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Produktbild: Prescriptive Analytics

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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.10.2019

Verlag

Pearson Academic

Seitenzahl

352

Maße (L/B/H)

22,5/15/2 cm

Gewicht

470 g

Sprache

Englisch

ISBN

978-0-13-438705-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.10.2019

Verlag

Pearson Academic

Seitenzahl

352

Maße (L/B/H)

22,5/15/2 cm

Gewicht

470 g

Sprache

Englisch

ISBN

978-0-13-438705-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Prescriptive Analytics

  • 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