology
      3. 6.3 Highlights and Challenges in the Adoption of Blockchain Technology
      4. 6.4 Application of Blockchain Technology in Supply Chain Management
      5. 6.5 Understanding Challenges to Blockchain Technology Adoption in the Supply Chain
      6. 6.6 Conclusion
      7. References
8. Section II Data Mining, Computational Framework, and Practices
   1. 7 Mathematical Model of Consensus and its Adaptation to Achievement Consensus in Social Groups
      1. 7.1 Introduction
      2. 7.2 Description of the Model for Consensus Based on Regular Markov Chains
      3. 7.3 Specific Cases in The Model of Attaining Consensus
         1. 7.3.1 Coalitions
         2. 7.3.2 Domination
         3. 7.3.3 Presence of One or Several Autocratic Group Members
         4. 7.3.4 Global Domination
         5. 7.3.5 Responsibility Shift
      4. 7.4 Analysis of The General Case in The Consensus Model
      5. 7.5 Consensus Building Model in Coalitions
         1. 7.5.1 Unilateral Concession by Members of a Small Coalition
         2. 7.5.2 Unilateral Concessions by Members of a Large Coalition
      6. 7.6 Presence of One or Several Autocratic Group Members Domination
         1. 7.6.1 Time Required to Reach a Consensus When There are One or Two Autocratic Group Members
         2. 7.6.2 Time Required to Reach a Consensus When There is a Leader in the Group
      7. 7.7 Influence of the Unilateral Concession Upon Variability of the Number of Negotiations
      8. 7.8 Main Results
      9. 7.9 Conclusion
      10. References
   2. 8 Data to Data Science A Phenomenal Journey
      1. 8.1 Introduction
      2. 8.2 Data Warehousing
      3. 8.3 Data Warehousing and its Issues
      4. 8.4 Data Mining and its Challenges
      5. 8.5 Big Data
         1. a Descriptive Analytics (Bhatnagar et al. 2021, Han et al. 2020, Nida 1949)
         2. b Predictive Analytics
         3. c Perspective Analytics (Kumar 2021, Deshpande et al. 2019)
         4. d Diagnostic Analytics (Belle et al. 2015, Deshpande et al. 2019)
      6. 8.6 Challenges of Big Data
      7. 8.7 Conclusion
      8. References
   3. 9 Application of Algorithm on Computational Intelligence and Machine Learning for Product Design Emerging Needs and Challenges
      1. 9.1 Introduction
      2. 9.2 Research Objectives
      3. 9.3 Literature Review
      4. 9.4 Research Methodology
         1. Null Hypothesis: [Ho]
         2. Alternative hypothesis: [He]
      5. 9.5 Algorithm-Based Application of Computational Intelligence
         1. Performance Table of Product Simulation Model
      6. 9.6 Findings
      7. 9.7 Discussion and Conclusion
      8. 9.8 Limitations and Future Scope
      9. References
9. Section III Business Intelligence and Analytics Applications
   1. 10 HR ANALYTICS Galvanizing the Organizations with the Prowess of Technology
      1. 10.1 Introduction
      2. 10.2 Literature Review
      3. 10.3 Applications of HR Analytics
         1. 10.3.1 Talent Acquisition
         2. 10.3.2 Training and Development
         3. 10.3.3 Employee Performance
         4. 10.3.4 Employee Compensation
         5. 10.3.5 Employee Retention
         6. 10.3.6 Employee Engagement
      4. 10.4 Research Methodology
         1. 10.4.1 Brief of the Sample Company
         2. 10.4.2 A Brief About the Research Tool (Theoretical Basis of Herzberg's Theory)
      5. 10.5 Data Analysis and Interpretation
      6. 10.6 Challenges in Successful Implementation of HR Analytics
         1. 10.6.1 Organizing Quality Data
         2. 10.6.2 Development of HR Metrics
         3. 10.6.3 Insufficient Predictive and Optimization Models
         4. 10.6.4 Comprehension and Interpretation of HR Analytics
         5. 10.6.5 Division of Opinion about the Onus of HR Analytics
         6. 10.6.6 Leveraging the Company's Resources
