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Demand Analysis
PRESENTED BY :-BABASAB PATIL
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.4/10/2013 Babasabpatilfreepptmba.com
 Price Quotations in the retail and wholesale markets.
Urban.
Rural.
Local.
National.
International or Global.
4/10/2013 Babasabpatilfreepptmba.com
 Sales Promotion.
Advertising .
Free samples.
Discounts.
Window display.
As well as the expenditures so incurred or involved.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
Official publications of International Bodies
1) IMF.
2) UNO.
3) World Bank etc.
4/10/2013 Babasabpatilfreepptmba.com
Market reports and trade bulletins published by
stock exchange
1) Trader associations.
2) Large business houses.
3) Chambers of Commerce, etc.
4/10/2013 Babasabpatilfreepptmba.com
Publications brought out by research institutions
1) Universities.
2) Associations. etc.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
The Important Demand Forecasting Methods
are:
• Consumption Level Method
• Trend Projection Method
• Regression Analysis and Econometric Method
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
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.
4/10/2013 Babasabpatilfreepptmba.com
Thank you
4/10/2013 Babasabpatilfreepptmba.com

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Demand analysis PPT OF MANAGERIAL ECONOMICS MBA

  • 1. Demand Analysis PRESENTED BY :-BABASAB PATIL 4/10/2013 Babasabpatilfreepptmba.com
  • 2. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 3. 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.4/10/2013 Babasabpatilfreepptmba.com
  • 4.  Price Quotations in the retail and wholesale markets. Urban. Rural. Local. National. International or Global. 4/10/2013 Babasabpatilfreepptmba.com
  • 5.  Sales Promotion. Advertising . Free samples. Discounts. Window display. As well as the expenditures so incurred or involved. 4/10/2013 Babasabpatilfreepptmba.com
  • 6. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 7. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 8. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 9. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 10. Official publications of International Bodies 1) IMF. 2) UNO. 3) World Bank etc. 4/10/2013 Babasabpatilfreepptmba.com
  • 11. Market reports and trade bulletins published by stock exchange 1) Trader associations. 2) Large business houses. 3) Chambers of Commerce, etc. 4/10/2013 Babasabpatilfreepptmba.com
  • 12. Publications brought out by research institutions 1) Universities. 2) Associations. etc. 4/10/2013 Babasabpatilfreepptmba.com
  • 13. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 14. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 15. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 16. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 17. The Important Demand Forecasting Methods are: • Consumption Level Method • Trend Projection Method • Regression Analysis and Econometric Method 4/10/2013 Babasabpatilfreepptmba.com
  • 18. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 19. 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 4/10/2013 Babasabpatilfreepptmba.com
  • 20. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 21. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 22. 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. 4/10/2013 Babasabpatilfreepptmba.com
  • 23. 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. 4/10/2013 Babasabpatilfreepptmba.com