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

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

Table of Contents:
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

About the Author :
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.