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Demand forcasting

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Demand forecasting
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Page 1: Demand forcasting

Demand forecasting

Page 2: Demand forcasting

Why Demand Forecasting?

• Demand results in sales• Which is the primary source of Revenue

• Predicting future demand for a product• To avoid under or over production• Minimize the “Uncertainties”• Rough estimate of the demand prospects

• Demand forecasting helps in planning to acquire inputs

( men & material), organizing production, advertisement and organizing sales channels.

Page 3: Demand forcasting

Purpose of Forecasting Demand

• Short –run Forecast :– Seasonal patterns are important– Forecasting helps in preparing suitable sales policy and

proper scheduling of output.– Pricing policy and modification in advertising and sales

techniques

• Long-run Forecast :– Capital planning– Planning of production, material, man-hours, machine

time – Changes in variables are included

Page 4: Demand forcasting

Steps Involved in Forecasting

Identification of Objective

Nature of Goods

Selection of methodOf Forecasting

Interpretation of Results

Estimation of one / more than one aspect

Goods have different demand pattern

Page 5: Demand forcasting

Steps Involved in Forecasting

Page 6: Demand forcasting

Time Horizon

Page 7: Demand forcasting

Demand Forecast Determined

Page 8: Demand forcasting

Methods of Demand Forecasting

Techniques

Survey MethodsStatistical Methods

Consumer SurveyDirect Interview

Opinion PollMethods

Trend Projection

EconometricMethods

Page 9: Demand forcasting

• Where the purpose is to make short- run forecast of demand.

• Consumer surveys are conducted to collect information about their intentions and future plans.

1) Survey of potential consumers on their intentions and

plan.

2) Opinion polling of experts

Survey Methods

Page 10: Demand forcasting

1) Consumer Survey Method

• Direct Interview with the potential consumers.• Ask what quantity of the product would they buy at different

prices over a given period of time.

Consumer SurveyMethod

Complete Enumeration

MethodSample Survey End-Use Method

Page 11: Demand forcasting

a) Complete Enumeration Method:

– All potential users of the product are contacted and asked about their future plans of purchasing the product

– The quantities indicated by the consumers are added together to obtain demand of the product• Dp = q1 + q2 + q3+…………..+ qn

n= ∑ qi

i = 1

Limitation:

1) Only successful if consumers are concentrated in a certain region or locality.

2) Consumers actual demand in future may not be known3) Consumers may give hypothetical answers4) Consumers response could be biased to their expectations5) Consumers plan may change with the change in factors not

included in questionnaire

Page 12: Demand forcasting

b) Sample Survey Method:

– Only few potential consumers /users are selected.– Its through face to face/ telephonic interview or mailed / web

questionnaire– On the information, the probable demand may be estimated.– Less costly, less time- consuming– Used to estimate short-term demand (yearly)

Dp = HR HS

Dp = probable demand forecastH = Census number of households Hs = Sample HouseholdHr = No of HH reporting demand for the productAd = Avg expected consumption ( Total quantity reported to be

consumed / no of hh)

( H. AD )

Page 13: Demand forcasting

– Business firms, Government departments and

Households plan their expenditure one year in advance.– Therefore they can supply a fairly reliable estimate of

their future expectations.

Limitations:– Similar to complete enumerations– Quantification of variables (e,g Feelings, opinions,

expectations) is not possible

Page 14: Demand forcasting

c) End- Use Method:

– Requires building up schedule for probable aggregate future demands for inputs by consuming industries / sectors

– Technological, structural & other changes which might influence the demand are taken into account in the process of estimation

– More relevant for B2B markets

Page 15: Demand forcasting

c) End- Use Method:

– Stage 1 : List all possible uses of the product

– Stage 2 : Fix suitable technical norms for each

end use» Per unit of production of complete product /

per unit of investment / per capita use» Questionnaires used to collect relevant

information

– Stage 3 : Application of Norms» Necessary to know targeted levels of output

of individual industries for the target year» And likely development in other economic

activities which use the product & likely output targets

– Stage 4 : Aggregation of demand of each end use

Page 16: Demand forcasting

Limitations

• Enumeration of all possible uses – due to lack of published data

• Despatch records of the manufactures, if available need not enumerate all the final users.

• Impossible to organise and collect data of wide network of wholesale and retail agencies

• Possibility of missing out end-uses or new applications– Therefore estimations should provide some margin of

error• Establishing norms – is difficult• Inaccuracy in estimating sales of target industries

Advantages

• Probing into current use-pattern of consumption of product – it provides opportunity to determine the demand by types, categories & sizes etc

• It facilitates in diagnosis & pin-pointing as to where & why did the actual consumption deviate from estimated demand

Page 17: Demand forcasting

2) Opinion Poll Method

• Aims at collecting opinions of those who possess knowledge of the market

• Sales representatives, sales executives, marketing experts and consultants

Opinion Poll

Expert -Opinion Delphi methodMarket studies/

experiment

Page 18: Demand forcasting

a) Expert – Opinion Method:

– Firms having good network of sales representatives can ask them to assess demand

– As they are in touch with consumers and consumption pattern

– Can provide a approximate figure of likely demand

– Limitations:• Estimates are reliable only to the extent of their

skill to analyse the market.• The assessor may have subjective judgement

which may lead to over / under estimation• Inadequate information may be available to the

assessor as they may have narrow view of the market.

