Market and Demand Analysis

MARKET AND DEMAND ANALYSIS

1

Situational Analysis and Specifications of Objectives

Collection of Secondary Information

Conduct of Market Survey

Characterization of the Market

Demand Forecasting

Market Planning

2

SITUATIONAL ANALYSIS AND SPECIFICATIONS OF

OBJECTIVES

3

COLLECTION OF SECONDARY INFORMATION

• General Sources of Secondary Information

• Industry Specific Sources of Secondary Information

• Evaluation of Secondary Information

4

SECONDARY SOURCES OF DATA

1. Indian Economic Survey2. Indian Basic Facts3. Reports of Export Working Groups on

Various Industries4. Census of Manufacturing Industries5. Indian Statistical Yearbook6. Monthly Statistical Bulletin7. Annual Report of RBI8. Annual Reports and Accounts of the

Companies Listed on the Stock Exchange9. Annual Reports of the Various Associations

of Manufacturers5

CONDUCT OF MARKET SURVEY

• Census Survey• Sample Survey• Steps in a Sample Survey

– Define the Target Population– Select the Sampling Scheme and Sample Size– Develop the Questionnaire– Recruit and Train the Field Investigators– Obtain Information as Per the Questionnaire from

the Sample of Respondents– Scrutinizes the Information Gathered– Analyze and interpret the Information 6

CONDUCT OF MARKET SURVEY

• Some Problems– Heterogeneity of the Country– Multiplicity of the Languages– Design of Questionnaire

7

CHARACTERISATION OF THE MARKET

• Effective Demand in the Past and Present

Production + Imports – Exports – Change in stock level

• Breakdown of Demand– Nature of Product– Consumer Groups– Geographical Division

8

CHARACTERISATION OF THE MARKET

• Price

• Methods of Distribution and Sales Promotion

• Consumers

• Supply and Competition

• Government Policy

9

Forecasting

• Predicting the future• Qualitative forecast

methods– subjective

• Quantitative forecast methods– based on mathematical

formulas

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Types of Forecasting Methods

• Depend on– time frame– demand behavior– causes of behavior

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Time Frame

• Indicates how far into the future is forecast– Short- to mid-range forecast

• typically encompasses the immediate future• daily up to two years

– Long-range forecast• usually encompasses a period of time longer

than two years

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Demand Behavior

• Trend– a gradual, long-term up or down movement of

demand

• Random variations– movements in demand that do not follow a pattern

• Cycle– an up-and-down repetitive movement in demand

• Seasonal pattern– an up-and-down repetitive movement in demand

occurring periodically

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• Analytical

• Cause effect relationship basis

• Quantitative

• Explicit

Causes of Behavior

14

DEMAND FORECASTING

• Qualitative Methods– These methods rely essentially on the

judgment of experts to translate qualitative information into quantitative estimates

– Used to generate forecasts if historical data are not available (e.g., introduction of new product)

– The important qualitative methods are:• Jury of Executive Method• Delphi Method 15

JURY OF EXECUTIVE OPINION METHOD

• Rationale– 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

• Main advantages – Combine knowledge and expertise from various

functional areas– People who have best information on future

developments generate the forecasts16

JURY OF EXECUTIVE OPINION METHOD

• Main drawbacks – Expensive– No individual responsibility for forecast quality– Risk that few people dominate the group– Subjective– Reliability is questionable

• Typical applications– Short-term and medium-term demand

forecasting

17

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

18

DELPHI METHOD

• Approach

Coordinator Sends Initial Questionnaire

Each expertwrites response(anonymous)

Coordinatorperformsanalysis

Coordinatorsends updatedquestionnaire

Coordinatorsummarizesforecast

Consensusreached?

