Prescriptive analytics BA4206 Anna University PPT
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The document provides an overview of prescriptive analytics, detailing its role in aiding business decision-making through data analysis. It covers various optimization techniques, types of prescriptive modeling, and applications across multiple industries, along with methodologies used in the field. Additionally, it discusses the distinction between prescriptive, predictive, and business analytics, highlighting their impact on improving organizational performance.
Prescriptive analytics BA4206 Anna University PPT
- 1. PRESCRIPTIVE ANALYTICS Mrs. Rhema Joy Sharath
- 2. SYLLABUS Introduction to Prescriptive analytics Prescriptive Modeling Non Linear Optimization Demonstrating Business Performance Improvement 2
- 3. Introduction Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Forecasting the load on the electric grid over the next 24 hours is an example of predictive analytics, whereas deciding how to operate power plants based on this forecast represents prescriptive analytics. 3
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- 5. Optimization Optimization consists in the construction of a mathematical model (with variables and equations) whose resolution allows finding the best solution to a problem: the optimal one. A classic example is the traveling salesman problem, consisting in visiting a set of cities only once and returning to the city of departure traveling the shortest possible distance. 5
- 6. Stochastic Optimisation When the optimisation is done while considering uncertainty, it is called stochastic optimisation. 6
- 9. Prescriptive Modeling 9 Prescriptive models are designed to find the 'best' solution for a given problem. Such models make trade-offs between complicated options based on optimization criteria — that's why they're also called optimization models.
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- 11. Types of Prescriptive Modeling Linear programming Integer Programming Non-linear Optimization Decision Analysis Case studies Simulation Other methodologies
- 12. Linear Programming 12 Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships.
- 13. Integer Programming 13 This is the same as LP, but it permits decision variables to be integer values.
- 14. Non-linear Optimisation (NLP) 14 • Nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints or the objective function are nonlinear. • An optimization problem is one of calculation of the extrema (maxima, minima or stationary points) of an objective function over a set of unknown real variables and conditional to the satisfaction of a system of equalities and inequalities, collectively termed constraints.
- 15. Decision analysis 15 Decision analysis is a formalized approach to making optimal choices under conditions of uncertainty.
- 16. Case studies 16 A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context.
- 17. Simulation 17 A simulation imitates the operation of real world processes or systems with the use of models. The model represents the key behaviours and characteristics of the selected process or system while the simulation represents how the model evolves under different conditions over time.
- 18. Other methodologies 18 1. Network modeling 2. Project scheduling 3. Dynamic programming 4. Queuing models 5. Decision support systems 6. Heuristics 7. Artificial intelligence 8. Expert systems 9. Markov processes 10.Decision tree analysis 11.Game theory 12.Goal programming 13.Reliability analysis 14.Genetic programming 15.Data development analysis
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- 21. “ Rules-based techniques / Heuristics including inference engines, scorecards, and decision trees are used in prescriptive analytics to make a decision such as choosing to shut down equipment for maintenance when sensor readings exceed thresholds, or accepting a financial transaction when its score is high enough. 21
- 22. Analytics can be defined as a process that involves the use of statistical techniques (measures of central tendency, graphs, and so on), information system software (data mining, sorting routines), and operations research methodologies (linear programming) to explore, visualize, discover and communicate patterns or trends in data. Simply, analytics convert data into useful information. Analytics
- 23. Business analytics (BA) can be defined as a process beginning with business-related data collection and consisting of sequential application of descriptive, predictive, and prescriptive major analytic components, the outcome of which supports and demonstrates business decision-making and organizational performance. Business intelligence (BI) can be defined as a set of processes and technologies that convert data into meaningful and useful information for business purposes. Business analytics (BA)
- 30. Predictive analytics is the application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models to identify trends and relationships not readily observed in the descriptive analytic analysis. Knowing that relationships exist explains why one set of independent variables (predictive variables) influences dependent variables like business performance. Analysis of predictive analytics
- 31. The procedure by which multiple regression can be used to evaluate which independent variables are best to include or exclude in a linear model is called step-wise multiple regression. The backward step-wise regression starts with all the independent variables placed in the model, and the step-wise process removes them one at a time based on worst predictors first until a statistically significant model emerges. The forward step-wise regression starts with the best related variable (using correction analysis as a guide), and then step-wise adds other variables until adding more will no longer improve the accuracy of the model. Multiple regression
- 44. Demonstrating Business Performance Improvement
- 45. Thank you Rhema Joy Sharath 45