DemanDd Forecasting

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Demand Forecasting

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Lecture plan

Meaning of Demand Forecasting

Techniques of Demand Forecasting

Subjective Methods of Demand Forecasting

Survey methods Expert opinion methods

Quantitative Methods of Demand Forecasting

Trend methods

Smoothing methods Simulation

Statistical methods

Limitations of Demand Forecasting


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Objectives

To introduce the relevance of demand

forecasting in business.

To understand the types of demand forecasting. To explore qualitative techniques of forecasting

demand.

To understand quantitative and econometric

methods of demand forecasting. To point out the limitations of demand

forecasting.


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Meaning of Demand Forecasting

An estimate of sales in dollars or physical units fora specified future period under a proposedmarketing plan.

American Marketing Association

Demand forecasting is the scientific and analytical

estimation of demand for a product (service) for a

particular period of time.

It is the process of determining how much ofwhatproducts is needed when and where.

An operations research technique of planning and

decision making.


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Categorization of Demand Forecasting

By Level of Forecasting

Firm (Micro) level: forecasting of demand for its product by

an individual firm.

decisions related to production and marketing.

Industry level: for a product in an industry as a whole.

insight in growth pattern of the industry

in identifying the life cycle stage of the product

relative contribution of the industry in national income.

Economy (Macro) level: forecasting of aggregate demand (or

output) in the economy as a whole.

helps in various policy formulations at government level.


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Categorization of Demand Forecasting

By nature of goods

Capital Goods: Derived demand

demand for capital goods depends upon demand of

consumer goods which they can produce.

Consumer Goods: Direct demand

durable consumer goods: new demand or replacementdemand

Non durable consumer goods: FMCG

By Time Period

Short Term (0 to 3 months): for inventory management and

scheduling.

Medium Term (3 months to 2 years): for production planning,

purchasing, and distribution.

Long Term (2 years and more): may extend up to 10 to 20 years.

for capacity planning, facility location, and strategic planning, long term

capital requirement, and investment decisions.


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Choice of a forecasting technique

depends on: Imminent objectives of forecast, whether it is for a new

product, or to gauge impact of a new advertisement, etc.

Cost involved, cost of forecasting should not be more than

its benefits, here opportunity cost of resources will also be

important.

Time perspective, whether the forecast is meant for the

short run or the long run

Complexity of the technique, vis--vis availability of

expertise; this would determine whether the firm would lookfor experts in house or outsource it

Nature and quality of available data, i.e. does the time series

show a clear trend or is it highly unstable.


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Techniques of Demand Forecasting

Subjective (Qualitative) methods: rely on human judgment andopinion.

Buyers Opinion

Sales Force Composite

Market Simulation

Test Marketing Experts Opinion

Group Discussion

Delphi Method

Quantitative methods: use mathematical or simulation models

based on historical demand or relationships between variables. Trend Projection

Smoothing Techniques

Barometric techniques

Econometric techniques


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Consumers Opinion Survey

Buyers are asked about future buying intentions of products, brandpreferences and quantities of purchase, response to an increase in theprice, or an implied comparison with competitors products.

Census Method: Involves contacting each and every buyer

Sample Method: Involves only representative sample of buyers

Merits

Simple to administer and comprehend.

Suitable when no past data available.

Suitable for short term decisions regarding product and promotion.

Demerits

Expensive both in terms of resources and time.

Buyers may give incorrect responses.

Investigators bias regarding choice of sample and questions cannot be

fully eliminated.


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Sales Force Composite

Salespersons are in direct contact with the customers.

Salespersons are asked about estimated sales targets in their

respective sales territories in a given period of time.

Merits

Cost effective as no additional cost is incurred on collection of data.

Estimated figures are more reliable, as they are based on the notions

of salespersons in direct contact with their customers.

Demerits

Results may be conditioned by the bias of optimism (or pessimism) ofsalespersons.

Salespersons may be unaware of the economic environment of the

business and may make wrong estimates.

This method is ideal for short term and not for long term forecasting

Contd


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Experts Opinion Methodi) Group Discussion: (developed by Osborn in 1953) Decisions may be

taken with the help of brainstorming sessions or by structured

discussions.

ii) Delphi Technique: developed by the Rand Corporation at the beginning

of the Cold War, to forecast impact of technology on warfare. Way of getting repeated opinion of experts without their face to face interaction.

Consolidated opinions of experts is sent for revised views till conclusions

converge on a point.

Merits

Decisions are enriched with the experience of competent experts. Firm need not spend time, resources in collection of data by survey.

Very useful when product is absolutely new to all the markets.

Demerits

Experts may involve some amount of bias.

With external experts, risk of loss of confidential information to rival firms.

Contd


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Market Simulation Firms create artificial market, consumers are instructed to shop with some

money. Laboratory experiment ascertains consumers reactions to changes in

price, packaging, and even location of the product in the shop.

