Demand Fore Casting

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    DEMAND FORECASTING

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    Superior method of demand estimation

    Scientific method (based on dependent & independent

    variable)

    Estimates are more reliableEstimation involves small cost

    STATISTICAL METHODS

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    Following 3 techniques:-

    1. trend projection method

    2.

    3.

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    TREND PROJECTION METHOD

    It is used under the assumption that the factors

    responsible for the past trends in the variable to be

    projected will continue to play their past in future in the

    same manner & to the same extent .

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    Following three techniques:-

    1. Graphical method

    2. Fitting trend equation / least square method

    3. Box-jenkins method

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    (b) Fitting trend Equation -

    Extension of graphical method

    Using statistical techniques

    following types:-

    (a) linear trend(b) exponential trend

    (c) box-junkins method

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    Linear trend

    When a line seriesdata reveals a rising trend in sales

    then a straight line trend equation of the following form

    is fitted:-

    s=a+bT

    S= annual sales

    T = time (years) a&b = constant

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    Exponential trend

    When sales have increased over the past years at a

    increasing rate or at a costant percentage rate , then trend

    equation used is :-

    Y = ae^bT Y is sales , T is time

    Limitations:- 1.assumptions that past trend will

    follow again

    2.cannot be used for short termestimates.

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    Box-jenkins method

    1. Used only for short term predictions

    2. Only for monthly or seasonal variations recurring

    with some degree of regularity

    example:- sale of greeting cards in december last week

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    ECONOMETRIC METHODS

    In this method, statistical tools are combined with the

    economic theories to estimate economic variables and to

    forecast the intended economic variables.

    The forecasts made through econometric methods are

    much more reliable than those made through any other

    method.

    This method can be described briefly by two basicmethods

    1. Regression method

    2. Simultaneous equation method

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    Regression methods

    Regression analysis is the most popular method of

    demand estimation. This method combines economic

    theory and statistical techniques of estimation.

    I

    n this demand function, the quantity to be forecasted is adependent variable and the variables that effect or

    determine the demand are called independent or

    explanatory variables.

    Example: the demand of tunics depend on the socialculture and the seasonal changes. Here, the demand of

    tunics is dependent variable and social culture and

    seasonal changes are independent variables.

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    REGRESSION METHOD

    SINGLE ORBIVARIATEREGRESSION TECHNIQUE

    MULTI-VARIATEREGRESSION TECHNIQUE

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    Single or bivariate regression technique:

    In single regression technique only one variable is

    taken in consideration for the forecasting. And in bivariate

    regression technique only two variable are taken inconsideration.

    Multi variate regression technique:

    The multi variate regression equation is used where

    demand for a commodity is deemed to be the function ofmany variables or in cases in which the number of

    explanatory variables is greater than one.

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    Simultaneous Equations Model

    Simultaneous equations model is a complete and

    systematic approach to forecasting. This technique uses

    mathematical and statistical tools.

    Simultaneous equation model allows the forecaster to

    take into account the simultaneous interaction between

    dependent and independent variables.

    The variables included are:

    1. Endogenous variables

    2. Exogenous variables

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    Thank You

    ADITYA UPADHYAY

    LATIKA KATAYALP SHIVRAM

    SHAH TWINKLE

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