forecasting of demand
INTRODUCTION
A forecast is an estimate of an
event which will happen in future. The event may be demand of a product,
rainfall at a particular place, population of a particular country, or growth
of a technology. The forecast value is not deterministic quantity. Since, it is
an estimate based on past data related to a particular event, proper care must
be given in estimating it.
In any industrial enterprise,
forecasting is the first level decision activity that is the demand of a
particular product must be available before taking any other decision problems
like material planning, scheduling, type of production system (mass or batch
production ) to be implemented, etc.
DEMAND FORECASTING
The demand forecast gives the
expected level of demand for goods or services. This is the basic input for
business planning and control. Hence, decisions for all the functions of any
corporate house are influenced by demand forecast.
In simple words, forecast means
prediction of future events with certain amount of accuracy. Such predictions
are rarely perfect regardless of the quantity of historical data and the extent
of managerial experience. Problems before a manufacturing organizations are:
a. What to
produce?
b. When to
produce?
c. At what
cost to produce?
d. Where
to produce?
Thus, by estimating or
predicting the sales gives the base for the quantity of production. Therefore,
forecasting means, demand forecasting and indirectly the sales. Thus we can say
demand forecast refers to the prediction or estimation of future situation
under given constrain.
DEFINITION
In the words of Garfield,
“Demand forecasts are first
approximations in production planning. Demand forecast is an estimate of sales
in monetary or physical units for a specific future period under a proposed
plan”.
TYPES OF DEMAND
FORECASTING
1.
Based
on duration
a.
Short term demand forecasting: Short
term demand forecasting is concerned with short time period, usually less than
one year. This is required for current production scheduling, purchase of raw
material and inventory of stock, etc. In a short run
forecast, seasonal patterns are of much importance. It may cover a period of
three months, six months or one year. It is one which provides information for
tactical decisions.
b.
Medium term demand forecasting: Medium term demand forecasting usually
concerned with time period varying between, say two to five years.
c.
Long term demand forecasting: Long
term forecasting of demand is needed for capacity expansion i.e. growth of the
firm, recruitment and diversification policies, for all these decisions have
long run implication.
2.
Based
on nature
According to nature the forecast
may be general or specific. Though the general forecast is useful for the firm.
It will be more helpful if the general forecast is broken down into specific
forecast with respect to commodities, sales area, etc.
3.
Based
on products
On the basis of product,
forecast may be termed as capital and consumer durable and non durable products
according to nature of product. Capital goods are the goods which are used for
further production. It includes machinery, equipments, etc. the demand for
capital goods is derived demand.
4.
Based
on level
According to level, forecast
can be micro or macro. Micro is concerned with industry or company like
industrial products. Forecasting at national or international level is
macroeconomic forecasting.
5.
Based
on peculiarities
In every forecast peculiar
factors such as competition, sociological consideration, cultural factors and
fashions are relevant.
6.
Based
on status of the product
For the established products,
past sales trends and the competitive conditions are known while this is not so
in case of new product.
CLASSIFICATION
OF FORECASTING
1.
Active
forecasting
Under active forecast, prediction is done under the condition of
likely future changes in the actions by the firms.
2.
Passive forecasting
Under passive forecast prediction about future is based on the
assumption that the firm does not change the course of its
action.
FACTORS AFFECTING
DEMAND FORECASTING
Many factors influence demand at any
given time. These factors are internal and external.
1. External factors
External factors are those over which
management has no control especially the general state of economy. Boom economy
may positively influence the demand but the effect may not be same for all
products. Some external factors affecting demand are:
a.
General state of the economy
b.
Government actions
c.
Consumer tastes
d.
Public image of the product
e.
Competitor actions
2. Internal factors
Internal decisions can affect the
demand for products. Recognition by management that these decisions can be
controlled encourages the management to respond actively.
Some internal factors affecting
demand are:
a.
Product design
b.
Price and advertisement programs
c.
Packaging design
d.
