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DEMAND
FORECASTINGAND
PRODUCT LIFECYCLE
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DEMAND FORECASTING
• An activity of estimating consumers’demand
• Demand forecasting
• It helps in: – Pricing decisions – Assessing future capacity requirements – Decide whether to enter a new market
– Long run capital planning
Pa ssive
A ctive
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STEPS IN DEMANDFORECASTING
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NEED FOR DEMAND FORECASTING
1.The purpose of the Short term forecasting:
• Appropriate scheduling of production to avoidproblems of over production and under-production.
• Proper management of inventories• Evolving suitable price strategy to maintain
consistent sales• Formulating a suitable sales strategy in
accordance with the changing pattern of demand and extent of competition among thefirms.
• Forecasting financial requirements for the shortperiod.
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2.The purpose of long- term forecasting:
• Planning for a new project, expansion andmodernization of an existing unit,diversification and technological up gradation.
• Assessing long term financial needs. It takes timeto raise financial resources.
• Arranging suitable manpower. It can help a firm toarrange for specialized labour force andpersonnel.
• Evolving a suitable strategy for changing patternof consumption.
N E E D F O R D E M A N D F O R E C A S T IN G
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Qualitative MethodsØØ ’Consumers Survey Method
Ø
Ø Sales Force OpinionMethod
ØØ ’Experts Opinion Method
Quantitative MethodsØØ /Mechanical Extrapolation
Trend Line Projection
MethodØØBarometric Techniques
Ø
Ø Statistical MethodsØØSimultaneous EquationMethod
Ø
METHODS OF DEMANDFORECASTING
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ØComplete Enumeration SurveyFirst hand unbiased information
Consumers’ hesitation Consumers’ Biasness
ØSample Survey
Probable demand of each sample summedup
Less tedious and less costly Must be continued for a longer period
Difficult to select proper representative of population
’C on su m ers S u rvey M eth od
QUALITATIVE METHODS
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ØEnd-use MethodDemand analysis of end use of product
Complexity arises because of many end-uses
Demand in International markets
’C on su m ers S u rvey M eth od
QUALITATIVE METHODS
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Sales team is closest to market
Does not involve elaborated statisticalmeasurements
Ø
‘Congenital Optimism’ or ‘Congenital Pessimism’
Possible only for short term projection
Salesman might be ignorant of broader economic
changes
S a le s Fo rce O p in io n M e th o d
QUALITATIVE METHODS
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Views from group of specialists outside the firm
Divergent views from experts even if basic data islacking
More formalized: Delphi Technique
Ø Outcome depends highly on experts’ competence
’E xp e rts O p in io n M e th o d
QUALITATIVE METHODS
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Trend Projection Methods
ØFitting Trend Line by Observation
Ø
ØLeast squares MethodØ
ØSmoothing Methods
ØØARIMA Method
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Observation
ØPast sales data is plotted on a graphØØEstimation of the location of the Trend
Line is done just by observation
ØØ Trend Line is simply extended to a future
period
ØØCorresponding Sales forecast is readagainst that year
Ø
ØScientific Temper is lacking
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Graphic ( )Fitting trend line by observation
Ø
T R E N D P R O JE C T IO N T E C H N IQ U E
QUANTITATIVE METHODS
Q u a
n t
i t
y
d e
m a
n d
e d
Time
Past data
Future projection
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Least Squares Method
ØMost Widely used technique.Ø Statistical Technique.
ØWith the help of this statistical method a
trend line is fitted to the data.Ø Line is known as the ‘line of best fit’ØBy extending the trend line to the future
forecasting can be done.
ØMethod is naïve as it just states that thedata changes as a function of time .
ØNo reason for the changes are shown by thismethod.
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Smoothing Method
ØSmoothing Methods tend to removethe effect of the random variationson the value of the series
ØØ Thus , a clearer indication of the
direction of the movement of the
variable is revealed.ØØMethod of Moving Averages is used
as one of the Smoothing Methods.
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Method Of Moving Averages
Ø Series of arithmetic means calculated from theoverlapping groups of the successive valuesof the time series.
Ø
Ø Each Moving Average is based on valuescovering a fixed time interval called the‘period of moving interval’
Ø
Ø Optimum period of Moving Averages is the onethat coincides with or is a multiple of theperiod of cycle in the time series.
ØØ This would eliminate cyclical variations , reduce
irregular variations and give the best possible
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east Square Method
TREND PROJECTION TECHNIQUE
QUANTITATIVE METHODS
Year S t t2 St
2006 605 1 1 6052006 715 2 4 1430
2008 830 3 9 2490
2009 790 4 16 3160
2010 835 5 25 4175N=5 ΣS=3775 Σt=15 Σ t2=55 ΣSt=11860
Σ = +S N a b Σt
Σ =S t a Σ +t
b Σ t2
= . = .a 5 9 4 5 a n d b 5 3 5
Tre n d lin ee q u a tio n
= . + .5 4 9 5 5 3
5 t
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M o vin g A ve ra g e s
Year Firm's
MarketShare(A)
3 yearly moving
Average(F)
( - )F (A-
F)^21 10 - -2 9 10 -1 1
3 11 11 0 0
4 13 12 1 15 12 12 0 0
6 11 10 1 17 7 9 -2 4
8 9 10 -1 19 14 11 3 910 10 - - -
Tota
l(T)
17
=( / ) /RMSE T N ^1 2
Choose the one with the l
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Barometric Methods
• Based on the idea that future can be predicted from certain eventsin the present.
• Involves statistical indicators which when combined in certainways provide indication of the direction of change in economy.
