Endterm Presentation New

8/4/2019 Endterm Presentation New

http://slidepdf.com/reader/full/endterm-presentation-new 1/38

DEMAND

FORECASTINGAND

PRODUCT LIFECYCLE



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



STEPS IN DEMANDFORECASTING



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.



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



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



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



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



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



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



Trend Projection Methods

ØFitting Trend Line by Observation

Ø

ØLeast squares MethodØ

ØSmoothing Methods

ØØARIMA Method



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



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



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.



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.



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



east Square Method

TREND PROJECTION TECHNIQUE


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



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



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



• 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



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



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




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


l l



• 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



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



• 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




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




PRODUCT LIFE CYCLE



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



PRODUCT LIFE CYCLE

Time

S a

l e s

Introduction

MaturityDeclineGrowthProduct

Development



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





G R O W T H

M A T U R IT Y

D E C LIN E

Time

S a

l e s



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





G R O W T H

M A T U R IT Y

D E C LIN E

Time

S a

l e s



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





G R O W T H

M A T U R IT Y

D E C LIN E

Time

S a

l e s



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





G R O W T H

M A T U R IT Y

D E C LIN E

Time

S a

l e s



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



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



Endterm Presentation New

Documents

Transcript of Endterm Presentation New