Introduction To Business Analytics And Operational Research Solution Methods, Including Decision Analysis, Linear Programming, Inventory Control, Simulation And Markov Chains – Statswork
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 that arises
in connecting the business analytics to the operation research problem. In this
blog, I will explain to 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 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.
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.
Consider a bank that 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 statistical consultant to
find whether they end up in good loans, risky loans, paid-up loans or bad
loans. Consider a bank that 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.
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 paid-up or risky or good.
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.
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.
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