This book is written for both professionals who are interested in developing a holistic understanding about business analytics and especially about prescriptive analytics and college students, both at graduate and undergraduate levels, who are in need of a nicely balance book between theory and practice to explain prescriptive analytics as the top layer in business analytics continuum. • End-to-end, all-inclusive, holistic approach to prescriptive analytics—not only covering optimization and simulation, but also including multi-criteria decision-making methods along with inference- and…mehr
This book is written for both professionals who are interested in developing a holistic understanding about business analytics and especially about prescriptive analytics and college students, both at graduate and undergraduate levels, who are in need of a nicely balance book between theory and practice to explain prescriptive analytics as the top layer in business analytics continuum. • End-to-end, all-inclusive, holistic approach to prescriptive analytics—not only covering optimization and simulation, but also including multi-criteria decision-making methods along with inference- and heuristic-based decisioning techniques. • Enhanced with numerous conceptual illustrations, example problems and solutions, and motivational case and success stories. • Chapter 1 -- overview of business analytics, its longitudinal perspective and a simple taxonomy, and where prescriptive analytics fits into this big picture. • Chapter 2 -- introduces the topic of optimization and describes different types of optimization methods using simple yet practical examples and application cases. • Chapter 3 -- explains simulation, including Monte-Carlo simulation, discrete and continuous simulation, as a powerful tool to analyse complex systems to make better decisions. • Chapter 4 -- introduces multi-criteria decision making along with a simple taxonomy, and provides descriptions and examples of a variety of popular techniques used for multi-criteria problems commonly found in practice. • Chapter 5 -- about expert systems and case-based reasoning. • Chapter 6 -- introduces the latest techniques in analytics, namely Big Data, Deep Learning, and Cognitive Computing, as the leading edge for the next generation of automated decisioning and prescriptive analytics. Business analytics has been gaining popularity faster than any management trends we have seen in the recent history. As an attempt to unify the understanding of "what business analytics is,” the business community along with educational community have developed a simple taxonomy where they defined analytics using three progressive phases/echelons: descriptive/diagnostic analyticsHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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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