Demand forecasting | Prof. Sachin Paurush

Managerial Economics Demand Forecasting Prof. (Dr.) Sachin Paurush
Prof. (Dr) Sachin Kumar Paurush
Demand forecasting is the art and science the process of predicting the future demand for the
firm’s product to drive holistic execution of such demand by corporate supply chain and business
management. In other words, demand forecasting is comprised of a series of steps that involves the
anticipation of demand for a product in future under both controllable and non-controllable
factors.
Demand forecasting may be used in production planning, inventory management, and at times in
assessing future capacity requirements, or in making decisions on whether to enter a new market.
Predicting the future demand for a product helps the organization in making decisions in one of
the following areas:
 Planning and scheduling the production and acquiring the inputs accordingly.
 Making the provisions for finances.
 Formulating a pricing strategy.
 Planning advertisement and implementing it.
Steps in Demand Forecasting:
 Specifying the Objective
 Determining the Time Perspective
 Choice of method for Demand Forecasting
 Collection of Data and Data Adjustment
 Estimation and Interpretation of Results

Techniques of Demand Forecasting
1. Survey Methods: Under the survey method, the consumers are contacted directly and are
asked about their intentions for a product and their future purchase plans. This method is
often used when the forecasting of a demand is to be done for a short period of time. The
survey method includes:
1.1. Consumer Survey Method
Consumer Survey Method is one of the techniques of demand forecasting that involves direct
interview of the potential consumers.
Consumer Survey Method includes the further three methods that can be used to interview
the consumer:
1.1.1. Complete Enumeration Method:
Under this method, a forecaster contacts almost all the potential users of the product and asks
them about their future purchase plan. The probable demand for a product can be obtained by
adding all the quantities indicated by the consumers. Such as the majority of children in city report
the quantity of chocolate (Q) they are willing to purchase, then total probable demand (Dp) for
chocolate can be determined as:

Dp = Q1+Q2+Q3+Q4+……+Qn
Where, Q1, Q2, Q3 denote the demand indicated by children 1, 2,3 and so on.
One of the major limitations of this method is that it can only be applied where the consumers are
concentrated in a certain region or locality. And if the population is widely dispersed, then it can
turn out to be very costly. Besides this, the other limitation is that the consumers might not know
their actual demand in future. Due to this, they may give a hypothetical answer that may be
biased according to their own expectations regarding the market conditions.
1.1.2. Sample Survey:
The sample survey method is often used when the target population under study is large. Only the
sample of potential consumers is selected for the interview. A sample of consumers is selected
through a sampling method. Here, the method of survey may be a direct interview or mailed
questionnaires to the selected sample-consumers. The probable demand, indicating the response
of the consumers can be estimated by using the following formula:
Where Dp = probable demand forecast; H = Census number of households from the relevant
market; Hs = number of households surveyed or sample households; HR = Number of households
reporting demand for a product; AD = Average Expected consumption by the reporting
households (total quantity consumed by the reporting households/ Number of households.
This method is simple, less costly and even less time-consuming as compared to the comprehensive
survey methods. The sample Survey method is often used to estimate a short-run demand of
business firms, households, government agencies who plan their future purchases. However, the
major limitation of this method is that a forecaster cannot attribute more reliability to the forecast
than warranted.
End-use Method: The end-use method is mainly used to forecast the demand for inputs. This
method of demand forecasting has a considerable theoretical and practical value. Under this
method, a forecaster builds the schedule of probable aggregate future demand for inputs by
consuming industries and several other sectors. In this method, during the estimation of a demand
the changes in technological, structural and other factors that influence the demand is taken into
the consideration.
The end-use method helps in determining the future demand for an industrial product in details
by type and size. Also, with the help of end-use method, a forecaster can pinpoint or trace at any
time in the future as to where, why and how the actual consumption has been deviated from the
estimated demand.
Thus, these are some of the most commonly used consumer survey methods, wherein the
customers are directly asked about their intentions about the product and their future purchase
plans.

