You are previewing Global Business Analytics Models: Concepts and Applications in Predictive, Healthcare, Supply Chain, and Finance Analytics.
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Global Business Analytics Models: Concepts and Applications in Predictive, Healthcare, Supply Chain, and Finance Analytics

Book Description

THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS

  • Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy

  • Analytical opportunities to solve key managerial problems in global enterprises

  • Written for working managers: packed with realistic, useful examples

  • This guide helps global managers use modern analytics to gain reliable, actionable, and timely business intelligence–and use it to manage risk, build winning strategies, and solve urgent problems.

    Dr. Hokey Min offers a practical, easy-to-understand overview of business analytics in a global context, focusing especially on managerial and strategic implications. After demystifying today’s core quantitative tools, he demonstrates them at work in a wide spectrum of global applications.

    You’ll build models to help segment global markets, forecast demand, assess risk, plan financing, optimize supply chains, and more. Along the way, you’ll find practical guidance for developing analytic thinking, operationalizing Big Data in global environments, and preparing for future analytical innovations.

    Whether you’re a global executive, strategist, analyst, marketer, supply chain professional, student or researcher, this book will help you drive real value from analytics–in smarter decisions, improved strategy, and better management.

    In today’s global business environments characterized by growing complexity, volatility, and uncertainty, business analytics has become an indispensable tool for managing these challenges. Specifically, global managers need analytics expertise to solve problems, identify opportunities, shape strategy, mitigate risk, and improve their day-to-day operational efficiency.

    Now, for the first time, there’s an analytics guide designed specifically for decision-makers in global organizations. Leveraging his experience teaching a number of students and training hundreds of managers and executives, Dr. Hokey Min demystifies the principles and tools of modern business analytics, and demonstrates their real-world use in global business.

    First, Dr. Min identifies key success factors and mindsets, helping you establish the preconditions for effective analysis. Next, he walks you through the practicalities of collecting, organizing, and analyzing Big Data, and developing models to transform them into actionable insight.

    Building on these foundations, he illustrates core analytical applications in finance, healthcare, and global supply chains. He concludes by previewing emerging trends in analytics, including the newest tools for automated decision-making.

    Compare today’s key quantitative tools

    Stats, data mining, OR, and simulation: how they work, when to use them

    Get therightdata…

    …and get the data right

    Predict the future…

    …and sense its arrival sooner than others can

    Implement high-value analytics applications…

    …in finance, supply chains, healthcare, and beyond

    Table of Contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Dedication Page
    5. Contents
    6. Acknowledgments
    7. About the Author
    8. 1. Introduction to Business Analytics
      1. 1.1 The Origin and Evolution of Business Analytics
      2. 1.2 Developing Analytical Thinking
      3. 1.3 Operationalizing Big Data from Global Perspectives
      4. 1.4 Extracting Useful Information from Big Data
      5. 1.5 Unique Challenges for Business Analytics
      6. 1.6 Capitalizing on Business Analytics for Building a Winning Global Strategy
      7. Bibliography
    9. 2. Collecting, Sorting, Prioritizing, and Storing Big Data
      1. 2.1 Finding and Capturing the Right Data
      2. 2.2 Data Sampling
      3. 2.3 Data Preparation
      4. 2.4 Data Segmentation
      5. 2.5 Data Filtering
      6. 2.6 Data Warehousing
      7. 2.7 Data Security
      8. 2.8 Fitting Analytics Models to Data
      9. Bibliography
    10. 3. Business Analytics Models
      1. 3.1 Quantitative Tools for Business Analytics
      2. 3.2 Basic Statistical Techniques
      3. 3.3 R Programming
      4. 3.4 Hypothesis Testing
        1. 3.4.1 t-Test
        2. 3.4.2 ANOVA Test
        3. 3.4.3 Nonparametric Test
      5. 3.5 Power Analysis
      6. 3.6 Data Mining
        1. 3.6.1 Decision Trees
        2. 3.6.2 Neural Networks
        3. 3.6.3 Text Mining
        4. 3.6.4 Image Mining
      7. Bibliography
    11. 4. Predictive Analytics
      1. 4.1 Predicting International Customer Behavior
      2. 4.2 Demand Forecasting in Unfamiliar Foreign Markets
        1. 4.2.1 Moving Average
        2. 4.2.2 Exponential Smoothing
        3. 4.2.3 Trend Analysis
        4. 4.2.4 Focus Forecasting
        5. 4.2.5 Agent-Based Forecasting
      3. 4.3 Global Market Basket Analysis
      4. 4.4 Risk Analytics
      5. 4.5 Digital Analytics
      6. 4.6 Social Sensing
      7. 4.7 Mobile Analytics
      8. Bibliography
    12. 5. Essentials for the Successful Implementation of Business Analytics
      1. 5.1 Understanding the Voice of Overseas Customers
      2. 5.2 Collaborating with Foreign Business Partners for Sharing Big Data
        1. 5.2.1 Building Trust
        2. 5.2.2 Establishing an Information Exchange Mechanism
        3. 5.2.3 Ensuring Secure Data Transmission
      3. 5.3 Analytics Execution and Implementation
      4. 5.4 Performance Measurement and Metrics
      5. 5.5 Outcome Analysis
      6. 5.6 Corrective Actions
      7. 5.7 Emulating Best-in-Class Practices
      8. Bibliography
    13. 6. Global Finance Analytics
      1. 6.1 Foreign Market Scenario Planning
      2. 6.2 Financing Global Business Operations through Capital Management
      3. 6.3 Global Financial Risk Assessment
        1. 6.3.1 Foreign Direct Investment Risk Analysis
        2. 6.3.2 Loan and Credit Risk Assessment
        3. 6.3.3 Liquidity Risk Assessment
        4. 6.3.4 Foreign Currency Exchange Risk Assessment
        5. 6.3.5 Value at Risk (VaR) as the Financial Risk Measure
      4. 6.4 Foreign Investment Portfolio Analysis
      5. 6.5 Product/Service Pricing Using Analytics
      6. 6.6 Multinational Profit Planning and Budgeting Using Analytics
      7. Bibliography
    14. 7. Global Supply Chain Analytics
      1. 7.1 Turning Integrated Big Data into Supply Chain Intelligence
      2. 7.2 Global Sales and Promotion Analytics
      3. 7.3 Global Sourcing Analytics
      4. 7.4 Contract Manufacturing Analytics
      5. 7.5 Distribution Analytics
      6. 7.6 Transportation Analytics
      7. 7.7 Integrating Functional Analytics into Global Supply Chain Management
      8. Bibliography
    15. 8. Healthcare Analytics
      1. 8.1 Healthcare Analytics as an Emerging Discipline
      2. 8.2 Big Data in Healthcare
      3. 8.3 Analyzing Clinical and Pharmaceutical Data
      4. 8.4 Analyzing the Voice of the Patient
      5. 8.5 Healthcare Quality Function Deployment via Analytics
      6. 8.6 Healthcare Outcome Analysis
      7. Bibliography
    16. 9. The Future of Business Analytics
      1. 9.1 Innovating Analytics
      2. 9.2 Embedding Business Analytics into Enterprise-wide Information Systems
      3. 9.3 Future Roles of Business Analytics in Global Business Intelligence
      4. 9.4 Epilogue
      5. Bibliography
    17. Index