YesNo

19

DELPHI METHOD

• Main advantages– Generate consensus– Can forecast long-term trend without

availability of historical data

• Main drawbacks – Slow process – Experts are not accountable for their

responses– Little evidence that reliable long-term

forecasts can be generated with Delphi or other methods

20

DELPHI METHOD

• Typical application– Long-term forecasting– Technology forecasting

21

TIME SERIES PROJECTION METHODS

• These methods generate forecasts on the basis of an analysis of the historical time series.

• Assume that what has occurred in the past will continue to occur in the future

• Relate the forecast to only one factor - time

The important time series projection methods are:– Trend Projection Method– Exponential Smoothing Method– Moving Average Method

22

Linear Trend Line

12-12-2323

yy = = aa + + bxbx

wherewherea a = intercept of the = intercept of the relationshiprelationshipb b = slope of the line= slope of the linex x = time period= time periody y = forecast for = forecast for demand for period demand for period xx

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

xn

yn

23

Least Squares Example

12-12-2424

xx(PERIOD)(PERIOD) yy(DEMAND)(DEMAND) xyxy xx22

11 7373 7373 1122 4040 8080 4433 4141 123123 9944 3737 148148 161655 4545 225225 252566 5050 300300 363677 4343 301301 494988 4747 376376 646499 5656 504504 8181

1010 5252 520520 1001001111 5555 605605 1211211212 5454 648648 144144

7878 557557 38673867 650650

24

Least Squares Example (cont.)

12-12-2525

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

25

12-12-2626

Linear trend line y = 35.2 + 1.72x

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

70 70 –

60 60 –

50 50 –

40 40 –

30 30 –

20 20 –

1010 –

0 0 –

| | | | | | | | | | | | |11 22 33 44 55 66 77 88 99 1010 1111 1212 1313

ActualActual

Dem

and

Dem

and

PeriodPeriod

Linear trend lineLinear trend line

26

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

Trend Projection Method

27

Exponential Smoothing

12-12-2828

Averaging method Averaging method Weights most recent data more stronglyWeights most recent data more strongly Reacts more to recent changesReacts more to recent changes Widely used, accurate methodWidely used, accurate method

28

Exponential Smoothing (cont.)

12-12-2929

FFt t +1 +1 = = DDtt + (1 - + (1 - ))FFtt

where:where:

FFt t +1+1 = = forecast for next periodforecast for next period

DDtt == actual demand for present periodactual demand for present period

FFtt == previously determined forecast for previously determined forecast for

present periodpresent period

== weighting factor, smoothing constantweighting factor, smoothing constant

29

Effect of Smoothing Constant

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0.0 0.0 1.0 1.0

If If = 0.20, then = 0.20, then FFt t +1 +1 = 0.20= 0.20DDtt + 0.80 + 0.80 FFtt

If If = 0, then = 0, then FFtt +1 +1 = 0= 0DDtt + 1 + 1 FFtt = = FFtt

Forecast does not reflect recent dataForecast does not reflect recent data

If If = 1, then = 1, then FFt t +1 +1 = 1= 1DDtt + 0 + 0 FFtt ==DDtt Forecast based only on most recent dataForecast based only on most recent data

30

Exponential Smoothing (α=0.30)

12-12-3131

FF22 = = DD11 + (1 - + (1 - ))FF11

= (0.30)(37) + (0.70)(37)= (0.30)(37) + (0.70)(37)

= 37= 37

FF33 = = DD22 + (1 - + (1 - ))FF22

= (0.30)(40) + (0.70)(37)= (0.30)(40) + (0.70)(37)

= 37.9= 37.9

FF1313 = = DD1212 + (1 - + (1 - ))FF1212

= (0.30)(54) + (0.70)(50.84)= (0.30)(54) + (0.70)(50.84)

= 51.79= 51.79

PERIODPERIOD MONTHMONTHDEMANDDEMAND

11 JanJan 3737

22 FebFeb 4040

33 MarMar 4141

44 AprApr 3737

55 May May 4545

66 JunJun 5050

77 Jul Jul 4343

88 Aug Aug 4747

99 Sep Sep 5656

1010 OctOct 5252

1111 NovNov 5555

1212 Dec Dec 5454

31


12-12-3232

FORECAST, FORECAST, FFtt + 1 + 1

PERIODPERIOD MONTHMONTH DEMANDDEMAND (( = 0.3) = 0.3) (( = 0.5) = 0.5)

