Preface xii Chapter 1 Introduction to Business Analytics and Decision-Making 1 Data and Business Analytics 1 An Overview of the Human Decision-Making Process 4 Simon's Theory of Decision-Making 5 An Overview of Business Analytics 21 Why the Sudden Popularity of Analytics? 22 What Are the Application Areas of Analytics? 23 What Are the Main Challenges of Analytics? 24 A Longitudinal View of Analytics 27 A Simple Taxonomy for Analytics 31 Analytics Success Story: UPS's ORION Project 36 Background 37 Development of ORION 38 Results 39 Summary 40 Analytics Success Story: Man Versus Machine 40 Checkers 41 Chess 41 Jeopardy! 42 Go 42 IBM Watson Explained 43 Conclusion 47 References 47 Chapter 2 Optimization and Optimal Decision-Making 49 Common Problem Types for LP Solution 51 Types of Optimization Models 52 Linear Programming 52 Integer and Mixed-Integer Programming 52 Nonlinear Programming 53 Stochastic Programming 54 Linear Programming for Optimization 55 LP Assumptions 56 Components of an LP Model 58 Process of Developing an LP Model 59 Hands-On Example: Product Mix Problem 60 Formulating and Solving the Same Product-Mix Problem in Microsoft Excel 68 Sensitivity Analysis in LP 72 Transportation Problem 76 Hands-On Example: Transportation Cost Minimization Problem 76 Network Models 81 Hands-On Example: The Shortest Path Problem 82 Optimization Modeling Terminology 89 Heuristic Optimization with Genetic Algorithms 92 Terminology of Genetic Algorithms 93 How Do Genetic Algorithms Work? 95 Limitations of Genetic Algorithms 97 Genetic Algorithm Applications 98 Conclusion 98 References 99 Chapter 3 Simulation Modeling for Decision-Making 101 Simulation Is Based on a Model of the System 106 What Is a Good Simulation Application? 110 Applications of Simulation Modeling 111 Simulation Development Process 113 Conceptual Design 114 Input Analysis 114 Model Development, Verification, and Validation 115 Output Analysis and Experimentation 116 Different Types of Simulation 116 Simulation May Be Dynamic (Time-Dependent) or Static (Time-Independent) 117 Simulations May Be Stochastic or Deterministic 118 Simulations May Be Discrete and Continuous 118 Monte Carlo Simulation 119 Simulating Two-Dice Rolls 120 Process of Developing a Monte Carlo Simulation 122 Illustrative Example-A Business Planning Scenario 125 Advantages of Using Monte Carlo Simulation 129 Disadvantages of Monte Carlo Simulation 129 Discrete Event Simulation 130 DES Modeling of a Simple System 131 How Does DES Work? 135 DES Terminology 138 System Dynamics 143 Other Varieties of Simulation Models 149 Lookahead Simulation 149 Visual Interactive Simulation Modeling 150 Agent-Based Simulation 151 Advantages of Simulation Modeling 153 Disadvantages of Simulation Modeling 154 Simulation Software 155 Conclusion 158 References 159 Chapter 4 Multi-Criteria Decision-Making 161 Types of Decisions 164 A Taxonomy of MCDM Methods 165 Weighted Sum Model 170 Hands-On Example: Which Location Is the Best for Our Next Retail Store? 172 Analytic Hierarchy Process 173 How to Perform AHP: The Process of AHP 176 AHP for Group Decision-Making 184 Hands-On Example: Buying a New Car/SUV 185 Analytics Network Process 190 How to Conduct ANP: The Process of Performing ANP 194 Other MCDM Methods 201 TOPSIS 202 ELECTRE 202 PROMETHEE 204 MACBETH 205 Fuzzy Logic for Imprecise Reasoning 207 Illustrative Example: Fuzzy Set for a Tall Person 208 Conclusion 210 References 210 Chapter 5 Decisioning Systems 213 Artificial Intelligence and Expert Systems for Decision-Making 214 An Overview of Expert Systems 222 Experts 222 Expertise 223 Common Characteristics of ES 224 Applications of Expert Systems 228 Classical Applications of ES 228 Newer Applications of ES 229 Structure of an Expert System 232 Knowledge Base 233 Inference Engine 233 User Interface 234 Blackboard (Workplace) 234 Explanation Subsystem (Justifier) 235 Knowledge-Refining System 235 Knowledge Engineering Process 236 1 Knowledge Acquisition 237 2 Knowledge Verification and Validation 239 3 Knowledge Representation 240 4 Inferencing 241 5 Explanation and Justification 247 Benefits and Limitations of ES 249 Benefits of Using ES 249 Limitations and Shortcomings of ES 253 Critical Success Factors for ES 254 Case-Based Reasoning 255 The Basic Idea of CBR 255 The Concept of a Case in CBR 257 The Process of CBR 258 Example: Loan Evaluation Using CBR 260 Benefits and Usability of CBR 260 Issues and Applications of CBR 261 Conclusion 266 References 267 Chapter 6 The Future of Business Analytics 269 Big Data Analytics 270 Where Does the Big Data Come From? 271 The Vs That Define Big Data 273 Fundamental Concepts of Big Data 276 Big Data Technologies 280 Data Scientist 282 Big Data and Stream Analytics 284 Deep Learning 289 An Introduction to Deep Learning 291 Deep Neural Networks 295 Convolutional Neural Networks 296 Recurrent Networks and Long Short-Term Memory Networks 301 Computer Frameworks for Implementation of Deep Learning 304 Cognitive Computing 308 How Does Cognitive Computing Work? 310 How Does Cognitive Computing Differ from AI? 311 Conclusion 312 References 313 Index 315