Page 19: Demand forcasting

b) Delphi Method:

– To consolidate the expert opinions and arrive at

estimate of future demand.

– Experts are provided information on the estimates of

other experts, and they revise their own estimates

– The consensus of experts about the forecast

constitutes the final forecast

This technique can be used for cross – checking information on forecasts.

Page 20: Demand forcasting

c) Market studies and experiments

To carry out studies on consumer behaviour under actual, controlled market conditions.

Market studies:– Firms select areas of market having similar features

( populations, income levels, cultural/ social

backgrounds, choices….)– Carry out experiments by changing variables of

demand functions– Consequent changes in demand are recorded – Assessment of demand of the product is made.

Experiments:– Consumers are given money to buy goods with varying

prices, packages, displays…– It reveals consumers responsiveness to the changes

Page 21: Demand forcasting

Limitations:

– Expensive – unaffordable for small firms– Experiments are carried out on a small scale leads to

generalization– Studies are based on short term and controlled

conditions may not exist in uncontrolled market.– Changes in socio-economic , climatic conditions may

alter the results.

Page 22: Demand forcasting

• Advantages

– Subjectivity is minimum– Method of estimation is scientific– Estimates are relatively more reliable– Involves smaller cost

Methods

1) Trend Projection Method

2) Econometric Method

Statistical Methods

Page 23: Demand forcasting

a) Trend Projection

• It is a study of movement of variables through time.• Requires long and reliable time-series data• Its based on the assumption that factors responsible for

the past trends will continue to be the same in future.

a) Graphical Method:– Annual sales data is plotted on a graph – Line is drawn through the plotted points– Free line is drawn that the total distance between the

line and points is minimum.– Second line drawn taking the mid values of variations.– The trend line is then extended to forecast the

demand for next year.

• The projections may not be realisable as the extension of trend line involves subjectivity and personal bias.

Page 24: Demand forcasting

1) Graphical Method

P

Year

Sales

Page 25: Demand forcasting

2) Fitting Trend equation:

b) Fitting Trend equation: Least square method:

Trend line is fitted to the time-series dataLinear best fit curveMinimises the deviation of the actual line

S = a + bT

S = Annual salesT = time (years)a & b are constant

∑ S = na + b ∑ T ∑ ST = a ∑ T + b ∑ T

2

Page 26: Demand forcasting

b) Econometric Method

• It combines statistical tools with economic theories to estimate economic variables.

• Forecast are more reliable

• This model try to identify all those economic and demographic variables that influence the future value of the variable under forecast.

Two types of method:

a) Regression method

b) Simultaneous method

Page 27: Demand forcasting

b) Econometric Method

a) Regression method

• Establishes casual relationship between

– Dependent variable (demand)

– Independent variables (parameters that impact demand)

• Most popular method

• As it combines

– Economic theory

• To specific determinants of demand & their relationship with demand

– Statistical techniques

• To estimate the values of parameter in the equation. Or estimate the impact in the demand for a unit change in the determinant

Page 28: Demand forcasting

b) Econometric Method

a) Regression method

• Simple / Bivariate regression

– If demand of a commodity depends on a single independent variable

• E.g. – demand for salt / sugar depends largely on population

– The relationship can be established using ‘least square method’

• As used in time series

• Only difference is time is replaced by the ‘independent variable’ on which the demand depends the most

Page 29: Demand forcasting

b) Econometric Methoda) Regression method

• Multi-variate regression

– If demand of a commodity depends on a more than one independent variables

• E.g. – demand for sweets, fruits, & vegetables depends on price of the product, price of its substitutes, household income, population etc.

– Procedure• Specify variables that have an impact on demand. This

will be different for different categories• Next specify the form of equation – linear, logarithmic,

power etc• Collect the necessary data• Estimate the value of co-efficient of the independent

variables through statistical techniques. Essentially done with the help of computer

Page 30: Demand forcasting

b) Econometric Method

a) Regression method

– It uses one single equation

– It assumes one-way causation i.e. only independent variable causes

variation in dependent variable and not vice versa

• However, realistically demand for a product also has an impact on price of the product

– This issue can be addressed through simultaneous equation model

Page 31: Demand forcasting

b) Econometric Method

b) Simultaneous method

• Is a complete & systematic approach to forecasting• It involves solving several simultaneous equations for estimating

demand.• It takes two- way causation i.e: simultaneous interaction between

dependent and independent variable. As well as inter-dependence of independent variables

– For instance • Demand for white goods depends on product price, price of

substitute, household income, consumer preference, availability of credit & interest rate

• Interest rate depends on Availability of credit• Which in turn may depend on many other economic

parameters & government policies at that point.• And so on

– Thus estimation of demand will require solving all such functions simultaneously

Page 32: Demand forcasting

Salient features of good forecasting method

• Simplicity• Accuracy• Economy• Availability• Applicability

Though mere possession of right tools is does not necessarily mean accurate forecast. Equally important

is analysts judgement.


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