         7. 10.6.7 Privacy Laws and Data Abuse
         8. 10.6.8 Translation of Insights into Action
      7. 10.7 Conclusion
      8. References
   2. 11 Marketing Analytics Concept, Applications, Opportunities, and Challenges Ahead
      1. 11.1 Introduction
      2. 11.2 Literature Review
         1. 11.2.1 Evolution of Marketing Analysis
         2. 11.2.2 Marketing Analytics—The Concept
         3. 11.2.3 Marketing Analytics—Applications
         4. 11.2.4 Analytics for Market Segmentation—Consumer Lifestyle Segmentation
      3. 11.3 Research Methodology
      4. 11.4 Analysis and Findings
      5. 11.5 Marketing Analytics—Opportunities and Challenges
      6. 11.6 Conclusion
      7. References
   3. 12 Effect of Social Media Usage on Anxiety During a Pandemic An Analytical Study on Young Adults
      1. 12.1 Introduction
      2. 12.2 Literature Review
      3. 12.3 Procedure and Measure
      4. 12.4 Analysis
      5. 12.5 Conclusion
      6. 12.6 Limitations of Tthe Study
      7. 12.7 Future Scope for the Study
      8. References
   4. 13 An Exploratory Study of Understanding Consumer Buying Behaviour Towards Green Cosmetics Products in the Indian Market
      1. 13.1 Introduction
      2. 13.2 Literature Review
      3. 13.3 Research Methodology
         1. 13.3.1 Research Problem
         2. 13.3.2 Significance of the Research
         3. 13.3.3 Management Decision Problems
         4. 13.3.4 Variables to the Objectives
         5. 13.3.5 Research Methods and Design
         6. 13.3.6 Data Collection and Segregation
      4. 13.4 Result Analysis
         1. Demographics of the sample
         2. Influence of Demographic Profile on Buying of Green Cosmetic Products
         3. Respondents' Knowledge, Buying Behaviour, Attitude related to buying Green Products
            1. General knowledge of Green Products (including Cosmetics)
            2. Have you ever bought any sustainable cosmetic products?
         4. Influence of Green Marketing on buying of Green Cosmetic Products
         5. Relationship between Green Consumerism and Social Status
         6. Influence of Psychographic factors on Buying of Green Cosmetic Products
      5. 13.5 Conclusion
      6. References
   5. 14 Analytical Study of Factors Affecting the Adoption of Blockchain by Fintech Companies
      1. 14.1 Introduction
      2. 14.2 Background of Blockchain Technology
      3. 14.3 Literature Review
      4. 14.4 Research Methodology
      5. 14.5 Data Analysis
         1. Hypothesis Testing-I
            1. Ordinal Logistic Regression Results—Commercial Banking
            2. Results and Conclusion for Hypothesis Testing-I
         2. Hypothesis Testing-II
            1. Ordinal Logistic Regression Results—Real Estate
            2. Results and Conclusion for Hypothesis Testing-II
         3. Hypothesis Testing-Iii
            1. Ordinal Logistic Regression Results—Capital Market Infrastructure
         4. Results and Conclusion for Hypothesis Testing-III
      6. 14.6 Conclusion
      7. References
   6. 15 A Study of the Performances of Small Cap, Large Cap and Banking & Financial Sector Funds of Nippon India AMC, ICICI Prudential AMC and Tata AMC
      1. 15.1 Introduction
      2. 15.2 Objectives of the Study
         1. 15.2.1 Significance of the Study
      3. 15.3 Literature Review
      4. 15.4 Research Gap
      5. 15.5 Research Methodology
         1. 15.5.1 Data Source
         2. 15.5.2 Data Analysis Techniques
      6. 15.6 Data Analysis
      7. 15.7 Findings
         1. 15.7.1 Small Cap fund
         2. 15.7.2 Large Cap Fund
         3. 15.7.3 Banking and Financial Services Fund
      8. 15.8 Conclusion
      9. 15.9 Limitations of the Study
      10. 15.10 Managerial Implications
      11. References
10. Index