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

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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

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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

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