Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition BUSINESS INTELLIGENCE ANALYTICS Chapter 6 AND DATA SCIENCE Prescriptive Analytics A Managerial Optimization and Simulation Ramesh Sharda Dursun Delen Efraim Turban PEarson Pearson Copyright 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Business Intelligence, Analytics, and Data Science: A Managerial Perspective Fourth Edition Chapter 6 Prescriptive Analytics: Optimization and Simulation Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links by using INSERT+F7
Learning Objectives (1 of2 6. 1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6. 4 Describe how spreadsheets can be used for analytical modeling and solutions Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (1 of 2) 6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions
Learning Objectives (2 of 2) 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6. 8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Learning Objectives (2 of 2) 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation
Opening Vignette School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts Discussion Questions 1. What decision was being made in this vignette? 2. What data(descriptive and or predictive )might one need to make the best allocations in this scenario? 3. What other costs or constraints might you have to consider in awarding contracts for such routes? 4. Which other situations might be appropriate for applications of such models Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Opening Vignette School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts Discussion Questions 1. What decision was being made in this vignette? 2. What data (descriptive and or predictive) might one need to make the best allocations in this scenario? 3. What other costs or constraints might you have to consider in awarding contracts for such routes? 4. Which other situations might be appropriate for applications of such models?
Model-Based Decision Making Prescriptive analytics -making decision using some kind of analytical model Descriptive and predictive analytics creates the foundation (i.e, choice alternatives) for prescriptive analytics (i.e, making best possible decision) Descriptive and Predictive leads to Prescriptive Descriptive, Predictive -> Prescriptive ° EXample Profit maximization based on optimal spending on promotions and product/service pricing Pearson Copyright C 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved Model-Based Decision Making • Prescriptive analytics – making decision using some kind of analytical model – Descriptive and predictive analytics creates the foundation (i.e., choice alternatives) for prescriptive analytics (i.e., making best possible decision) • Descriptive and Predictive leads to Prescriptive – Descriptive, Predictive → Prescriptive • Example – Profit maximization based on optimal spending on promotions and product/service pricing