11 JanJan 3737 –– ––22 FebFeb 4040 37.0037.00 37.0037.0033 MarMar 4141 37.9037.90 38.5038.5044 AprApr 3737 38.8338.83 39.7539.7555 May May 4545 38.2838.28 38.3738.3766 JunJun 5050 40.2940.29 41.6841.6877 Jul Jul 4343 43.2043.20 45.8445.8488 Aug Aug 4747 43.1443.14 44.4244.4299 Sep Sep 5656 44.3044.30 45.7145.71

1010 OctOct 5252 47.8147.81 50.8550.851111 NovNov 5555 49.0649.06 51.4251.421212 Dec Dec 5454 50.8450.84 53.2153.211313 JanJan –– 51.7951.79 53.6153.61

32


12-12-3333

70 70 –

60 60 –

50 50 –

40 40 –

30 30 –

20 20 –

1010 –

0 0 –| | | | | | | | | | | | |11 22 33 44 55 66 77 88 99 1010 1111 1212 1313

ActualActual

Ord

ers

Ord

ers

MonthMonth

= 0.50= 0.50

= 0.30= 0.30

33

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

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Moving Average:Naïve Approach

12-12-3535

JanJan 120120FebFeb 9090MarMar 100100AprApr 7575MayMay 110110JuneJune 5050JulyJuly 7575AugAug 130130SeptSept 110110OctOct 9090

ORDERSORDERSMONTHMONTH PER MONTHPER MONTH

--120120

9090100100

7575110110

50507575

130130110110

9090Nov -Nov -

FORECASTFORECAST

35

Simple Moving Average

12-12-3636

MAMAnn = =

nn

ii = 1= 1 DDii

nnwherewhere

nn ==number of periods number of periods in the moving in the moving

averageaverageDDii ==demand in period demand in period ii

36

3-month Simple Moving Average

12-12-3737

JanJan 120120

FebFeb 9090

MarMar 100100

AprApr 7575

MayMay 110110

JuneJune 5050

JulyJuly 7575

AugAug 130130

SeptSept 110110

OctOct 9090NovNov --

ORDERSORDERS

MONTHMONTH PER PER MONTHMONTH

MAMA33 = =

33

ii = 1= 1 DDii

33

==90 + 110 + 13090 + 110 + 130

33

= 110 orders= 110 ordersfor Novfor Nov

––––––

103.3103.388.388.395.095.078.378.378.378.385.085.0

105.0105.0110.0110.0

MOVING MOVING AVERAGEAVERAGE

37

5-month Simple Moving Average

12-12-3838

JanJan 120120

FebFeb 9090

MarMar 100100

AprApr 7575

MayMay 110110

JuneJune 5050

JulyJuly 7575

AugAug 130130

SeptSept 110110

OctOct 9090NovNov --

ORDERSORDERS

MONTHMONTH PER PER MONTHMONTH MAMA55 = =

55

ii = 1= 1 DDii

55

==90 + 110 + 130+75+5090 + 110 + 130+75+50

55

= 91 orders= 91 ordersfor Novfor Nov

––––

– – ––

– – 99.099.085.085.082.082.088.088.095.095.091.091.0

MOVING MOVING AVERAGEAVERAGE

38

Smoothing Effects

12-12-3939

150 150 –

125 125 –

100 100 –

75 75 –

50 50 –

25 25 –

0 0 –| | | | | | | | | | |

JanJan FebFeb MarMar AprApr MayMay JuneJune JulyJuly AugAug SeptSept OctOct NovNov

