6.demand forecasting

8/3/2019 6.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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Examples General Motors, Ford, DaimlerChrysler, Nissan: all use

estimates of demand in making decisions about how manyunits of each model to produce and what prices to chargefor different car models.

Managers a the national headquarters of Dominos Pizzaneed to estimate how pizza demand in United States is

affected by a downturn in the economy: take-out foodbusiness tends to prosper during recession

At HCA, the short run and the long run estimates ofpatient load(demand) in its various geographic markets iscrucial for making expansion plans.

All electric utilities employ economists and statisticians toestimate the current demand for electricity.


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Meaning of Demand Forecasting

An estimate of sales in dollars or physical units for aspecified future period under a proposed marketing plan.

American Marketing Association

Demand forecasting is the scientific and analyticalestimation of demand for a product (service) for aparticular period of time.

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

An operations research technique of planning anddecision 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 goodswhich they can produce.

Consumer Goods: Direct demand

durable consumer goods: new demand or replacement demand

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 look for 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 and

opinion. 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, brand

preferences 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


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Problems Selection of a representative sample: This sample has the

same characteristics as the population as a whole. Randomsampling is generally used for the same.

Eg.1 If 52% of the population is female and if 35% have

annual incomes over $65000,then a representative sampleshould have approx. 52% females and 35% persons withincome over $65000.This is very difficult to obtain.


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Eg.2 What can happen if the sample drawn is not random

occurred during the Presidential campaign in 1948 in

America. A survey was performed that predicted anoverwhelming victory for Thomas Dewey . In fact, HarryTruman won the election. The problem with the survey wasthat the sample was drawn from a subscription list of a

particular magazine. The subscribers were notrepresentative of the entire population of the US, they wereinstead a subgroup of the voting population and had imp.common characteristics. Thus biased sample biasedresults.


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Eg. 3 In 1936 also there was a election forecast error, a popular

magazine predicted Franklin Roosevelt would lose theelection, but it was wrong because the pollsters usedtelephone survey and only wealthy people were able toafford phone at that time.

Today election forecasting has become so accurate becauseof advanced sampling technique employed by the pollsters


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Problem 2 Response bias: The difference between the response given by an

individual to a hypothetical question and the action the individual

takes when the situation actually occurs. Many a times the questions may be such that the respondents give

what they view as a socially acceptable response rather than revealingtheir true preference.

Eg.1 Past surveys by food manuf. Have yielded bad results because of

response bias. On the basis of the results of these surveys foodmanufacturers develop new products. But as noted by the Wall street

journal, there is one big problem People dont always tell the truth,nobody likes to admit that he likes junk food So, a response bias existin such surveys. Asking a sweet eater how many sweets he eats is like

asking an alcoholic if he drinks much!!!!!!!!!!!


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Problem 3

Simply unable to answer accurately the question

posed: Eg.1 A firm is doing a survey to find out the elasticity of

demand for its products, it is interested to know theresponse of the consumers to incremental changes in price

and some other variable. If the firm needs to know howthe consumer would react to such things as 1,2,or 3 percentinc (or dec) in a price or a 5 per cent inc (or dec) inadvertising expenditures. Most of the people interviewedare not able to answer these questions precisely.


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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 agiven 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 ofsalespersons 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 thebusiness and may make wrong estimates.

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

Contd


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Market studies and experiments Expensive and a difficult technique

Analyst hold everything constant except the price of the product. Under this method the firm first selects the some areas as

representative markets-3/4 cities having similar featuresvizpopulation, income levels, cultural and social backgrounds,occupational distribution, choices and preferences of consumers.

Then, they carry out market experiments by changing prices,advertisements expenditure and other controllable variables indemand function under assumption that other things remainsame.

The controlled variables may be changed over time either

simultaneously in all the markets or in selected markets. Afterthese changes are introduced the changes in demand over aperiod of time ( week, fortnight or a month) are recorded. Onthe basis of data elasticity coefficients are computed. These arethen used to predict the future demand for the product


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In field experiments the researchers wont be able to change

the price of goods and actually observe the behavior of theconsumers

Caselet: Some economists at Texas A&M were interested inestimated the price elasticity of the demand for electric

energy. They recruited a sample of 100 households toparticipate in their experiment.

Objective of the study:to observe these households weeklyconsumption of electric power.

After establishing the households baseline level of usagethe researchers experimentally changed the price of theelectric power for part of their sample by paying rebates forreductions in weekly usage


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For example in one of the subgroups the researchers

paid the household 1.3 cents for every kilowatt-hour(kwh) reduction in weekly usage. At the time thisstudy was conducted, the cost of electric power to theresidential consumers was 2.6 cents per kwh

Now, for this subgroup the price of consuming anadditional kwh was increased: to consume anadditional kwh, the household not only had to pay 2.6cents but also had to forgo the rebate of 1.3 cents it

could have received had it conserved rather thanconsumed electricity. Hence, for the subgroup, price ofelectricity increased by 50 per cent from 2.6 to 3.9cents per kwh


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Other subgroups were given other rebate schedules. Andone group the control group was given no rebate.

