Introduction to Business Analytics and Operational Research Solution
Methods, Including Decision Analysis, Linear Programming, Inventory
Control, Simulation and Markov Chains
Dr. Nancy Agens, Head,
Technical Operations, Statswork
info@statswork.com
I. INTRODUCTION
In modern years, 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. However, there is often a question
arise in connecting the business analytics to
the operation research problem. In this blog,
I will explain you the meaning of business
analytics and how it is related and useful in
the operation research methods or decision
making including linear programming,
inventory management, simulation and
II. MARKOV CHAINS
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.
However, 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.
There is a different approach to business
analytics, which in turn delivers profitable
benefits (Budnick et al., 1994). I will list out
a few uses of business analytics for the
betterment of the business.
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
If a business company wants to identify
the pattern of the sales of a product or to
find a new pattern to promote the growth
of the business, then 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.
Apart from the benefits and uses 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. So far, I
explained to you about the meaning and
benefits of business analytics. However, 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 (Choi et al., 2017; Hillier &
Lieberman, 2015). Thus, in the coming
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years, business analytics tools are the most
powerful tool to take the business standard
to the next level. Now, let look at how a
simple Markov chain is used to solve a
business problem.
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. Hence, the bank
executive decided to hire a consultant to find
whether they end up in good loans, risky
loans, paid-up loans or bad loans.
In this example, 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. Let's say; the following
Markov chain represents the pattern of loans
for the previous year
Fig 1. Markov Chain for pattern of loans
From the above transition diagram, it
is clear that the bad loans and paid-up loans
are the absorbing states; that is, the process
end and stays in these states forever.
Otherwise, paid-up loans cannot be a bad
loan or risky or good and similarly, the bad
loans cannot be a paid-up or risky or good.
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Next step is to calculate the
transition probability matrix with the
previous probability. That, it with the
previous probability, estimate the number of
loans belongs to each category. From the
diagram, it is clear that 60% has good loans,
and 40% has bad loans. Thus, the calculation
becomes,
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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. Thus, from this Markov chain
example, 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, then with the same transition matrix,
it is calculated as
Similarly, the process is repeated until the convergence is achieved. That is,
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From the convergence result, it is identified
that 54% of the present loan will be paid
fully, and 46% will be a bad loan. This is
useful in identifying the risk of banks in
issuing loans to customers.
Suppose, 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%.
Similarly, if you are interested in identifying
the proportion of risk loans ending as paidup or bad loans then assign 100%
probability to risk loans and others with 0%
probability the do the process until
convergence and deliver a valid conclusion.
The previous case deals with the
Markov process into business insights.
However, there is still a question persists
where the analytics relate to operation
research? An operation research scientist is
everywhere in the process and few having
developed these kinds of tools to solve a
business problem and few have developed a
robust model for the same. In practice,
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.
III. SUMMARY
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I presented an informal description
of business analytics and Operations
Research in this blog with an application to
a retail bank industry using Markov chains. I
personally feel that if I want to understand
anything, it is better to dig deeper into the
topic and go for details.
REFERENCES
[1] Budnick, F.S., McLeavey, D.W. & Mojena, R. (1994).
Principles Of Operations Research For Management
(2nd Edition). Irwin series in quantitative anlysis for
business. [Online]. A.I.T.B.S. Publishers. Available
from:
https://books.google.co.in/books?id=wBMVYAAA
CAAJ.
[2] Choi, T.-M., Chan, H.K. & Yue, X. (2017). Recent
Development in Big Data Analytics for Business
Operations
and
Risk
Management.
IEEE
Transactions on Cybernetics. [Online]. 47 (1). pp.
81–92.
Available
from:
http://ieeexplore.ieee.org/document/7378465/.
[3] Hillier & Lieberman, J. (2015). Introduction to
Operations Research. [Online]. Available from:
https://notendur.hi.is/kth93/3.20.pdf.
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