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DEMAND FORECASTING

A forecast is the prediction of a future

situation. Aim of demand forecasting is to

reduce risk and in planning for firms

long term growth. It start with macroeconomic forecast. Demand and sales of

most goods and services are strongly

affected by business conditions e.g. salesof automobiles, new houses etc.


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DEMAND FORECASTING

A forecast is the prediction of a futuresituation. Aim of demand forecasting is to

reduce risk and in planning for firms long

term growth. It start with macro economicforecast. Demand and sales of most goods

and services are strongly affected by

business conditions e.g. sales of

automobiles, new houses etc.


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TYPES OF DEMAND

FORECASTING

PASSIVE- based on assumption that firm doesnotchange its course of action.

ACTIVE- forecast is done under the conditions

likely changes in future.

FORECASTING STEPS

A Identification of objective

B determining the nature of goodsC selecting a proper method of forecasting

D Interpretation of results


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METHODS OF FORECASTING

Fundamentally two approaches

A SHORTRUN- to obtain information

about the intentions of consumers by

means of market research, survay,economic intelligence etc.

B LONGRUN- to use past exprience as a

guide and by exploiting past trends, to

estimate the level of future demand.


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METHODS OF FORECASTING

NEW PRODUCT--- SURVEY METHOD usedbecause no historical data is available.EXPERT OPINION SURVEY METHOD

Obtaing information from group of experts regarding

future technological stares. Speedy and less costly.DELPHI TECHNIQUE

Panel members are asked by letters to give theirpredictions. They got information throgh post & sent

outcome. Those who dissent are invited to givereasons or else modify their forecasts. Processrepeated and final range of outcome is regarded asprobabilistic forecast.


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CONSUMERS SURVEY METHODIt is a direct approach. Three types

A COMPLETE ENUMERATION SURVEY

The probable demands of all the cosumers for the forecast period are summed up.ADVANTAGE

1. Unbaised

2. Accurate

DISADVANTAGE

1. Cotact large number of persons

2. Tedious

3. Authenticity of data is doubtful

B SAMPLE SURVEYProbable demand expressed by each selected unit is summed up. Then total

sample demand x by the ratio of number of consuming units in thepopulation.Give good results for new product.

ADVANTAGE

less tedious

less costly

less data errors


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C END USE METHOD

Demand surveys of industries using this product as an intermediateproduct. Demand for the final product is the end use demand.Intermediate product may have many end use. Domestic and

international market demand. The demands for final consumptionand exports net of imports are estimated through some otherforecasting methods and its demanded for intermediate useestimated through a survey ofits users industries. Such a method isfeasible for national planning organisations and not for industry.

ADVANTAGES

1. Provides sectorwise demand forecasting

2. Does not require historical data

3. If numbers of end users is limited it will be convenient to use this

method.DISADVANTAGES

It required every industry to furnish its future plan,individualindustry will have to rely on some other method to estimate future

demand, only intermediate demand can be predicted.


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STATISTICAL METHODA TREND METHOD

Based on analysis of past sales pattern. Thesemethods dispense with the need for costly

market research because necessary

information is often already available.Time series data- main 5 techniques as :

1 FITTING A TREND LINE BY OBSERVATION

pattren trend

xx actual bimonthly sale

oct oct oct oct

1980 1981 1982 1983Time

TREND THROUGH LEAST SQUARE METHOD


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TREND THROUGH LEAST SQUARE METHOD

Mathematical

Sales = a + b (year ) or

S = a + btWhere a & b have been calculated from past data & t is theyear number for which forecast is to be made. e.g.

The sale record of company x reveal the following

Year 1970 1972 1974 1976 1978 1980Sales in crores 30 40 45 50 48 57

Estimate sales for the year 1982 &1983.

SOLUTION

To find values of a & b we will have ti solve the normalequation

S = Na + b T

ST = a T + b T 2

IN THE TABLE BELOW WE FIND S, ST , T, T2

YEAR YEAR NUMBER SALES (S) S X T T 2


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YEAR YEAR NUMBER SALES (S) S X T T 2

1970 1 30 30 1

1972 3 40 120 9

1974 5 45 225 25

1976 7 50 350 491978 9 48 432 81

1980 11 57 627 121

36 270 1784 266substituting

270 = 6a + 36 b

1784 =36a +266b

solving these equations for a & b we get a = 30.76 b = 2.34

Thus the trend equation becomes S = 30.76 + 2.34 t years 1982 &1983take on the years number 13 &14.

By substituting these values for t we get Rs. 61.18 & 63.52 crores.

Trend method is popular because it is simple and gives good forecastingmore over it does not require knowledge of economic theory andmarket.


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C TIME SERIES ANALYSIS

MAIN DRAWBACK

Past rate of change in variables continue in future.notappropriate for short term, can not explain turning

point of business cycle.