• The indicators are of the following types:
1. Leading Indicators – Data that move ahead of the series being compared
– E.g.. Birth rates – demand of seats in school
–2. Coincidental Indicators
– Data in series move up and down along with some other series
– E.g.. National Income – Unemployment in economy
–3. Lagging Indicators
– Data move up and down behind the series being compared – E.g.. Industrial wages – Price index for industrial workers
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• Coincident Indicators are never used forforecasting, rather they are used to confirmor refute the validity of the forecasts arrived.
•• Leading Indicators are used for the forecast.
•• Steps involved:
– Locate the leading indicator – Estimate the relationship between the indicatorand the variable
– Find out the forecasted value of the variable – Verify the validity of the forecast
Barometric Methods
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Limitations
• Its not easy to locate the leadingindicator.
• The Time lag between leadingindicator and the predicted eventsometimes is so small that theleading indicator is not useful for
prediction.• The indicators can be used to derive
the direction of the change but no
idea about the magnitude of
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B A R O M E TR IC TE C H N IQ U E
1.Leading Series – Data that move ahead of the series being
compared
– Eg. Birth rates – demand of seats in school –
2.Coincidental Series – Data in series move up and down along with
some other series
– Eg. National Income – Unemployment ineconomy
–3.Lagging Series
– Data move up and down behind the series beingcompared
QUANTITATIVE METHODS
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S TA T IS T IC A L M E T H O D S
1.Naive models – Useful when situation is stable or gradual
change
– Eg. Ratio of Advertising outlay and Salesin past
2.Correlation and Regression method
– Dependent and independent variables – Method consists of 2 steps-1)Identifying variables influencing sales(i.e
dependent variable) through
correlation2 Stud of chan es in sales throu h
QUANTITATIVE METHODS
l l
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• Positive correlation and negativecorrelation
• Types1. Simple correlation(1 independent variable)
2. Multiple correlation(more than 1 independentvariable)
• It identifies the most appropriate set of
variables that influence the dependentvariables.
• For this coefficient of correlation (r) is usedto find the closeness of dependent and
independent variables.
C o rre la tio n A n a lysis
l l
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Example: Year Production(‘000
tonnes)Fuelconsumed(‘000
tonnes)
1989 100 30
1990 102 241991 104 26
1992 107 22
1993 105 24
1994 112 241995 103 38
1996 99 52
C o rre la tio n A n a lysis
R i E i M h d
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• Identified variables are expressed inequational form.
• Depending upon the trend of dependentvariable i.e. linear or non linear, it isdivided into two parts:
1.Linear regression equations
ii.Graphical method
iii.Least squares method5.Non linear regression equations
v. Logarithmic model
vi.Parabolic regression model
R e g re ssio n E q u a tio n M e th o d
QUANTITATIVE METHODS
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S IM U LTA N E O U S E Q U A T IO N M E T H O D
ØAlso known as Complete SystemsApproach
ØØ Involves simultaneous consideration of all variables
ØØSet of equations is made equal to the
number of dependent variables
Ø
ØVery complex
QUANTITATIVE METHODS
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PRODUCT LIFE CYCLE
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ØPhase of Product’s life
ØØNot all products follow this life cycle
ØØ The Product Life Cycle has five Stages
– Product Development – Introduction – Growth – Maturity – Decline
P R O D U C T LIFE C YC LE
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PRODUCT LIFE CYCLE
Time
S a
l e s
Introduction
MaturityDeclineGrowthProduct
Development
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U C T D E V E LO P M E N T
IN TR O D U C T IO N
G R O W T H
M A T U R IT Y
D E C LIN E
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
vCom pany finds and develops a new productIdea
vTranslating various piece of information into a new pr
vvProduct is exposed to test marketvvIf survives in test market, are sent into a real marketpl
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U C T D E V E LO P M E N T
IN TR O D U C T IO N
G R O W T H
M A T U R IT Y
D E C LIN E
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
v Pro d u ct is in tro d u ce d in th e m a rke t fo r th e first tim evv N ot m u ch p eo p le kno w ab ou t th e p rod u ctvv , ,C h a ra cte rize d b y h ig h co sts slo w sa le s vo lu m e s little o r
v D e m a n d is lo w in th e b e g in n in g so it h a s to b e cre a te d
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U C T D E V E LO P M E N T
IN TR O D U C T IO N
G R O W T H
M A T U R IT Y
D E C LIN E
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
costs re d u ced d u e to e co n o m ie s o f scale
sa le s v o lu m e in cre a se s sig n ifica n tly
p u b lic a w a re n e ss in cre a ses
co m p e titio n b e g in s to in cre a se w ith a fe w n e w p la ye rs in e
in cre a se d co m p e titio n le a d s to p rice d e cre a se s
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U C T D E V E LO P M E N T
IN TR O D U C T IO N
G R O W T H
M A T U R IT Y
D E C LIN E
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
o w e re d a s a re su lt o f p ro d u ctio n vo lu m e s in cre a sin g a n d ex p e a k s
co m p e tito rs e n te rin g th e m a rke t
re n tia tio n a n d fe a tu re d ive rsifica tio n is e m p h a size d to m a i
ro fits g o d o w n
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U C T D E V E LO P M E N T
IN TR O D U C T IO N
G R O W T H
M A T U R IT Y
D E C LIN E
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
a tio n a n d d e clin e stage
-b e com e cou n te r o p tim a l
vo lu m e d e clin e o r sta b ilize
, p ro fita b ility d im in ish
/b e co m e s m o re a ch a lle n g e o f p ro d u ctio n d istrib u tio n e ffici
FORECAST OF ANNUAL SALES
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FORECAST OF ANNUAL SALESYear nnual sales
2003 45000
2004 52000
2005 60000
2006 69000
2007 79000
2008 900002009 102000
2010 ?????
Time
S a
l e s
IntroductionMaturity
DeclineGrowthProductDevelopment
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