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Prescriptive Analytics

The Final Frontier for Evidence-Based Management and Optimal Decision Making
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Paperback, blz. | Engels
Pearson Education | e druk, 2019
ISBN13: 9780134387055
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Pearson Education e druk, 2019 9780134387055
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Samenvatting

Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimise decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process.

In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimisation, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyse complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

Specificaties

ISBN13:9780134387055
Taal:Engels
Bindwijze:Paperback
Uitgever:Pearson Education

Inhoudsopgave

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

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