This is an extension of linear regression which

attempts to build seasonal and cyclical variationsinto the estimating equation,

SALES = a (TREND) + b ( SEASON ) +c(CYCLE ) +d where a,b,c,d are constants calculated

from past data. Trend value is the year number. Thevalue of season is given by normal % differencefrom trend for the season being forecast. The valueof cycle could be found from barometric indicators

for the period of forecast.


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D MOVING AVERAGE &ANNUAL

DIFFERENCE METHOD

A moving average of order k is obtained by adding yearly

demands for successive years of k number of years and

dividing it by k.Thus the moving average of order 5 at year t

= 1/5 ( Dt-2 + Dt1 + Dt + Dt +1 + Dt +2 ) where Dt is

demand in year t.E EXPONENTIAL WEIGHTED MOVING

AVERAGE METHOD

A progressively smaller weight is given to the more

distance as compared to the more recent past years.These

weights will have to be chosen properly chosen. Once a

smooth time series is obtained the trend method can be

applied to these series to generate demand forecasting.


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BAROMATRIC TECHNIQUESIt is based on that the future can be predicted for certain events occuring

in the present. It involve statistical indicators. Time series provide

indications of change in economy or specific industry. These aretermed as the barometer for market change.

( 1 ) Leading indicatorsconsists of indicators which move up or downahead of some series e.g. ( I) index of net business ( capital )

formation ( ii) new orders of durable goods (iii) new building permits( reflect future market change ).

( 2 ) Coincidental indicators which coincide with or fall behindgeneral economic activity or market trends. e.g. number of employeesin non agriculture sector.

(3) Lagging indicators-Those indicators which follow a change aftersometime e.g. are manufacturers stock level and cosumer creditoutstanding.

The problem of choice may arise because some of the indicatorsappear in more than one class.so it is advisable to rely on just oneindicator.This lead to uses the diffusion index.


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DIFFUSION INDEXIt cops with problem of differing signals given by the

indicators. A diffusion index gives the % of risingindicators. In calculating a diffusion index for a group ofindicators scores alloted are1 to rising series, to constantseries and zero to falling series. If 3 out of 6 indicators aremoving up in lagging series the index shall be 50%. It is for

short term forecast.

QS

What is delphi method? What is the use of this method?

How baromatric leadership is achieve discuss in light ofperfect competition?

What are the indicators available in our economy? Presentin the class.

REGRESSION METHOD


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REGRESSION METHOD

Regression anlysis denotes method by which the

relationship between quantity demanded and one or

more independent variables (like income , price

,advertisement ) is estimated.

Simple regression analysis is used when the quantitydemanded is estimated a function of a single

independent variable, such as price. Multiple

regression analysis is used to estimate demanded asa function of two or more variables simultaneously.

SIMPLE REGRESSION METHOD


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SIMPLE REGRESSION METHOD

(SINGLE VARIABLE )A single independent variable is used to estimate a statistical value of

dependent variable that is , the variables to be forecast.QUARTERLY CONSUMPTION OF SUGER

Year Population (millions) Suger consumed 000 Tons

1985-86 10 40

1986-87 12 50

1987-88 15 60

1988-89 20 70

1989-90 25 80

1990-91 30 90

1991-92 40 100suppose we have to forecast demand for suger for 1994-95 on tha

basis of 7year data given in table. This can be done by estimating aregression equation

Y = a + bX

Y is suger consumed, x is population ana a &b are constants.

Like trend fitting method above equation can be estimated by using least square method The parameter a and


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Like trend fitting method above equation can be estimated by using least square method. The parameter a andb can be estimated by solving the following two linier equations:

Y1 = na + bX1 --------------------- 1

XiYi = Xia + bXi ------------------------ 2

calculation of terms in linier equations

YEAR POPULATION X SUGER CONSUMED Y X2 XY

1985-86 10 40 100 4001986-87 12 15 144 600

1987-88 15 60 225 900

1988-89 20 70 400 1400

1989-90 25 80 625 2000

1990-91 30 90 900 2700

1991-92 40 100 1600 4000

n =7 x1 = 152 Y1 = 490 x2 =3994 xy = 12,000

by substituting value in equation 1 and 2 we get

490 = 7a + 152 b -------------------- 3

12,000 = 152a + 3994b ----------------- - 4

by solving equation 3 and 4 we get a = 27.42 b = 1.96

by substituting values for a and b in equationY = a + bx

Y = 27.44 = 1.96 X

Given the regression equation, the demand for suger for 1994-95 can be easily projected if population for1994-95 is known. Suppose population is projected 70 million, the demand for suger in 1994-95 may beestimated as

Y = 27.44 + 1.96 (70)

=164,640tonnes.


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MULTIVARIATE REGRESSION

Function of many variables

1 to specify the variables that are supposed to explain the

variatios in the demand. The explainatory variables arechosen from the determinants of demand.

