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Business Analytics with Management Science Models and Methods

Book Description

Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics.

Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.

Table of Contents

  1. About This eBook
  2. Title Page
  3. Copyright Page
  4. Praise for Business Analytics with Management Science Models and Methods
  5. Dedication Page
  6. Contents
  7. Acknowledgments
  8. About the Author
  9. Preface
  10. About the Book
  11. 1. Business Analytics with Management Science
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Success Stories
    3. Introduction
    4. Implementing Business Analytics
    5. Business Analytics Domain
      1. Databases and Data Warehouses
      2. Descriptive Analytics
      3. Predictive Analytics
      4. Prescriptive Analytics
    6. Challenges with Business Analytics
      1. Lack of Management Science Experts
      2. Analytics Brings Change in the Decision-Making Process
      3. Big Data Leads to Incorrect Information
      4. Big Data Demands Big Thinking
    7. Exploring Big Data with Prescriptive Analytics
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  12. 2. Introduction to Linear Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Chevron Optimizes Processing of Crude Oil
    3. Introduction
    4. LP Formulation
      1. Example 1: Rolls Bakery Production Runs
      2. Example 2: Political Advertisement Agency
    5. Solving LP Models: A Graphical Approach
      1. Solution for Rolls Bakery Production Run
      2. Solution for the PoliCom Campaign
    6. Possible Outcome Solutions to LP Model
      1. Multiple Solutions
      2. No Solutions
      3. Unbounded Solutions
      4. Solving LP Problems with Solver
      5. Solving the Rolls Bakery Problem with Solver
      6. Solving the PoliCom Problem with Solver
    7. Exploring Big Data with LP Models
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  13. 3. Business Analytics with Linear Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Nu-kote Minimizes Shipment Cost
    3. Introduction
    4. General Formulation of LP Models
    5. Formulating a Large LP Model
      1. Example: Primer Manufacturer Inc. Production Mix
    6. Solving Linear Programming Models with Excel
      1. Step 1: Set Up Constraints and Objective Function in Solver
      2. Step 2: Generate the Solution and Results
      3. Step 3: Use Sensitivity Analysis to Gain Greater Insight
    7. Big Optimizations with Big Data
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  14. 4. Business Analytics with Nonlinear Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Netherlands Increases Protection from Flooding
    3. Introduction
    4. Challenges to NLP Models
      1. Local Optimum Versus Global Optimum
      2. The Solution Is Not Always Found at an Extreme Point
      3. Multiple Feasible Areas
    5. Example 1: World Class Furniture
      1. Formulation of NLP Models
      2. Solving NLP Models with Solver
      3. Sensitivity Analysis for NLP Models
    6. Example 2: Optimizing an Investment Portfolio
      1. Investment Portfolio Problem Formulation
      2. Solving the Portfolio Problem
    7. Exploring Big Data with Nonlinear Programming
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  15. 5. Business Analytics with Goal Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Airbus Uses Multi-Objective Optimization Models
    3. Introduction
    4. GP Formulation
    5. Example 1: Rolls Bakery Revisited
      1. GP Formulation Steps
      2. Putting It Together: GP Formulation for Rolls Bakery
    6. Solving GP Models with Solver
    7. Example 2: World Class Furniture
      1. Priorities and GP Formulation
      2. Solving NLGP Models with Solver
    8. Exploring Big Data with Goal Programming
    9. Wrap Up
    10. Review Questions
    11. Practice Problems
  16. 6. Business Analytics with Integer Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Zara Uses Mixed IP Modeling
    3. Introduction
    4. Formulation and Graphical Solution of IP Models
    5. Types of Integer Programming Models
    6. Solving Integer LP Models with Solver
    7. Solving Nonlinear IP Models with Solver
    8. Solving Integer GP Models with Solver
