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Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making, 1st edition
Published by Pearson FT Press (October 24, 2019) © 2020
- Dursun Delen Oklahoma State University
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Using prescriptive analytics techniques, decision-makers can identify their best choices, optimize their approaches for handling new opportunities and risks, and update their decision options continually as new data arrives. Of all the forms of business analytics available, prescriptive analytics is most directly linked to successful decision-making. In Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making, Dr. Dursun Delen illuminates the state-of-the-art in prescriptive analytics for both business professionals and students. Delen’s end-to-end, all-inclusive, holistic approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning techniques, and more. Throughout, he promotes understanding by presenting numerous conceptual illustrations, example problems and solutions, and motivational case and success stories. Coverage includes:
- An overview and taxonomy of business analytics, and where prescriptive analytics fits into the big picture
- How humans make decisions and how prescriptive analytics can support them
- How to use mathematical modeling to find the optimal solution for achieving business objectives within real-world constraints
- How Monte-Carlo, discrete, and continuous simulations can help you analyze complex systems for better decision-making
- Powerful multi-criteria decision making techniques and examples
- Expert systems, case-based reasoning, and their renewed impact on modern decision systems
- Advanced prescriptive analytics techniques that leverage big data, deep learning, and cognitive computing
- 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
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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 ESÂ Â Â Â 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
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 ESÂ Â Â Â 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
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.
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.
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