2 to collect time series data on the independent variables.

3 to estimate the parametersin the chosen equatios with thehelp of statistical techniques.

MULTIPLICATIVE DEMAND FUNCTION

Qx = a Pbx Yc Pd

Qx = Quantity demanded for x, Px = price of commodity, y

= cosumer income, A = advertisement expences, a is

constant, and bcd are parameters expressing the relationship

between demand and Px, y, Py, and A.

SIMULTANEOUSLY EQUATION METHOD


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SIMULTANEOUSLY EQUATION METHOD

also known as complete systmatic approach. It involvessimultaneously cosiderations of all variables, as it is believed thatevery variable influences the other variable influences the other

variable in an economic decision environment, so here the set ofequations equals the number of variables.

Mathematical & statistical -

1 First step-Develop a complete model and specify the behaviouralassumptions regarding the variable included in this model. Thevariable included in models are called

a endogeneous variables

b exogeneous variables

Endogeneous variables that are determined with in the model as

dependent variable

Exogeneous variable determined outside model e.g. Govt. tax rate

2 Data collectionon both above variable which is hardly available

3 the model is solved for each endogeneous vriable in term of exogeneous variable into the


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3 the model is solved for each endogeneous vriable in term of exogeneous variable into theequations, the objective value is calculated and prediction made. e.g. consider a simple macroeconomic model

Yt = Ct + It + Gt + Xt ----------------- 1

YtGross National Product, CtTotal cosumption expenditure, It = Gross Private Investment,Gt = Govt. expenditure, Xt = Net Exports, (X M ) where M = Imorts and subscript t

represents a given time unit eq.1 is an identity , which can be explained with a system ofsimultaneously equations. Suppose in eq. 1

Ct = a + b Yt -------------------- 2

It = 20 --------------------------- 3

Gt = 10 --------------------------- 4

Xt = 5-----------------------------5

In the above system of equations, Yt & Ct are endogeneous variables, It, Gt, Xt are exogeneousvariable. Eq2 is a regression equation that has to be eliminated. EQ 3 4 5 shows the value ofexogeneous variable determined outside model. Suppose we want to predict value of Yt &Ctsimultaneously. Suppose also we estimate the eq 2, we get

Ct = 100 + 0.75 Yt

Now using the equation system, we may determine the value of Yt as

Yt = Ct + 20 +10 +5

= Ct +35since Ct = 100 + 0.75 Yt by substitution we get

Yt0.75 Yt = 100 +35

0.25 Yt = 135

Yt = 135/ 0.25

= 540

now we can calculate Ct easily.

BOX JENKINS METHOD


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BOX JENKINS METHODFor short term, only for stationary time series sales data, only monthly or seasonal

variation, date shows variation e.g. woolen cloth in winter, desert cooler in summer.

According to this approach any time series data can be analysed by the following threemethods:

1 Auto regression model

2 Moving average model

3 Auto regression and Moving average method

STEPS

1 to eliminate trend from time series data2 to check seasonality in stationary time series

3 to pridict the sales in intended period

1 AUTO REGRESSION MODEL

The behavior of a variable in a period is linked to the behaviour of another variable infuture periods. The general form of model is :

Yt = a1 Yt-1 + a2 Yt2--------------+ anYt-n +e1

Value of Y in period t depends on the values of Y in period t-1, t-2,-------tn, e1 israndom portion of Yt. If value of coefficients a1,a2,---an are different from zero itrevals seasonality in data.

2 MOVING AVERAGE MODEL


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2MOVING AVERAGE MODEL

Model estimates Yt in relation to residuals e1 of the previous years.

The generl form of model is:

Yt = m +b1 et-1 + b2 et-2---------bp et-p + et

where M is mean of the stationary time series. et-1, et-2, et-p residuals,the random components of Y in t-1, t-2, --------t-p periods.

3 AUTO REGRESSION ( MOVING AVERAGE MODEL )

After moving Average model is estimated, it is combined with autoregression model to form the final form of the BOX JENKINS

MODEL as below:Y1 = a1 Yt-1 + a2 Y t-2 +------------ +an Yt-n + b1 et-1 +b2 et-2 +------

+ bp et-p + e1

Sophisticated and complicated method require computer.

MOVING AVERAGE METHOD

This simple method assumed that demand in a future year equals theaverage of demand in the past years as below:

Dt = 1/N (Xt-1 + Xt-2----------Xt-n)

Sales in previous years N = number of preceding year.


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Questions

1)You are given the following data:

X 3 6 8 10 13 13 1314

Y 8 6 10 12 12 1414 20

Estimate the regression equation Y = a + b X

2) Why is demand forecasting essential? Is demandforecasting equally important for small and big, andold and new business ventures? ( DISCUSS IN THECLASS )

3) Demand forecasting is must for the business?

Documents

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