Sales person quotas and incentives
e.
Product mix
OBJECTIVES OF DEMAND FORECASTING
1. Short term objectives
The following are the objectives of short term demand
forecasting.
·
To evolve a suitable production policy: Short
term forecasts help the firm to plan the production so as to avoid the problems
of over production and short supply.
·
To plan the purchase of raw materials: The
firm's can plan the purchase of raw materials at appropriate time to reduce the
cost and control inventories.
·
To plan short term financial
requirements: The firms require not only short term funds for
purchase of raw materials and payment of wages, but also medium term funds for
replacement and renewal to maintain productive efficiency.
·
To determine appropriate price policy: Short
run forecasting helps the firm to evolve a suitable price policy depending upon
the expected market conditions to maintain consistent sales.
·
To fix sales targets: Realistic
sales targets can be fixed for the salesmen on the basis of short term demand
forecasting. If the targets are too high the salesmen may fail to achieve them
and they will get discouraged. If the targets are too low the salesmen will
achieve the targets so easily that the incentives will prove meaningless.
2. Long term objectives
The following are the objectives of long term demand forecasting.
·
To plan the establishment of a new unit or
expansion of an existing unit: Planning of a new unit or
expansion of an existing unit requires an analysis of the long term demand
potential of the products. The competitive strength of the firm will be greater
if it has better knowledge than the rivals of the growth trends in the economy.
·
To plan long term financial
requirements: Long run forecasts are essential to assess long
term financial requirements. When the funds required for expansion,
modernization and diversification are large, it takes time to make necessary
arrangements for raising sufficient resources through long term loans and the
issue of shares and debentures.
·
To plan manpower requirements: Long
term demand forecasting is useful for manpower planning. Training and personnel
development can be started well in advance on the basis of estimates of
manpower requirements assessed according to long term demand forecasts.
FORECASTING FOR
DIFFERENT TYPES OF PRODUCTS
There
are different forecasts for different types of products like:
(i) Forecasting demand for nondurable
consumer goods,
(ii) Forecasting demand for durable
consumer goods,
(iii) Forecasting demand for capital
goods, and
(iv) Forecasting demand for
new-products.
Non-Durable Consumer Goods
These are also known as ‘single-use
consumer goods’ or perishable consumer goods. These vanish after a single act
of consumption. These include goods like food, milk, medicine, fruits, etc. Demand
for these goods depends upon household disposable income, price of the
commodity and the related goods and population and characteristics.
Symbolically,
Dc =f(y, s, p, pr) where
Dc = the demand for commodity с
у = the household disposable income
s = population
p = price of the commodity с
pr = price of its related goods
(i) Disposable income expressed as Dc
= f (y) i.e. other things being equal, the demand for commodity с depends upon
the disposable income of the household. Disposable income of the household is
estimated after the deduction of personal taxes from the personal income.
Disposable income gives an idea about the purchasing power of the household.
(ii) Price,
expressed as Dc = f (p, pr) i.e. other
things being equal, demand for commodity с depends upon its own price and the
price of related goods. While the demand for a commodity is inversely related
to its own price of its complements. It is positively related to its
substitutes.’ Price elasticities and cross elasticities of non-durable consumer
goods help in their demand forecasting.
(iii) Population, expressed as Dc= f
(5) i.e. other things being equal, demand for commodity с depends upon the size
of population and its composition. Besides, population can also be classified
on the basis of sex, income, literacy and social status. Demand for non-durable
consumer goods is influenced by all these factors. For the general demand
forecasting population as a whole is considered, but for specific demand
forecasting division of population according to different characteristics
proves to be more useful.
Durable Consumer Goods
These goods can be consumed a number
of times or repeatedly used without much loss to their utility. These include
goods like car, T.V., air-conditioners, furniture etc. After their long use,
consumers have a choice either these could be consumed in future or could be
disposed of.
