DEMAND FORECASTING

By,Sandeep P

WHAT IS DEMAND FORECASTING?• Reasonable judgment of future probabilities of market

events • An estimation of future demand of a product• It may not be 100% precise, but it can give a reliable

approximation of the possible demand or outcome.

SIGNIFICANCE OF DEMAND FORECASTING• Production Planning• Sales Forecasting• Control of Business• Inventory Control• Growth and Long-term Investment programs• Economic Planning and Policy Making

STEPS IN DEMAND FORECASTINGThe objective of demand forecasting is achieved only when it is made systematically and scientifically and when it is fairly reliable.Specifying the objective: the objective or the purpose of demand forecasting must be clearly specified. Determining the time perspective: depending on the firm’s objective, demand may be forecast for a short period or for a long period.Making choice of method for forecasting: the demand forecaster had to choose a suitable method keeping in view his purpose and requirements. The choice of forecasting method mainly depends on the availability of required data, and also on purpose, experience and expertise of the forecaster.

Collection of data and data adjustment: the next step is to collect the primary and secondary data. Sometimes the required data has to be generated from the secondary sources.Estimation and interpretation of results: the availability of data often determines the method and also the trend equation to be used for demand forecasting. Once the required data is collected and forecasting method is finalized, the final step is to make the estimate of demand

1.Survey methods

Consumer survey /direct

interview

Complete enumeration

Sample survey

End-use method

Opinion poll methods

Expert opinion

Sample survey method

Delphi method

Market studies and experiments

Market test Laboratory tests

VARIOUS METHODS OF DEMAND FORECASTING

2.Statistical methods

Trend projection

Graphical method

Least square method

Box-jenkins method

Barometric

Lead-lag indicators

Diffusion indices

Econometric method

Regression methods

Bivariate Multivariate

Simultaneous equations

CONSUMER SUVEY METHOD• This method involves the direct interview of the potential

costumers• Any of the following method can be used depending on the

purpose, time and cost of survey.

1.COMPLETE ENUMERATIION• Almost all the potential customers are involved in this method.• The quantities indicated by the customers are added to get the

probable demand for the product.

2.SAMPLE SURVEY METHOD• This method is used when the population of the target

market is very huge• Only a sample of potential consumers are selected for the

interview• Method of survey can be direct interview or mailed

questionnaire to the sample• Used for short-term estimation of demand• Simpler, economic and less time-consuming than the

comprehensive survey method

3.THE END-USE METHOD• First stage : need to identify all the possible users of the

product• where relevant data is not available, the managers need to

have through knowledge of the product and its uses• Despite every effort made to trace out the end-users it is

likely that some of them are overlooked. To address such lapses, a margin of error should also be taken into account

• Second stage involves fixing suitable technical norms of consumption of the product. Norms are usually expressed in physical terms either per unit of production or per unit of investment or per capita use.

• Having established the technical norms of consumption for the different industries and other end-users of the product the third stage is the application of the norms

• The fourth stage in the end-use method is to aggregate the product wise or use-wise content of the item for which the demand is to be forecast

• This aggregate will gives the estimate of demand for the product as a whole for the terminal year in question.

OPINION POLL METHODS

1.EXPERT OPINION METHOD• Firms having a good network of sales representatives can

put them to work of assessing the demand for the product in the areas they represent

• Being close to the customers they will be in a position to provide at least an approximate, if not accurate, estimate of the demand for the product

• The estimates obtained from different regions are added up to get the overall probable demand for the product

2.DELPHI METHOD• An extension of expert-opinion poll method. This method

is used to consolidate the divergent expert opinion and to arrive at a compromise estimate

• The participants are supplied with responses to questions from others in a group by a leader. The leader provides each expert with opportunity to react to the information given by others, including reasons advanced, without disclosing the source.

• The experts may revise their own experts in light of forecasts made by other experts.

3.MARKET STUDIES AND EXPERIMENTS• Under this method firms select some areas of the

representative markets, then carryout market experiments by changing the prices, adv. expenditure and other controllable variables

• The controllable variables can be changed simultaneously in all markets or in the selected markets

• The consequent changes in the demand over a period of time are recorded and future demands are assessed.

TREND PROJECTION METHODS• This is a classical method of forecasting and requires a

long and reliable time-series data• This method is used under the assumption that the factors

that are responsible for past trends, will continue to play their part in future in the same manner

GRAPHICAL METHOD• In this method, annual sales data is plotted on a graph and

a line is drawn with a free hand so that the distance between the line and the points is minimum

• And another line ‘straight trend line’ is also drawn• By extending these two lines we can approximate the

sales of a product

Let us suppose that a local bread manufacturing company wants to assess the demand for its product for the year 2009. for this purpose it uses time-series data on its total sales over past 10yrs .

Year Sales of bread (‘000 tonnes)1997 10

1998 12

1999 11

2000 15

2001 18

2002 14

2003 20

2004 18

2005 21

2006 25

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 20090

5

10

15

20

25

30

2.LEAST SQUARE METHOD• In this method a trend line is fitted to the time series sales

data with the aid of statistical techniques• The form of trend equation that can be fitted to the time-

series data is determined either by plotting the data or by trying different forms of trend equation for the best fit.

