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.
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.
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. Moreover if the data are wrongly recorded,
this method will be totally useless.
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.
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. The only limitation in this method is that it assumes
that the past is repeated in future. Also, it is an appropriate method
for long-run forecasts, but inappropriate for short-run forecasts.
Sometimes the time series analysis may not reveal a significant trend of
any kind. In that case, the moving average method or exponentially
weighted moving average method is used to smoothen the series.
(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.
For example, 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.
Generally, this barometric method has
been used in some of the developed countries for predicting business
cycles situation. For this purpose, some countries construct what are
known as ‘diffusion indices’ by combining the movement of a number of
leading series in the economy so that turning points in business
activity could be discovered well in advance. Some of the limitations of
this method may be noted however. The leading indicator method does not
tell you anything about the magnitude of the change that can be
expected in the lagging series, but only the direction of change. Also,
the lead period itself may change overtime. Through our estimation we
may find out the best-fitted lag period on the past data, but the same
may not be true for the future. Finally, it may not be always possible
to find out the leading, lagging or coincident indicators of the
variable for which a demand forecast is being attempted.
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.
We are on the realm of multiple regression and multiple correlation. The form of the equation may be:
DX = a + b1 A + b2PX + b3Py
You know that the regression coefficients b1, b2, b3 and b4 are the components of relevant elasticity of demand. For example, b1 is
a component of price elasticity of demand. The reflect the direction as
well as proportion of change in demand for x as a result of a change in
any of its explanatory variables. For example, b2< 0 suggest that DX and PX are inversely related; b4 > 0 suggest that x and y are substitutes; b3 > 0 suggest that x is a normal commodity with commodity with positive income-effect.
Given the estimated value of and bi, you may forecast the expected sales (DX), if you know the future values of explanatory variables like own price (PX), related price (Py),
income (B) and advertisement (A). Lastly, you may also recall that the
statistics R2 (Co-efficient of determination) gives the measure of
goodness of fit. The closer it is to unity, the better is the fit, and
that way you get a more reliable forecast.
The principle advantage of this method
is that it is prescriptive as well descriptive. That is, besides
generating demand forecast, it explains why the demand is what it is. In
other words, this technique has got both explanatory and predictive
value. The regression method is neither mechanistic like the trend
method nor subjective like the opinion poll method. In this method of
forecasting, you may use not only time-series data but also cross
section data. The only precaution you need to take is that data analysis
should be based on the logic of economic theory.
(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.
The method is indeed very complicated.
However, in the days of computer, when package programmes are available,
this method can be used easily to derive meaningful forecasts. The
principle advantage in this method is that the forecaster needs to
estimate the future values of only the exogenous variables unlike the
regression method where he has to predict the future values of all,
endogenous and exogenous variables affecting the variable under
forecast. The values of exogenous variables are easier to predict than
those of the endogenous variables. However, such econometric models have
limitations, similar to that of regression method.