1.1.3. Opinion Poll Methods
The Opinion Poll Methods are used to collect
opinions of those who possess the knowledge
about the market, such as sales
representatives, professional marketing
experts, sales executives and marketing
consultants.
The Opinion poll methods include the
following survey methods:
1.1.3.1. Expert-Opinion Method:
Companies with an adequate network of sales representatives can capitalize on them in assessing
the demand for a target product in a particular region or locality that they represent. Since sales
representatives are in direct touch with the customer, are supposed to know the future purchase
plans of their customers, their preference for the product, their reaction to the introduction of a
new product, their reactions to the market changes and the demand for rival products.
Thus, sales representatives are likely to provide an approximate, if not accurate, estimation of
demand for a target product in their respective regions or areas. In the case of firms, which lack in
sales representatives can collect information regarding the demand for a product through
professional market experts or consultants, who can predict the future demand on the basis of
their expertise and experience.
Although the expert opinion method is too simple and inexpensive, it suffers from serious
limitations. First, The extent to which the estimates provided by the sales representatives or
professionals are reliable depends on their skill and expertise to analyze the market and their
experience. Secondly, There are chances of over or under-estimation of demand due to the
subjective judgment of the assessor. Thirdly, the evaluation of market demand is often based on
inadequate information available to the sales representatives since they have a narrow view of
the market.
1.1.3.2. Delphi Method:
The Delphi method is the extension of the expert opinion method wherein the divergent expert
opinions are consolidated to estimate a future demand. The process of the Delphi technique is very
simple. Under this method, the experts are provided with the information related to estimates of
forecasts of other experts along with the underlying assumptions. The experts can revise their
estimates in the light of demand forecasts made by the other group of experts. The consensus of
experts regarding the forecast results in a final forecast.
1.1.3.3. Market Studies and Experiments:
Another alternative method to collect information regarding the current as well as future demand
for a product is to conduct market studies and experiments on the consumer behavior under
actual, but controlled market conditions. This method is commonly known as Market Experiment
Method.
Under this method, a firm select some areas of representative markets, such as three or four cities
having the similar characteristics in terms of the population income levels, social and cultural

background, choices and preferences of consumers and occupational distribution. Then the market
experiments are carried out by changing the prices, advertisement expenditure and all other
controllable factors under demand function, other things remaining the same. Once these changes
are introduced in the market, the consequent changes in the demand for a product are recorded.
On the basis of these recorded estimates, the elasticity coefficients are calculated. These computed
coefficients along with the demand function variables are used to assess the future demand for a
product.
The alternative method to market experiments is the Consumer Clinics or Controlled Laboratory
Method wherein the consumers are given some money to make purchases in stipulated store
goods with different prices, packages, displays, etc. This experiment displays the responsiveness
towards the changes made in the prices, packaging and a display of the product. One of the
major limitations of market experiment method is that it is too expensive and cannot be afforded
by small firms. Also, this method is based on short-term controlled conditions which might not exist
in the uncontrolled market. Therefore, the results may not be applicable in the long term
uncontrolled conditions.
Thus, these are some of the opinion poll methods that are used to gather expert opinions of those
who are closely related to the market with an aim to estimate a future demand for the product.
2. Statistical Methods: The statistical methods are often used when the forecasting of demand is
to be done for a longer period. The statistical methods utilize the time-series (historical) and
cross-sectional data to estimate the long-term demand for a product. The statistical methods
are used more often and are considered superior than the other techniques of demand
forecasting due to the following reasons:
 There is a minimum element of subjectivity in the statistical methods.
 The estimation method is scientific and depends on the relationship between the dependent
and independent variables.
 The estimates are more reliable
 Also, the cost involved in the estimation of demand is the minimum.
The statistical methods include:
2.1. Trend Projection Methods
The Trend Projection Method is the most
classical method of business forecasting,
which is concerned with the movement of
variables through time. This method
requires a long time-series data.
The trend projection method is based on
the assumption that the factors liable for
the past trends in the variables to be
projected shall continue to play their role
in the future in the same manner and to
the same extent as they did in the past while determining the variable’s magnitude and direction.
In predicting demand for a product, the trend projection method is applied to the long time-series
data. A long-standing firm can obtain such data from its departments (such as sales) and the

books of accounts. While the new firms can obtain data from the old firms operating in the same
industry. The trend projection method includes three techniques based on the time-series data.
These are:
2.1.1. Graphical Method:
It is the most simple statistical method in which the annual sales data are plotted on a graph, and
a line is drawn through these plotted points. A free hand line is drawn in such a way that the
distance between points and the line is the minimum. Under this method, it is assumed that future
sales will assume the same trend as followed by the past sales records. Although the graphical
method is simple and inexpensive, it is not considered to be reliable. This is because the extension of
the trend line may involve subjectivity and personal bias of the researcher.
2.1.2. Fitting Trend Equation or Least Square Method:
The least square method is a formal technique in which the trend-line is fitted in the time-series
using the statistical data to determine the trend of demand. The form of trend equation that can
be fitted to the time-series data can be determined either by plotting the sales data or trying
different forms of the equation that best fits the data. Once the data is plotted, it shows several
trends. The most common types of trend equations are:
Linear Trend: when the time-series data reveals a rising or a linear trend in sales, the following
straight line equation is fitted:
S = a + bT
Where S = annual sales; T = time (years); a and b are constants.
Exponential Trend: The exponential trend is used when the data reveal that the total sales have
increased over the past years either at an increasing rate or at a constant rate per unit time.
2.1.3. Box-Jenkins Method:
Box-Jenkins method is yet another forecasting method used for short-term predictions and
projections. This method is often used with stationary time-series sales data. A stationary time-
series data is the one which does not reveal a long term trend. In other words, Box-Jenkins method
is used when the time-series data reveal monthly or seasonal variations that reappear with some
degree of regularity.
Thus, these are the commonly used trend-projection methods that tell about the trend of demand
for a product and are based on a long and reliable time-series data.
2.2. Barometric Methods
The Barometric Method of Forecasting was developed to forecast the trend in the overall
economic activities. This method can nevertheless be used in forecasting the demand prospects, not
necessarily the actual quantity expected to be demanded.
Often, the barometric method of forecasting is used by the meteorologists in weather forecasting.
The weather conditions are forecasted on the basis of the movement of mercury in a barometer.
Based on this logic, economists use economic indicators as a barometer to forecast the overall
trend in the business activities.

