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Demand Analysis Ppt Mba

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    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 asDATA

    .

    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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    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 elses records. These Data are already

    in existence in the recorded or published forms. SecondaryData 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 Surve National Sam le Surve Re orts.

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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) Firms 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 periodunits 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 thedata.

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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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