The
choice depends upon the following factors:
(i) Whether a consumer will go for
the replacement of a durable good or keep on using it after necessary repairs
depends upon his social status, level of money income, taste and fashion, etc.
Replacement demand tends to grow with increase in the stock of the commodity
with the consumers. The firm can estimate the average replacement cost with the
help of life expectancy table.
(ii) Most
consumer durables are consumed in common by the members of a family. For
instance, T.V., refrigerator, etc. are used in common by households. Demand
forecasts for goods commonly used should take into account the number of households
rather than the total size of population. While estimating the number of
households, the income of the household, the number of children and sex-
composition, etc. should be taken into account.
(iii) Demand
for consumer durables depends upon the availability of allied facilities. For
example, the use of T.V., refrigerator needs regular supply of power, the use
of car needs availability of fuel, etc. While forecasting demand for consumer
durables, the provision of allied services and their cost should also be taken
into account.
(iv) Demand for consumer durables is
very much influenced by their prices and their credit facilities. Consumer
durables are very much sensitive to price changes. A small fall in their price
may bring large increase in demand.
Forecasting Demand for Capital Goods
Capital goods are used for further
production. The demand for capital good is a derived one. It will depend upon
the profitability of industries. The demand for capital goods is a case of
derived demand. In the case of particular capital goods, demand will depend on
the specific markets they serve and the end uses for which they are bought.
The demand for textile machinery
will, for instance, be determined by the expansion of textile industry in terms
of new units and replacement of existing machinery. Estimation of new demand as
well as replacement demand is thus necessary.
Three
types of data are required in estimating the demand for capital goods:
(a) The growth prospects of the user
industries must be known,
(b) The norm of consumption of the
capital goods per unit of each end-use product must be known, and
(c) The velocity of their use.
Forecasting Demand for New Products
The methods of forecasting demand for
new products are in many ways different from those for established products.
Since the product is new to the consumers, an intensive study of the product
and its likely impact upon other products of the same group provides a key to
an intelligent projection of demand.
Joel
Dean has classified a number of possible approaches as follows:
(a) Evolutionary Approach:
It consists of projecting the demand
for a new product as an outgrowth and evolution of an existing old product.
(b) Substitute Approach:
According to this approach the new
product is treated as a substitute for the existing product or service.
(c) Growth Curve Approach:
It estimates the rate of growth and
potential demand for the new product as the basis of some growth pattern of an
established product.
(d) Opinion-Poll Approach:
Under this approach the demand is
estimated by direct enquiries from the ultimate consumers.
(e) Sales Experience Approach:
According to this method the demand
for the new product is estimated by offering the new product for sale in a
sample market.
(f)
Vicarious Approach:
By this method, the consumers’
reactions for a new product are found out indirectly through the specialized
dealers who are able to judge the consumers’ needs, tastes and preferences.
The various steps involved in
forecasting the demand for non-durable consumer goods are the following:
(a) First identify the variables
affecting the demand for the product and express them in appropriate forms, (b)
gather relevant data or approximation to relevant data to represent the
variables, and (c) use methods of statistical analysis to determine the most
probable relationship between the dependent and independent variables.
IMPORTANCE OF DEMAND FORECASTING
1. Distribution of
resources: We know that inputs are processed to result into output. These
inputs include resources like materials, machinery and of course human
resources. The business firm also has to take decisions regarding capital
arrangement, manpower planning and so on. These all could be done with a bit of
ease if the firm has idea about the demand for its product. In short the
estimation of demand enables the firm to undertake critical business decisions.
2. Helps in
avoiding wastages of resources: Demand forecasting is not an
option but compulsion in today’s competitive environment. Imagine a firm that
does not undertake demand forecasting. As a result it will have no clue as to
where its product stands in the market and how is the future demand for the
same. This may result in wastage of resources. So in order to avoid wastages it
is always beneficial to have a sense of future demand for the products and
services.
