Prescriptive analytics BA4206 Anna University PPT

PRESCRIPTIVE
ANALYTICS
Mrs. Rhema Joy Sharath

SYLLABUS
Introduction to Prescriptive analytics
Prescriptive Modeling
Non Linear Optimization
Demonstrating Business Performance Improvement
2

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

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

Stochastic
Optimisation
When the optimisation is done while considering
uncertainty, it is called stochastic optimisation.
6

Prescriptive analytics
techniques
Simulation optimisation
Decision analysis
7

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Applications
of
Prescriptive
analytics
BFSI
Healt
hcare
Online
learning
Transportation
and
travel
Supply
chain
and
logistics
Manufacturing
Marketing
and
sales

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.

Types of Prescriptive Modeling
Linear
programming
Integer
Programming
Non-linear
Optimization
Decision Analysis
Case studies Simulation
Other
methodologies

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.

Integer Programming
13
This is the same as LP, but it permits decision variables to be integer
values.

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.

Decision analysis
15
Decision analysis is a formalized approach to making optimal choices
under conditions of uncertainty.

Case studies
16
A case study is an in-depth, detailed examination of a particular case (or cases) within a
real-world context.

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.

Other methodologies
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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

“
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

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

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)

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

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

PRESCRIPTIVE
ANALYTICS ANALYSIS

Demonstrating Business Performance Improvement

Thank you
Rhema Joy Sharath
45

Prescriptive analytics BA4206 Anna University PPT

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