Prescriptive Analytics

Dursun Delen, PhD, is the holder of the William S. Spears Endowed Chair in Business Administration, Patterson Family Endowed Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). He received his PhD in Industrial Engineering and Management from OSU in 1997. Prior to his appointment as an Assistant Professor at OSU in 2001, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for five years, during which he led a number of decision support, information systems, and advanced analytics-related research projects funded by federal agencies including DoD, NASA, NIST, and DOE. Dr. Delen provides professional education and consultancy services to companies and government agencies on analytics and information systems-related topics. He is often invited to national and international conferences for invited talks and keynote addresses on topics related to data/text mining, business intelligence, decision support systems, business analytics, and knowledge management. He served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management in Seoul, South Korea, and regularly chairs tracks and mini-tracks at various business analytics and information systems conferences. He has published more than 150 peer-reviewed articles. His research has appeared in major journals, including Decision Sciences, Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, and Expert Systems with Applications. He recently authored/co-authored ten books/textbooks within the broad areas of business analytics, decision support systems, data/text mining, and business intelligence. He is the editor-in-chief for the Journal of Business Analytics, AI in Business, and International Journal of Experimental Algorithms, senior editor for Decision Support Systems and Decision Sciences, associate editor for Journal of Business Research, Decision Analytics, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals.

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