3. Serves as a
direction to production: The production process is not confirmed to producing
goods and services. Producers need to ensure that there is continuous supply of
goods and services in the market. If there is proper prediction of the demand,
then it serves as a handy tool for the businesses to undertake future
production activities. This is but obvious because if there is strong demand
expected in the future then the firm can take steps accordingly. Also if the
firm sees lack of demand in the coming times, then too decisions regarding the
future production could be taken accordingly.
4. Pricing: The decision
regarding pricing of the goods and services is perhaps one of the most critical
business decisions. Demand forecasting is useful in this area too. If there are
sincere predictions about the future sales of the firm’s product then it could
serve as a good aid to devise pricing strategies.
5. Helps in
devising sales policy: Production is followed by sales. Demand forecasting
is nothing but estimating the sales of the product. The business firms can plan
its sales policy effectively on the backdrop of demand forecasting. This also
implies that the distribution of goods and services can be done appropriately depending
upon the predictions of the demand for the product.
6. Decrease of
business risk: Where there is business there is risk. Demand forecasting though
does not completely remove the business uncertainties, helps in reducing the
risks and uncertainties to a certain extent.
7. Inventory
management: Inventories is one of those aspects which is closely
associated with demand. This is because inventories are kept by the producers
to meet the demand in the coming times. Demand forecasting helps in devising
appropriate inventory management policies
Thus we have
seen that whether it is distribution of resources, production, sales or
inventory, demand forecasting is useful in all these areas. The markets are
nothing but a play of demand and supply. Since demand is such an indispensable
part of the market, demand forecasting is bound to be of great help to the
producers.
QUALITIES OF GOOD DEMAND FORECASTING
A forecast
should have following qualities:
(i)
Accuracy:
The forecast obtained must be accurate. How is an accurate
forecast possible? To obtain an accurate forecast, it is essential to check the
accuracy of past forecasts against present performance and of present forecasts
against future performance. Accuracy cannot be tested by precise measurement
but buy judgment.
(ii) Plausibility:
The executive should have good
understanding of the technique chosen and they should have confidence in the
techniques used. Understanding is also needed for a proper interpretation of
results. Plausibility requirements can often improve the accuracy of results.
(iii) Durability:
Unfortunately, a demand function
fitted to past experience may back cost very greatly and still fall apart in a
short time as a forecaster. The durability of the forecasting power of a demand
function depends partly on the reasonableness and simplicity of functions
fitted, but primarily on the stability of the understanding relationships
measured in the past. Of course, the importance of durability determines the
allowable cost of the forecast.
(iv) Flexibility:
Flexibility can be viewed as an
alternative to generality. A long lasting function could be set up in terms of
basic natural forces and human motives. Even though fundamental, it would
nevertheless be hard to measure and thus not very useful. A set of variables
whose co-efficient could be adjusted from time to time to meet changing
conditions in more practical way to maintain intact the routine procedure of
forecasting.
(v) Availability:
Immediate availability of data is a
vital requirement and the search for reasonable approximations to relevance in
late data is a constant strain on the forecasters patience. The techniques
employed should be able to produce meaningful results quickly. Delay in result
will adversely affect the managerial decisions.
(vi) Economy:
Cost is a primary consideration which
should be weighted against the importance of the forecasts to the business
operations. A question may arise: How much money and managerial effort should
be allocated to obtain a high level of forecasting accuracy? The criterion here
is the economic consideration.
(vii) Simplicity:
Statistical and econometric models
are certainly useful but they are intolerably complex. To those executives who
have a fear of mathematics, these methods would appear to be Latin or Greek.
The procedure should, therefore, be simple and easy so that the management may
appreciate and understand why it has been adopted by the forecaster.
(viii) Consistency:
The forecaster has to deal with
various components which are independent. If he does not make an adjustment in
one component to bring it in line with a forecast of another, he would achieve
a whole which would appear consistent.
