DEMAND FORECASTING & METHODS

By:Debbrata HazraAnkit SaxenaGaurav MishraChinmaya Mohanty

ContentsDemand Forecasting

Features

Managerial Uses

Levels of Forecasting

Criteria of a Good Demand Forecasting

Methods and Illustrations

Conclusion

Demand Forecasting

Demand Forecasting refers to an estimation of most likely future demand for a product under given

conditions.

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Features of Demand Forecasting It is basically a guess work – but it is an educated and well thought out guesswork.

It is in terms of specific quantities.

It is undertaken in an uncertain atmosphere.

A forecast is made for a specific period of time which would be sufficient to take a decision and put it into action.

It is based on historical information and the past data.

It tells us only the approximate demand for a product in the future.

It is based on certain assumptions.

It cannot be 100% precise as it deals with future expected demand

Managerial Uses Of Demand Forecasting

Short Run Long Run

Production planning

Sales forecasting

Estimates short run financial requirements

Reduce the dependence on chances

Frame realistic pricing policy

Business Planning

Manpower Planning

Financial Planning

Business Control

Determination of the growth rate of the firm

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Levels of Demand Forecasting

Micro Level

Industry Level

Macro Level

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Criteria of Good Demand Forecasting Equity

Plausibility

Simplicity

Durability

Flexibility

Availability of Data

Economy

Quickness

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Methods of Demand Forecasting

Survey Methods:1. Consumers’ Interview Method2. Direct Interview Method3. Jury of Executive Method4. Delphi Method5. Output Method

Statistical Methods:1. Trend Projection Methods2. Economic Indicators

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SURVEY METHODS

Consumers’ Interview Method

Under this method, efforts are made to collect the relevant information directly from the consumers

with regard to their future purchase plans.

Direct Interview Method

Under this method Customers are directly contacted and interviewed. Direct and simple questions are

asked to them.

Jury of Executive MethodRationale:

Upper-level management has best information on latest product developments and future product launches.

Approach:

Small group of upper-level managers collectively develop forecasts – Opinion of Group.

Typical Applications:

Short-term and medium-term demand forecasting.

Jury of Executive MethodMain Advantages:

1. Combine knowledge and expertise from various functional areas.

2. People who have best information on future developments generate the forecasts.

Main Drawbacks:1. Expensive.2. No individual responsibility for forecast quality.3. Risk that few people dominate the group.4. Subjective.5. Reliability is questionable.

Delphi Method

Rationale

Eliciting the opinions of a group of experts with the help of mail survey.

Anonymous written responses encourage honesty and avoid that a group of experts are dominated by only a few members.

Delphi Method (Approach)

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Coordinator Sends Initial Questionnaire

Each expertwrites response(anonymous)

Coordinatorperformsanalysis

Coordinatorsends updatedquestionnaire

Coordinatorsummarizesforecast

Consensusreached?

YesNo

Delphi Method (Contd.)

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Main Advantages:1. Generate consensus.2. Can forecast long-term trend without availability of

historical data.

Main Drawbacks:1. Slow process.2. Experts are not accountable for their responses.3. Little evidence that reliable long-term forecasts can

be generated with Delphi or other methods.

Output Method This method forecasts the demand based on the consumption

coefficient of the various uses of the product. Suitable for estimating demand for intermediate products. Also called as consumption coefficient method.

Steps:1. Identify the possible uses of the products.2. Define the consumption coefficient of the product for various

uses.3. Project the output levels for the consuming industries.4. Derive the demand for the project.

STATISTICAL METHODS

Trend Projection Methods

Secular or Long Run Movements

Seasonal Movements

Cyclical Movements

Random Movements

Trend Projection Methods (Contd.)

Advantages:

• It uses all observations.• The straight line is derived by statistical procedure.•A measure of goodness fit is available.

Disadvantages:

•More complicated.• The results are valid only when certain conditions are

satisfied.

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Linear Trend Line

12-21

y = a + bx

wherea = intercept of the relationshipb = slope of the linex = time periody = forecast for demand for period x

b =

a = y - b x

wheren = number of periods

x = = mean of the x values

y = = mean of the y values

xy - nxy

x2 - nx2

xnyn

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Least Squares Example

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x(PERIOD) y(DEMAND) xy x2

1 73 73 12 40 80 43 41 123 94 37 148 165 45 225 256 50 300 367 43 301 498 47 376 649 56 504 81

10 52 520 10011 55 605 12112 54 648 144

78 557 3867 650

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Least Squares Example (Contd.)