ActualActual

Ord

ers

Ord

ers

MonthMonth

5-month5-month

3-month3-month

39

Weighted Moving Average

12-12-4040

WMAWMAnn = = ii = 1 = 1 WWii D Dii

wherewhere

WWii = the weight for period = the weight for period ii, ,

between 0 and 100 between 0 and 100 percentpercent

WWii = 1.00= 1.00

Adjusts Adjusts moving moving average average method to method to more closely more closely reflect data reflect data fluctuationsfluctuations

nn

40

Weighted Moving Average Example

12-12-4141

MONTH MONTH WEIGHT WEIGHT DATADATA

AugustAugust 17%17% 130130SeptemberSeptember 33%33% 110110OctoberOctober 50%50% 9090

WMAWMA33 = = 33

ii = 1 = 1 WWii D Dii

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

= 103.4 orders= 103.4 orders

November ForecastNovember Forecast

41

CAUSAL METHODS

• Causal methods seek to develop forecasts on the basis of cause-effects relationships specified in an explicit, quantitative manner.– Chain Ratio Method– Consumption Level Method– End Use Method– Leading Indicator Method– Econometric Method 42

CHAIN RATIO METHOD

• Market Potential for heated coats in the U.S.:– 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

43

CHAIN RATIO METHOD

• Market Potential for heated coats in the U.S.: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

44

CONSUMPTION LEVEL METHOD

• This method is used for those products that are directly consumed. This method measures the consumption level on the basis of elasticity coefficients. The important ones are

45


• Income Elasticity: This reflects the responsiveness of demand to variations in income. It is calculated as:

• E1 = [Q2 - Q1/ I2- I1] * [I1+I2/ Q2 +Q1] • Where

E1 = Income elasticity of demandQ1 = quantity demanded in the base yearQ2 = quantity demanded in the following yearI1 = income level in the base year I2 = income level in the following year

46


• Price Elasticity: This reflects the responsiveness of demand to variations in price. It is calculated as:

• EP = [Q2 - Q1/ P2- P1] * [P1+P2/ Q2 +Q1] • Where

EP = Price elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year P1 = price level in the base year P2 = price level in the following year

47

• 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

END USE METHOD

48

END USE METHOD

• This method forecasts the demand based on the consumption coefficient of the various uses of the product.

Projected Demand for IndchemConsumption

CoefficientProjected Output

in Year XProjected Demand for

Indchem in Year X

Alpha

Beta

Kappa

Gamma

2.0

1.2

0.8

0.5

10,000

15,000

20,000

30,000

Total

20,000

18,000

16,000

15,000

69,00049

LEADING INDICATOR METHOD

• This method uses the changes in the leading indicators to predict the changes in the lagging indicators.

• Two basic steps:1. Identify the appropriate leading indicator(s)

2. Establish the relationship between the leading indicator(s) and the variable to forecast.

50

ECONOMETRIC METHOD• An 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

• WhereDt = demand for a certain product in year t.

Pt = price of the product in year t.

Nt = income in year t.51

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.52

Advantages• The process sharpens the understanding

of complex cause – effect relationships• This method provides basis for testing

assumptionsDisadvantages• It is expensive and data demanding• To forecast the behaviour of dependant

variable, one needs the projected values of independent variables

ECONOMETRIC METHOD

53

UNCERTANITIES 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 54

UNCERTANITIES 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

55

COPING WITH UNCERTAINTIES

• Conduct analysis with data based on uniform and standard definitions.

• Ignore the abnormal or out-of-ordinary observations.

• Critically evaluate the assumptions• Adjust the projections.• Monitor the environment.• Consider likely alternative scenarios.• Conduct sensitivity analysis 56

Market planning• Current marketing situation

- Market, Competition, Distribution, PEST.

• Opportunity and issue analysis - SWOT

• Objectives- Break even, % market share…

• Marketing strategy- target segment, positioning, 4 Ps

• Action program- Quarter 1, Q2, Q3….

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Market and Demand Analysis