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Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making

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Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making

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About

Features

  • An end-to-end, holistic guide to theory and practice — packed with conceptual illustrations, example problems and solutions, and case studies
  • Covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning techniques, and more
  • Previews emerging techniques utilizing big data, deep learning, and cognitive computing
  • By Dr. Dursun Delen, one of the world’s leading experts in advanced business analytics

Description

  • Copyright 2020
  • Dimensions: 6" x 9"
  • Pages: 352
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-438705-8
  • ISBN-13: 978-0-13-438705-5

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale

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 optimize 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 optimization, 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
  • Analyze 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

Sample Content

Online Sample Chapter

Introduction to Business Analytics and Decision-Making

Table of Contents

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

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