12-23

x = = 6.5

y = = 46.42

b = = =1.72

a = y - bx= 46.42 - (1.72)(6.5) = 35.2

3867 - (12)(6.5)(46.42)650 - 12(6.5)2

xy - nxyx2 - nx2

781255712

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Linear trend line y = 35.2 + 1.72x

Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units

70 –

60 –

50 –

40 –

30 –

20 –

10 –

0 –

| | | | | | | | | | | | |1 2 3 4 5 6 7 8 9 10 11 12 13

Actual

Dem

and

Period

Linear trend line

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Moving Average•Naive forecast▫Demand in current period is used as next period’s forecast.

•Simple moving average▫Uses average demand for a fixed sequence of periods.▫Stable demand with no pronounced behavioral patterns.

•Weighted moving average▫Weights are assigned to most recent data.

12-25

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Moving Average: Naïve Approach

12-26

Jan 120Feb 90Mar 100Apr 75May 110June 50July 75Aug 130Sept 110Oct 90

ORDERSMONTH PER MONTH

-120

90100

751105075

13011090Nov -

FORECAST

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Simple Moving Average

12-27

MAn =

n

i = 1 Di

nwhere

n = number of periods in the moving

averageDi = demand in

period i

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3-Month Simple Moving Average

12-28

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH

MA3 =

3

i = 1 Di

3

=90 + 110 + 130

3

= 110 ordersfor Nov

–––

103.388.395.078.378.385.0

105.0110.0

MOVING AVERAGE

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5-Month Simple Moving Average

12-29

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH MA5 =

5

i = 1 Di

5

=90 + 110 + 130+75+50

5

= 91 ordersfor Nov

––

– –

– 99.085.082.088.095.091.0

MOVING AVERAGE

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Smoothing Effects

12-30

150 –

125 –

100 –

75 –

50 –

25 –

0 –| | | | | | | | | | |

Jan Feb Mar Apr May June July Aug Sept Oct Nov

Actual

Ord

ers

Month

5-month

3-month

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Weighted Moving Average

12-31

WMAn = i = 1 Wi Di

where

Wi = the weight for period i,

between 0 and 100 percent

Wi = 1.00

Adjusts moving average method to more closely reflect data fluctuations

n

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Weighted Moving Average (Example)

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MONTH WEIGHT DATA

August 17% 130September 33% 110October 50% 90

WMA3 = 3

i = 1 Wi Di

= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders

November Forecast

Chain Ratio Method

Market Potential for heated coats in the US:

▫Population (U) = 280,000,000▫Proportion of U that are age over 16 (A) = 75%▫Proportion of A that are men (M) = 50%▫Proportion of M that have incomes over $65k (I) = 50%▫Proportion of I that live in cold states (C) = 50%▫Proportion of C that ski regularly (S) = 10%▫Proportion of S that are fashion conscious (F) = 30%▫Proportion of F that are early adopters (E) = 10%▫Average number of ski coats purchased per year (Y) = .5

coats▫Average price per coat (P) = $ 200

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Chain Ratio Method

Market Potential for heated coats in the US:

Market Sales Potential = U x A x M x I x C x S x F x E x Y= 280 Million x 0.75 x 0.50 x 0.50 x 0.50 x 0.10 x 0.30 x

0.10 x200 = $7.88 Million

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Econometric MethodAn advanced forecasting tool, it is a mathematical expression

of economic relationships derived from economic theory.

Economic variables incorporated in the model.

1. Single Equation Model:Dt = a0 + a1 Pt + a2 Nt

Where,Dt = demand for a certain product in year t.

Pt = price of the product in year t.

Nt = income in year t.35

Econometric Method2. Simultaneous equation method:

GNPt = Gt + It + Ct

It = a0 + a1 GNPt

Ct = b0 + b1 GNPt

Where,

GNPt = gross national product for year t. Gt = Governmental purchase for year t. It = Gross investment for year t.

Ct= Consumption for year t. 36

Econometric MethodAdvantages:•The process sharpens the understanding of complex

cause – effect relationships.•This method provides basis for testing assumptions.

Disadvantages:• It is expensive and data demanding.•To forecast the behaviour of dependant variable, one

needs the projected values of independent variables.

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Uncertainties In Demand Forecasting

Data about past and present markets:▫Lack of standardization:- product, price, quantity, cost,

income….▫Few observations▫Influence of abnormal factors:- war, natural calamity

Methods of forecasting:▫Inability to handle unquantifiable factors▫Unrealistic assumptions▫Excessive data requirement

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Uncertainties In Demand Forecasting

Environmental changes:

▫Technological changes

▫Shift in government policy

▫Developments on the international scene

▫Discovery of new source of raw material

▫Vagaries of monsoon 39

Conclusion

Forecasting has taken an essential part of our life also.

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