Forecasting Techniques
Demand forecasting is a difficult
exercise. Making estimates for future under the changing conditions is a
Herculean task. Consumers’ behaviour is the most unpredictable one because it
is motivated and influenced by a multiplicity of forces. There is no easy
method or a simple formula which enables the manager to predict the future.
Economists and statisticians have
developed several methods of demand forecasting. Each of these methods has its
relative advantages and disadvantages. Selection of the right method is
essential to make demand forecasting accurate. In demand forecasting, a
judicious combination of statistical skill and rational judgement is needed.
Mathematical and statistical
techniques are essential in classifying relationships and providing techniques
of analysis, but they are in no way an alternative for sound judgement. Sound
judgement is a prime requisite for good forecast.
The judgment should be based upon
facts and the personal bias of the forecaster should not prevail upon the
facts. Therefore, a mid way should be followed between mathematical techniques
and sound judgment or pure guess work.
The more commonly used methods
of demand forecasting are discussed below:
The various methods of demand
forecasting can be summarised in the form of a chart as shown in Table 1.
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:
(a) Consumer’s Survey Method or
Survey of Buyer’s Intentions:
In this method, the consumers are
directly approached to disclose their future purchase plans. I his is done by
interviewing all consumers or a selected group of consumers out of the relevant
population. This is the direct method of estimating demand in the short run.
Here the burden of forecasting is shifted to the buyer. The firm may go in for
complete enumeration or for sample surveys. If the commodity under
consideration is an intermediate product then the industries using it as an end
product are surveyed.
(i) 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. This method has an advantage of
first hand, unbiased information, yet it has its share of disadvantages also.
The major limitation of this method is that it requires lot of resources,
manpower and time.
In this method, consumers may be
reluctant to reveal their purchase plans due to personal privacy or commercial
secrecy. Moreover, at times the consumers may not express their opinion
properly or may deliberately misguide the investigators.
(ii) 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 generalised opinion. This method is based on the basic assumption that
the sample truly represents the population. If the sample is the true
representative, there is likely to be no significant difference in the results
obtained by the survey. Apart from that, this method is less tedious and less
costly.
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 made from the
result. These are suitable for new products or for radically modified old
products for which no prior data exists. It is a more scientific method of
estimating likely demand because it stimulates a national launch in a closely
defined geographical area.
(iii) End Use Method or
Input-Output Method:
This method is quite useful for
industries which are mainly producer’s goods. In this method, the sale of the
product under consideration is projected as the basis of demand survey of the
industries using this product as an intermediate product, that is, the demand
for the final product is the end user demand of the intermediate product used
in the production of this final product.
The end user demand estimation of an
intermediate product may involve many final good industries using this product
at home and abroad. It helps us to understand inter-industry’ relations. In
input-output accounting two matrices used are the transaction matrix and the
input co-efficient matrix. The major efforts required by this type are not in
its operation but in the collection and presentation of data.
(b) Sales Force Opinion Method:
This is also known as collective
opinion method. In this method, instead of consumers, the opinion of the
salesmen 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 organisation. The advantages of
this method are that it is easy and cheap. It does not involve any elaborate
statistical treatment. The main merit of this method lies in the collective
wisdom of salesmen. This method is more useful in forecasting sales of new
products.
(c) Experts Opinion Method:
This method is also known as “Delphi
Technique” of investigation. The Delphi method requires a panel of experts, who
are interrogated through a sequence of questionnaires in which the responses to
one questionnaire are used to produce the next questionnaire. Thus any
information available to some experts and not to others is passed on, enabling
all the experts to have access to all the information for forecasting.
The method is used for long term
forecasting to estimate potential sales for new products. This method presumes
two conditions: Firstly, the panellists must be rich in their expertise,
possess wide range of knowledge and experience. Secondly, its conductors are
objective in their job. This method has some exclusive advantages of saving
time and other resources.
2. Statistical Method:
Statistical methods have proved to be
immensely useful in demand forecasting. In order to maintain objectivity, that
is, by consideration of all implications and viewing the problem from an
external point of view, the statistical methods are used.