The Barometric Method of forecasting was first developed in 1920’s, but, however, was abandoned
due to its failure to predict the Great Depression in 1930’s. The Barometric technique was,
however, revived, reformed and developed further by the National Bureau of Economic Research
(NBER), USA in the late 1930’s.
The barometric method is based on the approach of developing an index of relevant economic
indicators and forecasting the future trends by analyzing the movements in these indicators. A
time-series of several indicators is developed to study the future trend. These can be classified as:
1. Leading Series: The leading series is comprised of indicators which move up or down ahead
of some other series The most common examples of leading indicators are- net business
investment index, a new order for durable goods, change in the value of inventories,
corporate profits after tax, etc.
2. Coincidental Series: The coincidental series include indicators which move up and down
simultaneously with the general level of economic activities. The examples of coincidental
series – the rate of unemployment, the number of employees in the non-agricultural sector,
sales recorded by manufacturing, retail, and trading sectors, gross national product at
constant prices.
3. Lagging Series: A series consisting of those indicators, which after some time-lag follows the
change. Some of the lagging series are- outstanding loan, labor cost per unit production,
lending rate for short-term loans, etc.
The following are the criteria on which the indicators are chosen:
 The economic significance of the indicator; such as greater the significance the greater is the
score of the indicator.
 Time Series- statistical adequacy; a higher score is given to the indicator provided with
adequate statistics.
 Conformity with the movement in overall economic activities.
 Immediate availability of the time series.
 The consistency of the series to the turning points in overall economic activities.
 Smoothness of the series.
The problem of indicator selection may arise if some indicators appear in more than one class of
the indicators.
The only advantage of the barometric method of forecasting is that is helps to overcome the
problem of finding the value of an independent variable under regression analysis. The major
limitations of this method are; First, Often the leading indicator of the variable to be forecasted is
difficult to find out or is not easily available. Secondly, the barometric technique can be used only
for a short-term forecasting.
2.3. Econometric Methods
The Econometric Methods make use of statistical tools and economic theories in combination to
estimate the economic variables and to forecast the intended variables.
The econometric model can either be a single-equation regression model or may consist a system
of simultaneous equations. In most commodities, the single-equation regression model serves the
purpose.
But, however, in the case where the explanatory economic variables are so interdependent or
interrelated to each other that unless one is defined the other variable cannot be determined, a

single-equation regression model does not serve the purpose. And, therefore in such situation, the
system of simultaneous equations is used to forecast the variable.
The econometric methods are comprised of two basic methods, these are:
Regression Method: The regression analysis is the most common method used to forecast the
demand for a product. This method combines the economic theory with statistical tools of
estimation. The economic theory is applied to specify the demand determinants and the nature of
the relationship between product’s demand and its determinants. Thus, through an economic
theory, a general form of a demand function is determined. While the statistical techniques are
applied to estimate the values of parameters in the projected equation.
Under the regression method, the first and the foremost thing is to determine the demand
function. While specifying the demand functions for several commodities, one may come across
many commodities whose demand depends by or large, on a single independent variable. For
example, suppose in a city, the demand for items like tea and coffee is found to depend largely on
the population of the city, then the demand functions of these items are said to be single-variable
demand functions.
On the other hand, if it is found out that the demand for commodities like sweets, ice-creams,
fruits, vegetables, etc., depends on a number of variables like commodity’s own price, the price of
substitute goods, household incomes, population, etc. Then such demand functions are called as
multi-variable demand functions.
Thus, for a single variable demand function, the simple regression equation is used while for
multiple variable functions, a multi-variable equation is used for estimating the demand for a
product.
Simultaneous Equations Model: Under simultaneous equation model, demand forecasting involves
the estimation of several simultaneous equations. These equations are often the behavioral
equations, market-clearing equations, and mathematical identities.
The regression technique is based on the assumption of one-way causation, which means
independent variables cause variations in the dependent variables, and not vice-versa. In simple
terms, the independent variable is in no way affected by the dependent variable. For example,
D = a – bP, which shows that price affects demand, but demand does not affect the price, which is
an unrealistic assumption.
On the contrary, the simultaneous equations model enables a forecaster to study the simultaneous
interaction between the dependent and independent variables. Thus, simultaneous equation
model is a systematic and complete approach to forecasting. This method employs several
mathematical and statistical tools of estimation.
The econometric methods are most widely used in forecasting the demand for a product, for a
group of products and the economy as a whole. The forecast made through these methods is more
reliable than the other forecasting methods.
These are the different kinds of methods available for demand forecasting. A forecaster must
select the method which best satisfies the purpose of demand forecasting.

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