Demand Analysis
PRESENTED BY :-BABASAB PATIL
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The sources of Data Collection for Demand
Forecasting
Through a market research a variety of information .
Qualitative and quantitative is called as “DATA”.
This have to be collected to estimation of Demand
function and demand forecasting.
These information may be pertaining to varies aspects of
market and demand.
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Cont….
Demand in Past and Present.
Nature of product.
Types of consumer-
Domestic consumers and Industrial consumers.
Age.
Sex.
income of the consumers.
Attitude.
Preferences.
Tastes.
Habits.
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Price Quotations in the retail and wholesale markets.
Urban.
Rural.
Local.
National.
International or Global.
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Sales Promotion.
Advertising .
Free samples.
Discounts.
Window display.
As well as the expenditures so incurred or involved.
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Primary and Secondary Data
Primary Data:
Primary data or information are original in character
which are collected for the first time for the purpose of
analysis. Primary data are raw data and require statistical
processing.
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Secondary Data
Secondary data or information are those which are
obtained from someone else’s records. These Data are already
in existence in the recorded or published forms. Secondary
Data are like finished products since they have been processed
statistically in some form or the other.
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Secondary Sources of Data
Official publications of the Central, state and local
Governments.
1) Plan documents.
2) census of India.
3) Statistical Abstracts of the Indian Union.
4) Annual Survey of Industries.
5) Annual bulletin of Statistical of Exports and Imports.
6) monthly studies of production of selected Industries.
7) Economic Survey, National Sample Survey Reports.
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Trade and Technical or Economic journals and
publication
1) Economic and political weekly.
2) Indian Economic journal.
3) Stock exchange directory.
4) Basic statistics and other information's supplied by the centre
for monitoring Indian Economy.
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Official publications of International Bodies
1) IMF.
2) UNO.
3) World Bank etc.
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Market reports and trade bulletins published by
stock exchange
1) Trader associations.
2) Large business houses.
3) Chambers of Commerce, etc.
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Publications brought out by research institutions
1) Universities.
2) Associations. etc.
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Unpublished Data
1) Firm’s account books
1) Sales.
2) Profits.etc.
secondary data should not be taken at their face
value and are never to be used blindly.
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Statistical Methods of Forecasting Demand
• There are various methods adopted to estimate potential demand.
• Statistical methods are obviously more scientific, against crude value
judgment used to estimate future demand.
• One must take a mid-way by combining statistical results with the value
judgment.
• Again different statistical forecasting methods are not mutually exclusive.
• They are to be used in combination for accuracy and cross checking
purposes.
• For forecasting purposes, it is essential to estimate the structural form and
parameters of the demand function empirically.
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Two types of data for demand estimation:
• Time series data
• Cross-sectional data
Time series data
• Time series data refer to data collected over a period of time according
historical changes in price, income, and other relevant variables influencing
demand for a commodity.
• Time series analysis relate to the determination of change in a variables in
relation to time. Usually trend projections are important in this regard.
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Cross-sectional Data
• Cross -sectional analysis is undertaken to determine the effects
of changes in determining variables like price, income etc. On
the demand for a commodity, at a point of time.
• In time series analysis for instance, for measuring income
elasticity of demand, a sales income relationship may be
established from the historical data and their fast variations in
cross-sectional analysis, however different levels of sales
among different income groups may be compared at a specific
point of time.
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The Important Demand Forecasting Methods
are:
• Consumption Level Method
• Trend Projection Method
• Regression Analysis and Econometric Method
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Consumption Level Method
• Consumption Level demand may be estimated on the basis of the co-
efficients of income elasticity and price elasticity of demand.
• Viewing projected income level and income elasticity of demand
relationship, demand forecasting may be made as under:
D*=D(1+M*.em)
Where, D*=Projected per capita demand
D=Per capita demand
M*=Projected relative/percentage change in per capita
income.
em=Income elasticity of demand.
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Trend Projection Method
• A time series analysis of sales data over a period of time is
considered to serve as a good guide for sales or demand forecasting.
• For long-term demand forecasting trend is computed from the time
based demand function data.
• Trends refers to the long-term persistent movement of data in one
direction – upward or downward.
There are two important methods used for trend projections:
• The method as moving averages.
• The least square method
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The Method of Moving Averages.
• A moving average forecast is based on the average of a certain
number of most recent periods.
• One can select the number of months or years or other period
units in the moving average according to how for back the data
is relevant to future observations.
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The least square method
• The method of least squares is more scientific as compared to
the method of moving averages.
• It uses the straight line equation y= a+bx, to fit the trend to the
data.
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Regression analysis and econometric model
building
• Most commonly for demand forecasting proposes, the
parameters of the demand function are estimated with
regression analysis.
• In demand regression equations relevant variables have to be
included with practical considerations and relevant data have
to be obtained.
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Examples:
• Personal disposal income towards demand for
consumer product.
• Agricultural or farm incomes towards demand for
the agricultural equipments, fertilizers, etc.
• Construction contracts for demand towards
building material such as cement, bricks, steel,
tiles etc.
• Automobile registry over a period towards
demand for car spare parts, petrol etc.
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Thank you
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