Grabor-Granger test:

Half of members are shown new product to see whether they would actually buy it

at various prices on a random price list and then are shown the existing product.Other half is shown the existing product first and then the new product to ascertain

if a product would be bought at different prices.

Merits

Market experiments provide information on consumer behaviour regarding a

change in any of the determinants of demand.

Experiments are very useful in case of an absolutely new product.

Demerits

People behave differently when they are being observed.

In Grabor-Granger tests consumers may not quote the price they may pay.

Contd..


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Test Marketing

Involves real markets in which consumers actually buy a product withoutthe consciousness of being observed.

product is actually sold in certain segments of the market, regarded asthe test market.

Choice and number of test market(s) and duration of test are very crucial

to the success of the results. Merits

Most reliable among qualitative methods.

Very suitable for new products.

Considered less risky than launching the product across a wide region.

Demerits

Very costly as it requires actual production of the product, and in event offailure of the product the entire cost of test is sunk.

Time consuming to observe the actual buying pattern of consumers..

Extrapolation of figures for calculating demand in widely varying marketsacross its geographical regions may not give accurate results.

Contd.


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Quantitative Methods of Demand Forecasting

Trend ProjectionStatistical tool to predict future values of a variable on the basis of time

series data.

Time series data are composed of:

Secular trend (T): change occurring consistently over a long time and is

relatively smooth in its path.

Seasonal trend (S): seasonal variations of the data within a year

Cyclical trend (C): cyclical movement in the demand for a product that may

have a tendency to recur in a few years

Random events (R): have no trend of occurrence hence they create random

variation in the series.

Additive Form: Y = T + S + C + R..(1)

Multiplicative Form: Y = T.S.C.R.(2)

Log Y= log T + log S + log C + log R.(3)


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Quantitative Methods:

Methods of Trend Projection

Graphical method

Past values of the variable on vertical axis and time on horizontalaxis and line is plotted.

Movement of the series is assessed and future values of the variableare forecasted

simple but provides a general indication and fails to predict futurevalue of demand

0

20

40

60

80

100

120

140

160

180

200

2001 2002 2003 2004 2005

Year

Demandformobiles(inlakhs)


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Least squares method based on the minimization of squared deviations between the

best fitting line and the original observations given.

Estimates coefficients of a linear function.

Y=a+bX where a =intercept

and b =slope

The normal equations:

Y=na + bX

XY= aX+ bX2

Once the coefficients of the trend equation are estimated, wecan easily project the trend for future periods.

Solving the normal equations:

a=

b=

XbY

2

)(

))((

XX

XXYY


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ARIMA method: also known as Box Jenkins method

considered to be the most sophisticated technique of forecasting as it

combines moving average and auto regressive techniques.

Stage One: trend in the series is removed with help of differencing,

i.e. the difference between values at adjacent period of time.

Stage Two: Various possible combinations are created on basis of:

i. order of involvement of auto regressive terms;

ii. the order of moving average terms

iii. the number of differences of the original series. Combinations are selected which

provide an adequate fit to the series.

Stage Three: Parameter estimation is done using Least Squares. Stage Four: Goodness of fit is tested and if it is not a good fit then

the whole process is repeated from Stage Two.

Stage Five:Once a good fit is attained, its coefficients can be used to

forecast future demand.

Contd


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Quantitative Methods :


Moving Average: forecasts on the basis of demand valuesduring the recent past.

Dn= where Di= demand in the ith period, n= number of periods

in the

moving average

Weighted Moving Average: forecast the future value of saleson the basis ofweights given to the most recent observations.The formula for computing weighted moving average is given

as:

Dn= where Di= demand in the ith period, wi= weight for the i

th

period, n= number of periods in the moving average.

n

D

n

i

i1

n

i

iiDw

1


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Exponential Smoothing: assign greater weights to the most recentdata, in order to have a more realistic estimate of the fluctuations.

Weights usually lay between zero and one

Ft+1=aDt+(1-a)Ft

where Dt+1= forecast for the next period, Dt=actual demand in thepresent period, Ft=previously determined forecast for the present

period, and a=weighting factor, termed as smoothing constant.

New forecast equals old forecast plus an adjustment for the error

that had occurred in the last forecastFt+1=aDt+ a(1-a)Dt-1+ a(1-a)

2Dt-2+ a(1-a)3Dt-3+...+a(1-a)

t-1D1+ a(1-a)2Dt-2+ a(1-a)

tF1)

Ft+1 is thus a weighted average of all past observations.

The older the data, the smaller the weight.

Contd


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Barometric Techniques

Barometric Technique alerts businesses to changes in the overall

economic conditions.

Helps in predicting future trends on the basis of index of relevant

economic indicators especially when the past data do not show a

clear tendency of movement in a particular direction.