    9. The Assignment Method
      1. General Formulation of the Assignment Problem
      2. Solving the Assignment Method with Solver
    10. The Knapsack Problem
      1. General Formulation of the Knapsack Problem
    11. Exploring Big Data with Integer Programming
    12. Wrap Up
    13. Review Questions
    14. Practice Problems
  17. 7. Business Analytics with Shipment Models
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Danaos Saves Time and Money with Shipment Models
    3. Introduction
    4. The Transportation Model
      1. General Formulation of the Transportation Model
      2. Network Diagram of Transportation Models
      3. Solving Transportation Model with Solver
      4. Sensitivity Analysis
    5. The Transshipment Method
      1. General Formulation of the Transshipment Model
      2. Solving the Transshipment Model with Solver
    6. Exploring Big Data with Shipment Models
    7. Wrap Up
    8. Review Questions
    9. Practice Problems
  18. 8. Marketing Analytics with Linear Programming
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Hewlett-Packard Increases Profit with Marketing Optimization Models
    3. Introduction
    4. RFM Overview
      1. Recency Value
      2. Frequency Value
      3. Monetary Value
    5. RFM Analysis with Excel
      1. Using a Pivot Table to Summarize Records
      2. Using VLOOKUP to Assign RFM Scores
    6. Optimizing RFM-Based Marketing Campaigns
    7. LP Models with Single RFM Dimension
      1. LP Model for the Recency Case
      2. LP Model for the Frequency Case
      3. LP Model for the Monetary Value Case
    8. Marketing Analytics and Big Data
    9. Wrap Up
    10. Review Questions
    11. Practice Problems
  19. 9. Marketing Analytics with Multiple Goals
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: First Tennessee Bank Improves Marketing Campaigns
    3. Introduction
    4. LP Models with Two RFM Dimensions
      1. LP Model for the Recency and Frequency Case
      2. LP Model for the Recency and Monetary Value Case
      3. LP Model for the Frequency and Monetary Case
    5. LP Model with Three Dimensions
      1. LP Model Formulation
      2. Solving the RFM Model with Three Dimensions
    6. A Goal Programming Model for RFM
      1. GP Model Notations
      2. GP Model Formulation
      3. Solving the GP RFM Model
    7. Exploring Big Data with RFM Analytics
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  20. 10. Business Analytics with Simulation
    1. Chapter Objectives
    2. Prescriptive Analytics in Action: Blood Assurance Uses Simulation to Manage Platelet Inventory
    3. Introduction
    4. Basic Simulation Terminology
      1. System
      2. State of a System
      3. Discrete Versus Continuous Models
      4. Static Versus Dynamic Simulation Models
      5. Deterministic Versus Stochastic Simulation Model
    5. Simulation Methodology
      1. Problem Description
      2. Conceptual Model
      3. Data Collection
      4. Computer Simulation Model
      5. Design Experiments
      6. Simulation Runs
      7. Analyze Output
      8. Results and Recommendations
    6. Simulation Methodology in Action
      1. Problem Description
      2. Conceptual Model
      3. Data Collection
      4. Computer Simulation Model
      5. Design Experiments and Simulation Runs
      6. Analyze Output, Results, and Recommendations
    7. Exploring Big Data with Simulation
    8. Wrap Up
    9. Review Questions
    10. Practice Problems
  21. A. Excel Tools for the Management Scientist
    1. 1: Shortcut Keys
    2. 2: Sumif
    3. 3: Averageif
    4. 4: Countif
    5. 5: Iferror
    6. 6: Vlookup or Hlookup
    7. 7: Transpose
    8. 8: Sumproduct
    9. 9: If
    10. 10: Pivot Table
      1. Step 1: Select the Data
      2. Step 2: Go to Insert and Click on New Pivot Table Option
      3. Step 3: Select the Target Cell Where You Want to Place the Pivot Table
      4. Step 4: Create the Pivot Report with Required Criteria
  22. B. A Brief Tour of Solver
    1. Setting Up Constraints and the Objective Function in Solver
    2. Selecting Solver Options
      1. All Methods Options
      2. GRG Nonlinear Options
      3. Evolutionary Options
      4. Generate the Solution
  23. References
  24. Index