The
important statistical methods are:
(i) Trend Projection Method:
A firm existing for a long time will
have its own data regarding sales for past years. Such data when arranged
chronologically yield what is referred to as ‘time series’. Time series shows
the past sales with effective demand for a particular product under normal
conditions. Such data can be given in a tabular or graphic form for further
analysis. This is the most popular method among business firms, partly because
it is simple and inexpensive and partly because time series data often exhibit
a persistent growth trend.
Time series has got four types of
components namely, Secular Trend (T), Secular Variation (S), Cyclical Element
(C), and an Irregular or Random Variation (I). These elements are expressed by
the equation O = TSCI. Secular trend refers to the long run changes that occur
as a result of general tendency.
Seasonal variations refer to changes
in the short run weather pattern or social habits. Cyclical variations refer to
the changes that occur in industry during depression and boom. Random variation
refers to the factors which are generally able such as wars, strikes, flood,
famine and so on.
When a forecast is made the seasonal,
cyclical and random variations are removed from the observed data. Thus only the
secular trend is left. This trend is then projected. Trend projection fits a
trend line to a mathematical equation.
The trend can be estimated by
using any one of the following methods:
(a) The Graphical Method,
(b) The Least Square Method.
a) Graphical Method:
This is the most simple technique to
determine the trend. All values of output or sale for different years are
plotted on a graph and a smooth free hand curve is drawn passing through as
many points as possible. The direction of this free hand curve—upward or
downward— shows the trend. A simple illustration of this method is given in
Table 2.
Table 2: Sales of Firm
|
Year
|
Sales (Rs. Crore)
|
|
1995
|
40
|
|
1996
|
50
|
|
1997
|
44
|
|
1998
|
60
|
|
1999
|
54
|
|
2000
|
62
|
In Fig. 1, AB is the trend line which
has been drawn as free hand curve passing through the various points
representing actual sale values.
(b) Least Square Method:
Under the least square method, a
trend line can be fitted to the time series data with the help of statistical
techniques such as least square regression. When the trend in sales over time
is given by straight line, the equation of this line is of the form: y = a +
bx. Where ‘a’ is the intercept and ‘b’ shows the impact of the independent
variable. We have two variables—the independent variable x and the dependent
variable y. The line of best fit establishes a kind of mathematical
relationship between the two variables .v and y. This is expressed by the
regression у on x.
In order to solve the equation
v = a + bx, we have to make use of the following normal equations:
Σ y = na + b ΣX
Σ xy =a Σ x+b Σ x2
(ii) Barometric Technique:
A barometer is an instrument of
measuring change. This method is based on the notion that “the future can be
predicted from certain happenings in the present.” In other words, barometric
techniques are based on the idea that certain events of the present can be used
to predict the directions of change in the future. This is accomplished by the
use of economic and statistical indicators which serve as barometers of economic
change.
Generally
forecasters correlate a firm’s sales with three series: Leading Series,
Coincident or Concurrent Series and Lagging Series:
(a) The Leading Series:
The leading series comprise those
factors which move up or down before the recession or recovery starts. They
tend to reflect future market changes. For example, baby powder sales can be
forecasted by examining the birth rate pattern five years earlier, because
there is a correlation between the baby powder sales and children of five years
of age and since baby powder sales today are correlated with birth rate five
years earlier, it is called lagged correlation. Thus we can say that births
lead to baby soaps sales.
(b) Coincident or Concurrent
Series:
The coincident or concurrent series
are those which move up or down simultaneously with the level of the economy.
They are used in confirming or refuting the validity of the leading indicator
used a few months afterwards. Common examples of coinciding indicators are
G.N.P itself, industrial production, trading and the retail sector.
(c) The Lagging Series:
The lagging series are those which
take place after some time lag with respect to the business cycle. Examples of
lagging series are, labour cost per unit of the manufacturing output, loans
outstanding, leading rate of short term loans, etc.