LINEAR TREND• When a time-series data reveals a rising trend in sales, then

a straight-line trend equation of the following form is fitted

where, S is the annual sales, T is time and a & b are constants

The annual consumption of coal by different industries in India is as follows

Year Consumption (S) (million tons)

Time (T) T*T T*S

2001-02 349.74 1 1 349.74

2002-03 361.75 2 4 723.5

2003-04 379.4 3 9 1135.2

2004-05 404.69 4 16 1618.76

2005-06 433.26 5 25 2166.3

2006-07 462.32 6 36 2773.92

2007-08 502.66 7 49 3518.52

2008-09 547.37 8 64 4378.96

2009-10 587.81 9 81 5290.29

2010-11 592.99 10 100 5929.9

∑ S=4621.99 ∑T=55 ∑ T*T=385 ∑T*S=27885.09

All statistics as per the site www.data.gov.in

S = n.a. + b T …1ST = a T + b T2 ….2

Substituting the computed valueswe have,4622 = 10a + 55b ….3 27885= 55a + 385b …. 4Multiplying (3) by 11 and (4) by 2 we have50842 = 110a + 605b55770= 110a + 770b Therefore, 165b = 4928, b = 29.86Substituting b =29.86 in (3)4622 = 10a + 55(29.86)4622= 10a +1642.310a = 2979.7 a = 297.97

St. line equation is S = a + bTSubstituting the values of a and b,S = 297.97 + 29.86TTherefore,Forecast for the year 2014-15S= 297.97+ 29.86(14) = 716.01i.e. 716.01 million tons.

Now let us try to estimate the consumption of coal for the year 2014-15.

BAROMETRIC METHOD OF FORECASTING• This method follows the method meteorologists use in

weather forecasting.• This technique was developed to forecast the general

trend in overall economic activities.

The basic approach of this technique is to construct an index of relevant economic indicators and to forecast future trends on the basis of movement in these indicators.The economic indicators used in this method are classified as 1.Leading indicators2.Coincidental indicators3.Lagging indicators

ECONOMETRIC METHODSThis method combines statistical tools with economic theories to estimate economic variables and to forecast the intended economic variables. An econometric model may be a single equation regression model or it may consist of a system of simultaneous equations.

1.REGRESSION METHOD• This method combines economic theory and statistical

techniques of estimation.• Economic theory is employed to specify the determinants

of demand and to determine the nature of the relationship between the demand for a product and its determinants.

• Statistical techniques are employed to estimate the values of parameters on the estimated equation.

• While specifying the demand functions, one can come across commodities whose demand depends, by and large, on a single independent variable, or on a number of variables.

SIMPLE REGRESSION• In this method a single independent factor is used to

estimate a statistical value of dependent variable, that is the variable to be forecast.

• It is similar to that of least squares method

• Suppose that we have to forecast the demand of sugar consumption in India for the year 2020

The statistics of sugar consumption and Indian population is given below

Year Population (X)

Sugar consumption (Y)

X*X X*Y

2002 1,045 167.81 1092025 175361

2003 1,049 183.84 1100401 192848

2004 1,065 172.85 1134225 184086

2005 1,080 185.00 1166400 199800

2006 1,095 189.45 1199025 207448

2007 1,129 201.60 1274641 227606

2008 1,147 220.00 1315609 252340

2009 1,166 230.00 1359556 268180

2010 1,173 210.16 1375929 246517

2011 1,189 214.36 1413721 254874

∑X=11,138 ∑Y=1975 ∑X*X=12431532 ∑X*Y=2209060

∑Y= na + b∑X∑XY= ∑Xa + b∑X*X

1975=10a + 11138b…………………….(1)2209060= 11138a + 12431532b………..(2)by solving these two equations we get a= -200.68 and b=0.3575Now it is estimated that the population of India in 2020 would be around 1300 million. Now substitute these values in the equation to get the estimated consumption of sugar.

Y= -200.68 + 0.3575 * 1300Y= 264.07 lakh tons.

MULTI-VARIATE REGRESSION• This is used where the demand of the commodity is

considered to be a function of explanatory variables greater than one.

• The first step in this method is to specify the variables that are supposed to explain the variations in demand of the product.

• Secondly we need to collect the time-series data on the independent variable.

• The next step is to specify the form of equation which can appropriately describe the nature and extent of relationship between the dependent and independent variables.

• The final step is to estimate the parameters in the chosen equation with the help of statistical techniques

• Where the relationship between the demand and its determinants is given by linear equation, the most common form of equation used for estimating demand is given as below

• The multiplicative form of demand function or power

function is given as follows

Where Q: demand of the commodityP: price of the commodityY: consumers incomep: price of the substituteA: advertisement expenditure‘a’ is a constant and b,c,d,j are parameters expressing the relationship between Q and P,Y,p and A.

SIMULTANEOUS EQUATION MODEL• This model involves estimating several simultaneous

equations. These equations are generally behavioral equations, mathematical equations and market-clearing equations.

• Furthermore, regression technique assumes one-way causation i.e. only the independent variables cause variations in the dependent variable, not vice-versa.

• This is an unrealistic assumption.• The first step in this technique is to develop a complete

model and specify the behavioral assumptions regarding the variables included in the model.

• The second step is to collect the data on both endogenous and exogenous variables

• The third step is to estimate the model though some appropriate method. Generally a two stage least square method is used to predict the values of exogenous variable.

• Finally the model is solved for each endogenous variable in terms of exogenous variables. Then by plugging the values of exogenous variables into the equations the objective value is calculated and the prediction is made.

• A simple macroeconomic model is given below

Where Y= gross national productC= total consumption expenditureI= gross private investmentG= government expenditureNX= net exports.

CONCLUDING REMARKS• There are several methods for forecasting demand of a

product, and all the methods have their own limitations and advantages.

• The applicability and usefulness of a method depends on the purpose of forecasting and availability of reliable and relevant data.

• Mere possession of the right tools does not necessarily ensure an accurate forecast. Equally important is the analyst’s own judgment.

• Events related to economic activity take place suddenly and sometimes in an uneven manner. Without adequate information about the current situation, there are bound to be errors of judgement in statistical projections.

• By the very true nature of the problem, there can be no guarantee of accuracy in any specified methodology or system of indicators.