Demand Forecasting:

 On the basis of analysis and interpretation of information gathered
about various aspects of market and demand from primary and secondary sources, an attempt
is made to forecast the future demand of the proposed product or service. There are various
methods of demand forecasting available to the market analyst.

Methods of Demand Analysis

The various methods of forecasting demand may be grouped under the following categories:

1) Opinion Polling Method:

In this method, the opinion of the buyers, sales force and experts
could be gathered to determine the emerging trend in the market. The opinion polling
methods of demand forecasting are of three kinds:

i) Consumers Survey Methods: The most direct method of forecasting demand in the short-
run is survey method. Surveys are conducted to collect information about future purchase
plans of the probable buyers of the product. Survey methods include:

a) Complete Enumeration Survey:

Under the Complete Enumeration Survey, the firm has to
go for a door to door survey for the forecast period by contacting all the households in the
area.
b) Sample Survey and Test Marketing: Under this method some representative households
are selected on random basis as samples and their opinion is taken as the generalized opinion.
This method on random basis as samples and their opinion is taken as the generalized
opinion. This method is based on the basic assumption that the sample truly represents the
population. A variant of sample survey technique is test marketing. Product testing essentially
involves placing the product with a number of users for a set period. Their reactions to the
product are noted after a period of time and an estimate of likely demand is mad from the
result.
c) End–use Method: In this method, the sale of the product under consideration is projecting
on the basis of demand survey of the industries using this product and intermediate product.
In other words, demand for the final product is the end use demand of the intermediate
product used in the production of this final product.

ii) Sales Force Opinion Method:

This is also known as Collective Opinion Method. In this
method, instead of consumers, the opinion of the salesman is sought. It is sometimes referred
as the “grass roots approach” as it is a bottom-up method that requires each sales person in
the company to make an individual forecast for his or her particular sales territory. These
individual forecasts are discussed and agreed with the sales manager. The composite of all
forecasts then constitutes the sales forecast for the organization.

iii) Delphi Method:

This method is also known as Expert opinion method of investigation. In
this method instead of depending upon the opinions of buyers and salesmen, firms can obtain
views of the specialists or experts in their respective fields. Opinions of different experts are
sought and their identity is kept secret. These opinions are than exchanged among the various
experts and their reactions are sought and analysed. The process goes on until some sort of

unanimity is arrived at among all the experts. This method is best suited in circumstances
where intractable changes are occurring.

2) Statistical or Analytical Methods: Statistical methods are considered to be superior

techniques of demand estimation because:
i) The element of subjectivity in this method is minimum,
ii) Method of estimation is scientific,
iii) Estimation is based on the theoretical relationship between the dependents and
independents variables,
iv) Estimates are relatively more reliable and
v) Estimation involves smaller cost.
The statistical methods, which are frequently used, for making demand projections are:

i) Thread Projection Method:

An old firm can use its data of past years regarding its sales
in past years. These data are known as time series of sales. A trend line can be fitted by
graphic method or by algebraic equations. Equations method is more appropriate. The trend
can be estimated by using any one of the following methods.
a) Graphical Method: A trend line can be fitted through a series graphically. Old values of
sales for different areas are plotted on a graph and a free hand curve is drawn passing through
as many points as possible. The direction of this free hand curve shows the trend. The main
drawback of this method is that it may show the trend but not measure it.
b) Least Square Method: The least square method is based on the assumption that the past
rate of change of the variable under study will continue in the future. It is a mathematical
procedure for fitting a line to a set of observed data points in such a manner that the sum of
the squared difference between the calculated and observed value is minimized. This
technique is used to find a trend line which best fit the available data. The trend is then used
to project department variable in the future. This method is very popular because it is simple
and in expensive.
c) Time Series Methods: Time series forecasting methods are based on analysis of historical
data (time series; a set of observations measured at successive times or over successive
periods). They make the assumption that past patterns in data can be used to forecast future
data points. Moving averages (simple moving average, weighed moving average); forecast is
based on arithmetic average of a given number of past data points.

Components of Time series Demand
• Average: The mean of the observations over time.

• Trend: A gradual increase or decrease in the average over time.
• Seasonal Influence: Predictable short-term cycling behavior due to time of day, week,
month, season, year, etc.
• Cyclical Movement: Unpredictable long-term cycling behavior due to business cycle or
product/service life cycle.
• Random Error: Remaining variation that cannot be explained by the other four components.
Exponential Smoothing: It is one of the methods of trend projection methods.
Exponential smoothing is distinguishable by the special way it weights ach past demand. The
pattern of weights is exponential in form. Demand for the most recent period is weighted
most heavily; the weights placed on successively older periods decrease exponentially. In
other words, the weights decrease in magnitude the future back in time the data are weighted;
the decrease is non-linear (exponential).

ii). Regression method:

This is a very common method of forecasting demand. Under this
method a relationship is established between quantities demanded (dependent variable) and
independent variables such as income, price of the good, prices of the related goods etc. Once
the relationship is established, we drive regression equation assuming relationship between
dependent and independent variables. Once the regression equation is derived the value of Y
i.e. quantity demanded can be estimated for any given value of X.

iii). Simultaneous equations Methods of Forecasting:

 The econometric model forecasting involves estimating several simultaneous equations,
which are, generally, behavioural equations, mathematical identities and market-clearing
equations. The econometric model technique is also known as simultaneous equations
method and complete system approach to forecasting. This technique uses sophisticated
mathematical and statistical tools.

iv). Barometric Method:

 It is also known as ‘leading indicators forecasting’. National bureau of Economic Research
of U.S.A. has identified three types of indicators, coincidental indicators and Lagging
indicators. The analyst should establish relationship between the sales of the product and the
economic indicators to project the correct sales and to measure to what extent these indicators

affect the sales. To establish relationship is not easy task especially in case of new product
where there is no past record.