Indicators may be

Leading indicators: economic series that typically go up or down

ahead of other series

Coincident indicators: move up or down simultaneously with the

level of economic activities

Lagging series : which moves with economic series after a time lag.

Contd.


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Quantitative Methods

Simple (or Bivariate) Regression Analysis:

deals with a single independent variable that determines the value

of a dependent variable.

Demand Function: D = a+bP, where b is negative.

If we assume there is a linear relation between D and P, there may

also be some random variation in this relation.

Sum of Squared Errors (SSE) : a measure of the predictive accuracy

Smaller the value of SSE, the more accurate is the regression equation.

Nonlinear Regression Analysis

Log linear function log D =A + B log P + e

where A and B are the parameters to be estimated and e

represents errors or disturbances.

Linear form of log linear function D* = a + b P* + e

where D*= log D and P*=log P

Contd..


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Multiple Regression Analysis:

D = a1+a2.P+a3.A+e

(where A = advertising expenditure incurred).

D^ = a^1 + a^2P + a^3A,(where a1, a2 and a3 are the parameters and e is the random error

term (or disturbance), having zero mean).

Similar to simple regression analysis, multiple

regression analysis would aim at estimation of theparameters a1, a2 and a3.

Choose such values of the coefficients that would

minimize the sum of squares of the deviations.

Contd..


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Problems Associated with Regression Analysis Multicollinearity: when two or more explanatory variables in the

regression model are found to be highly correlated the estimated

coefficients may not be accurately determined.

Heteroscedasticity: Classical regression models assume that thevariance of error terms is constant for all values of the independent

variables in the model; i.e. variables are homoscedastic.

Specification errors: Omission of one or more of the independent

variables, or when the functional form itself is wrongly constructed orestimate a demand function in linear form, though the function should

have been nonlinear. There would obviously be errors of prediction.

Identification problem: Occurs in the estimation where the equations

have common variables, like a demand supply model.

Contd


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Simultaneous Equations Method Based on the fact that in any economic decision every variable

influences every other variable.

Incorporates mutual dependence among variables.

It is a simultaneous and two way relationships, A typical simultaneous equation model may comprise of:

Endogenous variables: included in the model as dependent

variables

Exogenous variables: given from outside the model

Structural equations: which seek to explain the relation between a

particular endogenous variable and other variables

Definitional equations: which specify relationships that are

considered to be true by definition


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Limitations of Demand Forecasting

Change in Fashion: Is an inevitable consequence of advancement

of civilization. Results of demand forecasting have short lasting

impacts especially in a dynamic business environment.

Consumers Psychology: Results of forecasting depend largely on

consumers psychology, understanding which itself is difficult.

Uneconomical: Requires collection of data in huge volumes andtheir analysis, which may be too expensive for small firms to afford.

Estimation process may take a lot of time, which may not be

affordable.

Lack of Experienced Experts:Accurate forecasting necessitates

experienced experts, who may not be easily available. Forecasting

by less experienced individuals may lead to erroneous estimates.

Lack of Past Data: Requires past sales data, which may not be

correctly available. Typical problem in case for a new product.


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Summary

Forecasting is an operations research technique of planning and decision

making; demand forecasting is the scientific and analytical estimation of

demand for a product (service) for a particular period of time.

Demand forecasting can be categorized on basis of: i. the level of

forecasting, i.e. firm, industry and economy; ii. time period, i.e. short run

and long run iii. nature of goods, i.e. capital and consumer goods.

Techniques of demand forecasting depend upon information on threequestions: a. What do people say? b. What do people do? c. What have

people done?

In consumers opinion survey buyers are asked about their future buying

intentions of products, their brand preferences and quantities of purchase.

Future demand level may also be ascertained by experts with the help of

brainstorming or by structured discussions or even by discussing withoutface to face interaction.

Demand forecasting may also be done by market experiments conducted

under controlled or simulated conditions or in real markets in which

consumers actually buy a product without the awareness of being

observed.


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Summary

Trend projection is a powerful statistical tool frequently used to predict

future values of a variable on the basis of time series data. Most timeseries data have components like seasonal trend, cyclical trend,

secular trend and random events. Trend projection can be done by

graphical method, least square method and ARIMA (Box Jenkins)

method

Smoothing techniques are used when the time series data exhibit little

trend or seasonal variations, but a great deal of irregular or randomvariation. The most popular smoothing methods include moving

average, weighted moving average and exponential smoothing.

In barometric forecasting we construct an index of relevant economic

indicators and forecast future trends on the basis of these indicators.

Econometric methods apply statistical tools on economic theories toestimate economic variables.

Regression analysis relates a dependant variable to one or more

independent variables in the form of a linear equation. Regression can

be linear, nonlinear and multiple.

Simultaneous equations method incorporates mutual dependence

among variables.

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