(iii) Regression Analysis:
It attempts to assess the
relationship between at least two variables (one or more independent and one
dependent), the purpose being to predict the value of the dependent variable from
the specific value of the independent variable. The basis of this prediction
generally is historical data. This method starts from the assumption that a
basic relationship exists between two variables. An interactive statistical
analysis computer package is used to formulate the mathematical relationship
which exists.
For example, one may build up
the sales model as:
Quantum of Sales = a. price + b.
advertising + c. price of the rival products + d. personal disposable income +u
Where a, b, c, d are the constants
which show the effect of corresponding variables as sales. The constant u
represents the effect of all the variables which have been left out in the
equation but having effect on sales. In the above equation, quantum of sales is
the dependent variable and the variables on the right hand side of the equation
are independent variables. If the expected values of the independent variables
are substituted in the equation, the quantum of sales will then be forecasted.
The regression equation can
also be written in a multiplicative form as given below:
Quantum of Sales =
(Price)a + (Advertising)b+ (Price of the
rival products) c + (Personal disposable
income Y + u
In the above case,
the exponent of each variable indicates the elasticities of the corresponding
variable. Stating the independent variables in terms of notation, the equation
form is QS = P°8. Ao42 .
R°.83. Y2°.68. 40
Then we can say that 1 per cent
increase in price leads to 0.8 per cent change in quantum of sales and so on.
If we take logarithmic form of
the multiple equation, we can write the equation in an additive form as
follows:
log QS = a log P +
b log A + с log R + d log Yd + log u
In the above
equation, the coefficients a, b, c, and d represent the elasticities of variables
P, A, R and Yd respectively.
The co-efficient in the logarithmic
regression equation are very useful in policy decision making by the
management.
(iv) Econometric Models:
Econometric models are an extension
of the regression technique whereby a system of independent regression
equation is solved. The requirement for satisfactory use of the econometric
model in forecasting is under three heads: variables, equations and data.
The appropriate procedure in forecasting
by econometric methods is model building. Econometrics attempts to express
economic theories in mathematical terms in such a way that they can be verified
by statistical methods and to measure the impact of one economic variable upon
another so as to be able to predict future events.
Utility of Forecasting
Forecasting reduces the risk
associated with business fluctuations which generally produce harmful effects
in business, create unemployment, induce speculation, discourage capital
formation and reduce the profit margin. Forecasting is indispensable and it
plays a very important part in the determination of various policies. In modem
times forecasting has been put on scientific footing so that the risks
associated with it have been considerably minimised and the chances of
precision increased.
Forecasts in India
In most of the advanced countries
there are specialised agencies. In India businessmen are not at all interested
in making scientific forecasts. They depend more on chance, luck and astrology.
They are highly superstitious and hence their forecasts are not correct.
Sufficient data are not available to make reliable forescasts. However,
statistics alone do not forecast future conditions. Judgment, experience and
knowledge of the particular trade are also necessary to make proper analysis
and interpretation and to arrive at sound conclusions.
Conclusion
Decision support systems consist of
three elements: decision, prediction and control. It is, of course, with
prediction that marketing forecasting is concerned. The forecasting of sales
can be regarded as a system, having inputs apprises and an output.
This simplistic view serves as a
useful measure for the analysis of the true worth of sales forecasting as an
aid to management. In spite of all these no one can predict future economic
activity with certainty. Forecasts are estimates about which no one can be
sure.
, the ideal forecasting method is one
that yields returns over cost with accuracy, seems reasonable, can be
formalised for reasonably long periods, can meet new circumstances adeptly and
can give up-to-date results. The method of forecasting is not the same for all
products.
There is no unique method for
forecasting the sale of any commodity. The forecaster may try one or the other
method depending upon his objective, data availability, the urgency with which
forecasts are needed, resources he intends to devote to this work and type of
commodity whose demand he wants to forecast.
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