# 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

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

LATIKA KATAYALP SHIVRAM

SHAH TWINKLE

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