The researchers could then actually measure the reductionin electricity consumption due to experimentally imposedprice increase by comparing the change in theconsumption of the subgroup receiving rebate with the

change in consumption of the control group.

Results for the experiment study indicated that themaximum price elasticity of the residential demand forelectricity was 0.32. i.e residential demand for electricity

was price inelastic. However, researchers indicated thatthis study measured extremely short run elasticity.


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Experts Opinion Methodi) Group Discussion: (developed by Osborn in 1953) Decisions may betaken with the help of brainstorming sessions or by structured discussions.

ii) Delphi Technique: developed by the Rand Corporation at thebeginning 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 inprice, 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 itat 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 toascertain 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 without

the consciousness of being observed. product is actually sold in certain segments of the market, regarded as the

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 of

failure 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 markets

across 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 thebasis of time series data.

Time series data are composed of:

Secular trend (T): change occurring consistently over a long time andis 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 thatmay have a tendency to recur in a few years

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

0

20

40

60

80

100

120

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200

2001 2002 2003 2004 2005

Year

Demandformobiles(inlakh

s)

Graphical method Past values of the variable on vertical axis and time on horizontal axis and

line is plotted. Movement of the series is assessed and future values of the variable are

forecasted simple but provides a general indication and fails to predict future value of

demand

Contd


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XbY

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 + bXXY= aX+ bX2

Once the coefficients of the trend equation are estimated, we can easily

project the trend for future periods. Solving the normal equations:

a=

b=

Contd

2

)(

))((

XX

XXYY

Q i i M h d


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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 whichprovide 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 thewhole process is repeated from Stage Two.

Stage Five:Once a good fit is attained, its coefficients can be used toforecast future demand.

Contd


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Quantitative Methods :

Smoothing Techniques

n

D

n

i

i1

Moving Average: forecasts on the basis of demand values duringthe 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 sales onthe 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

i

iiDw

1


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Smoothing Techniques

Exponential Smoothing: assign greater weights to themost recent data, in order to have a more realistic estimate ofthe fluctuations. Weights usually lay between zero and one

Ft+1=aDt+(1-a)Ftwhere Dt+1= forecast for the next period, Dt=actual demand in thepresent period, Ft=previously determined forecast for the presentperiod, 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

Q tit ti M th d


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Barometric Techniques

Barometric Technique alerts businesses to changes in theoverall economic conditions.

Helps in predicting future trends on the basis of index ofrelevant economic indicators especially when the past data donot show a clear tendency of movement in a particulardirection.

Indicators may be

Leading indicators: economic series that typically go up or downahead of other series

Coincident indicators: move up or down simultaneously with thelevel 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 mayalso 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 representserrors 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^2

P + a^3

A,

(where a1, a2 and a3 are the parameters and e is the random error term (ordisturbance), having zero mean).

Similar to simple regression analysis, multiple regressionanalysis would aim at estimation of the parameters a1, a2 and

a3.

Choose such values of the coefficients that would minimizethe sum of squares of the deviations.

Contd..


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Problems Associated with Regression Analysis

Multicollinearity: when two or more explanatory variables in theregression model are found to be highly correlated the estimatedcoefficients may not be accurately determined.

Heteroscedasticity: Classical regression models assume that thevariance of error terms is constant for all values of the independentvariables in the model; i.e. variables are homoscedastic.

Specification errors: Omission of one or more of the independentvariables, or when the functional form itself is wrongly constructed orestimate a demand function in linear form, though the function shouldhave been nonlinear.

Identification problem: where the equations have common variables,like a demand supply model.

Contd


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Limitations of Demand Forecasting

Change in Fashion: Is an inevitable consequence of advancement ofcivilization. Results of demand forecasting have short lasting impactsespecially in a dynamic business environment.

Consumers Psychology: Results of forecasting depend largely onconsumers psychology, understanding which itself is difficult.

Uneconomical: Requires collection of data in huge volumes and theiranalysis, 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 byless experienced individuals may lead to erroneous estimates.

Lack of Past Data: Requires past sales data, which may not becorrectly 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 aproduct (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. natureof goods, i.e. capital and consumer goods.

Techniques of demand forecasting depend upon information on three questions: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 buyingintentions of products, their brand preferences and quantities of purchase.

Future demand level may also be ascertained by experts with the help ofbrainstorming or by structured discussions or even by discussing without face toface interaction.

Demand forecasting may also be done by market experiments conducted undercontrolled or simulated conditions or in real markets in which consumers actuallybuy 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 time series data havecomponents like seasonal trend, cyclical trend, secular trend and randomevents. Trend projection can be done by graphical method, least square methodand ARIMA (Box Jenkins) method

Smoothing techniques are used when the time series data exhibit little trend orseasonal variations, but a great deal of irregular or random variation. The most

popular smoothing methods include moving average, weighted moving averageand exponential smoothing.

In barometric forecasting we construct an index of relevant economic indicatorsand forecast future trends on the basis of these indicators.

Econometric methods apply statistical tools on economic theories to estimateeconomic variables.

Regression analysis relates a dependant variable to one or more independentvariables in the form of a linear equation. Regression can be linear, nonlinearand multiple.

Simultaneous equations method incorporates mutual dependence amongvariables.

6.demand forecasting

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