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1. Demand
Forecasting

2. An Introduction
 Broadly speaking, there are two approaches to demand
forecasting- one is to obtain information about the
likely purchase behavior of the buyer through
collecting expert’s opinion or by conducting interviews
with consumers, the other is to use past experience as a
guide through a set of statistical techniques. Both
these methods rely on varying degrees of judgment.
The first method is usually found suitable for short-
term forecasting, the latter for long-term forecasting.
There are specific techniques which fall under each of
these broad methods.

4. Simple Survey Method:
 For forecasting the demand for existing product, such survey
methods are often employed. In this set of methods, we may
undertake the following exercise.
1) Experts Opinion Poll: In this method, the experts on the
particular product whose demand is under study are
requested to give their ‘opinion’ or ‘feel’ about the product.
These experts, dealing in the same or similar product, are able
to predict the likely sales of a given product in future periods
under different conditions based on their experience. If the
number of such experts is large and their experience-based
reactions are different, then an average-simple or weighted –is
found to lead to unique forecasts. Sometimes this method is
also called the ‘hunch method’ but it replaces analysis by
opinions and it can thus turn out to be highly subjective in
nature.

5. 2) Reasoned Opinion-Delphi Technique:
This is a variant of the opinion poll method. Here is an attempt to
arrive at a consensus in an uncertain area by questioning a group of
experts repeatedly until the responses appear to converge along a single
line. The participants are supplied with responses to previous
questions (including seasonings from others in the group by a
coordinator or a leader or operator of some sort). Such feedback may
result in an expert revising his earlier opinion. This may lead to a
narrowing down of the divergent views (of the experts) expressed
earlier. The Delphi Techniques, followed by the Greeks earlier, thus
generates “reasoned opinion” in place of “unstructured opinion”; but
this is still a poor proxy for market behavior of economic variables.
3) Consumers Survey- Complete Enumeration Method: Under this,
the forecaster undertakes a complete survey of all consumers whose
demand he intends to forecast, Once this information is collected, the
sales forecasts are obtained by simply adding the probable demands of
all consumers. The principle merit of this method is that the forecaster
does not introduce any bias or value judgment of his own. He simply
records the data and aggregates. But it is a very tedious and
cumbersome process; it is not feasible where a large number of
consumers are involved.

6. 4) Consumer Survey-Sample Survey Method: Under this
method, the forecaster selects a few consuming units out of the
relevant population and then collects data on their probable
demands for the product during the forecast period. The total
demand of sample units is finally blown up to generate the total
demand forecast. Compared to the former survey, this method is
less tedious and less costly, and subject to less data error; but the
choice of sample is very critical. If the sample is properly chosen,
then it will yield dependable results; otherwise there may be
sampling error. The sampling error can decrease with every
increase in sample size
5) End-user Method of Consumers Survey: Under this method,
the sales of a product are projected through a survey of its end-
users. A product is used for final consumption or as an
intermediate product in the production of other goods in the
domestic market, or it may be exported as well as imported. The
demands for final consumption and exports net of imports are
estimated through some other forecasting method, and its
demand for intermediate use is estimated through a survey of its
user industries.

7.  Complex Statistical Methods:
 We shall now move from simple to complex set of methods of
demand forecasting. Such methods are taken usually from
statistics. As such, you may be quite familiar with some the
statistical tools and techniques, as a part of quantitative methods
for business decisions.
 (1) Time series analysis or trend method: Under this method,
the time series data on the under forecast are used to fit a trend
line or curve either graphically or through statistical method of
Least Squares. The trend line is worked out by fitting a trend
equation to time series data with the aid of an estimation
method. The trend equation could take either a linear or any
kind of non-linear form. The trend method outlined above often
yields a dependable forecast. The advantage in this method is
that it does not require the formal knowledge of economic
theory and the market, it only needs the time series data.

8.  (2) Barometric Techniques or Lead-Lag indicators
method: This consists in discovering a set of series of some
variables which exhibit a close association in their
movement over a period or time.
 Eg. : It shows the movement of agricultural income (AY
series) and the sale of tractors (ST series). The movement
of AY is similar to that of ST, but the movement in ST takes
place after a year’s time lag compared to the movement in
AY. Thus if one knows the direction of the movement in
agriculture income (AY), one can predict the direction of
movement of tractors’ sale (ST) for the next year. Thus
agricultural income (AY) may be used as a barometer (a
leading indicator) to help the short-term forecast for the
sale of tractors.

9.  3) Correlation and Regression: These involve the use of
econometric methods to determine the nature and degree of
association between/among a set of variables. Econometrics, you
may recall, is the use of economic theory, statistical analysis and
mathematical functions to determine the relationship between a
dependent variable (say, sales) and one or more independent
variables (like price, income, advertisement etc.). The
relationship may be expressed in the form of a demand function,
as we have seen earlier. Such relationships, based on past data
can be used for forecasting. The analysis can be carried with
varying degrees of complexity. Here we shall not get into the
methods of finding out ‘correlation coefficient’ or ‘regression
equation’; you must have covered those statistical techniques as a
part of quantitative methods. Similarly, we shall not go into the
question of economic theory. We shall concentrate simply on the
use of these econometric techniques in forecasting.
 The form of the equation may be:
 DX = a + b1 A + b2PX + b3Py

10.  (4) Simultaneous Equations Method: Here is a very
sophisticated method of forecasting. It is also known
as the ‘complete system approach’ or ‘econometric
model building’. In your earlier units, we have made
reference to such econometric models. Presently we do
not intend to get into the details of this method
because it is a subject by itself. Moreover, this method
is normally used in macro-level forecasting for the
economy as a whole; in this course, our focus is limited
to micro elements only. Of course, you, as corporate
managers, should know the basic elements in such an
approach.

11. Objectives of Demand Forecasting
 Short run forecasting
Intermediate forecasting
 Long run forecasting

12. Need of Demand Forecasting
1 . Helps in Production Planning
2. Helps in Financial Planning
3. Helps in Economic Planning
4. Helps in Workforce Scheduling
5. Helps in Decisions Making
6. Helps in Controlling Business Cycles

13. PREASENTED BY
 Navdeep Singh Dakra
 MBA (Finance)
 Session : 2012’14