Abstract:
"
In the competitive business world demand becomes a vital driver for business. Demand acts as a
signal which drives all business decisions. With the increase in importance of demand for business
decisions, forecasting demand and identifying demand determinates plays a crucial role in
business. Demand in layman terms is the willingness of consumers to purchase the product. This
has been explained by different economist in different ways and have pointed out different factors
which effect the purchasing decision. It is vital for business to predict the demand accurately as
this will initiate all other business processes like ordering raw material, manufacturing, arranging
storage, arranging distribution, route allocation etc. It is utmost essential for the company to
identify the determinants which impact it’s product in order to identify the demand drivers and
cater to the market appropriately.
The study has followed an eclectic approach while not being restricted to a particular theory. The
study is based on the sample period from 2013 to 2020 and uses monthly data. Further 2021 1
st 3
months data will be used to compare the actual results against the model output results. The
Exponential Smoothing Model and the ARIMA forecasting model has been used to arrive at a
suitable predictive model while using 70% of data as training and 30% as testing data. It is also
identified that the Time series regression model the best fit."
