INTRODUCTION TO BUSINESS
ANALYTICS AND OPERATIONAL
RESEARCH SOLUTION
METHODS
WITH DECISION ANALYSIS, LINEAR PROGRAMMING,
INVENTORY CONTROL, SIMULATION AND MARKOV CHAINS
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, Statswork
Group: www.statswork.com
Email: info@statswork.com
TODAY'S
DISCUSSION
Outline of Topics
Introduction
Business Analytics
Benefits of Business Analytics
Goals of Business Analytics
Markov chain Example
Conclusion
Introduction
There is a growing demand in the field of business analytics.
It actually means that what outcome we should get in business from the data to make
better decisions.
This is often sound like relating a business problem to an operation research problem.
The meaning of business analytics and uses of the operation research methods or
decision making including linear programming, inventory management, simulation and
Markov chains are explained here.
Business Analytics
Analytics are used to identify (i) what has happened? (ii) What should happen? And (iii) what will
happen? In the business.
These three forms of question are categorized into Descriptive, Prescriptive and Predictive
analytics respectively.
Business analytics is the study of data via statistical techniques, constructing predictive models,
implementing the optimizing rule and draw a valid inference according to the business needs.
Thus, business analytics uses a huge amount of data or simply big data to make a profitable
conclusion.
Benefits of Business Analytics
Business analytics is used to implement the data mining techniques such as classification,
regression analysis, clustering analysis, etc., and to understand the complex data using neural
networks, deep learning and machine learning techniques.
Business analytics is used to do quantitative statistical analysis or solving a mathematical model to
deliver justifications for the occurrence of the problem.
It can be used as a supporting tool for conducting any multivariate testing and A/B testing to find
the relationship or test the relationship with past decisions.
It can be used for predictive modelling to improve business standards.
Goals of Business Analytics
The main goal of business analytics is to identify which dataset will be useful and
how it can be taken forward to solve the business problems and increase the profit,
productivity, and efficiency.
In recent years, business analytics in operational practice has become a great interest
among researchers.
With the growth of technologies, and with the large amount of data at hand, it is
important to make use of analytics and the operation research approach to solve many
complex business problems.
Markov chain Example
Consider a bank which deals with both asset and liability products, and it is obvious that loans
taken from the bank play a vital role in the revenue.
The bad loans and the paid-up loans are the absorbing nodes or the end state in a Markov chain.
The absorbing node is that it has no transition probability to any other nodes.
So, as a statistical consultant, the first step is to understand the trends in the loan cycle with the
previous study.
Contd..
Figure 1. Markov Chain
for pattern of loans
Next step is to calculate the transition probability matrix with the previous probability.
Estimate the number of loan which belongs to each category.
From the diagram, it is clear that 60% has good loans, and 40% has bad loans.
Thus, the calculation becomes,
Contd..
From the final output, it is expected that 15% of the loans are going to be paid-up loans for the
current year and 16% becomes a bad loan.
The retail industry can develop their business insights to decrease the percentage of bad loans
in the future.
In addition, if you want to predict the same for 2 years, it is calculated as
Contd..
Similarly, the process is repeated until the convergence is achieved. That is,
From the convergence result, it is identified that 54% of the present loan will be paid fully, and 46%
will be a bad loan.
Contd..
If you want to identify the proportion of good loans becoming a paid loan, then you should start
with 100% of good loans and others as 0% in the initial stage and repeat the process until
convergence is achieved.
From the results, it is identified as only 23% becomes a bad loan whereas in the previous case it
was recorded as 46%.
Conclusion
Operational Analytics or business analytics involves building a suitable
model or developing a predictive model to make meaningful business
decisions.
It may be a transportation model, or the Markov model, or the Linear
programming model or a simulation model; the objective is to satisfy the
business needs and do a profitable business.
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