market and demand analysis
TRANSCRIPT
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
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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
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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
12-12-3030
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
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Exponential Smoothing (cont.)
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
Exponential Smoothing (cont.)
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
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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
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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
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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
CONSUMPTION LEVEL METHOD
• 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
CONSUMPTION LEVEL METHOD
• 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
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• 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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