DEMAND FORECASTING
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Demand Forecasting
• To get an overview of the market and act
proactively
• To adjust production and avoid over
production and under production
• Essential for production scheduling,
purchase of raw materials, arranging
finance and advertising
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• Process of forecasting DD and sales of a
firm’s product:
• Firm uses macro forecast of general
economic activity (GNP) as inputs for
micro forecasts
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Qualitative Methods
• Used for short term forecasts when data
are not available
• Also for supplementing quantitative
forecasts
• Surveys on economic intentions can
reveal and can be used to forecast future
purchase of capital equipment, inventory
changes and major consumer
expenditures
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Survey Methods
• Complete enumeration (census) vs
sample
• Questionnaire, interview or observation
Opinion Polls:
• Consumer Survey
• Executive Polling
• Sales force polling
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Survey Methods
Buying Intentions of consumers has limited use
because
- Consumer may not be able to clearly foresee the
choice,
- Wishful thinking
-Answers tailored to impress interviewer
- New alternatives may emerge,
-Passive because it does not measure variables
which are under management control
-Intention may not translate into actual buying
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End-use method
Steps:
• Identify all possible uses e.g., as input to other
industries, plus direct consumption
• Establish technical norms of consumption for
each end use
• Find out target levels of output in all industries
consuming the product- including exports in
targeted year for each industry
• Likely new developments involving product
• Add
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Survey Methods
Expert Opinion Poll:
- Market consultants, industry analysts with
knowledge of product and the market
conditions
- Personal insights can be subjective
- To avoid the problem of dominant
personality, Delphi method
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Delphi method
Developed by Rand Corporation in 1940s- arriving
at consensus
- Anonymity
-Wide expertise
-Effective when there is no urgency
but
-Difficulty in getting panelists
-Requires understanding, skill and knowledge for
conceptualising, stimulating discussion and
making inferences by researcher .
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Market Studies and Experiments
- Consumer Clinics or controlled lab
experiments where consumer is given an
amount and expenditure behaviour is
observed
- Aware of being observed, consumer may
not behave naturally
- Costly
- Not large enough to generalise
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Market Experiments
Market Experiments:
• Changes are introduced in select markets
and consumer response studied
• Gives time for producer to make
necessary changes if necessary
• Costly errors are avoided
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QUANTITATIVE METHODS
1. Time Series Method- naïve forecasting
- Variables change with time
Sources of variation in Time series :
Secular Trend
Seasonal Changes
Cyclical Fluctuations
Random or irregular fluctuations
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Time Series MethodTotal variation , say in sales, is the result of
all four factors operating together.
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Time Series Method
• Trend Projection
• Simplest- projecting the past trend by
fitting a straight line to the data either
visually or more precisely through
regression
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Least Squares Method
Least Squares Method- Most widely used time
series method
Linear Equation of a straight line is
Y = a + bX
where Y is the demand and X is the time period
(no of years), a and b are constants depicting
intercept and slope of the line. Calculation of Y
for any value of X requires the values of a and b,
for which 2 normal equations are prepared:
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Least Squares Method
ΣY = na + b ΣX
ΣXY=a ΣX + b ΣX2
With values of a and b, straight line equation
is obtained and forecast is made for Y for
given value of X.
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Time Series Method
Smoothing Techniques:
These predict values of a time series on the basis
of some average of its past values.
Useful when time series exhibit little trend or
seasonal variations but a great deal of irregular
or random variations
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Time Series Method
Moving Average Method:
3 (or 5) monthly/ yearly/ quarterly
moving averages computed
Average value of the last 3 (or 5)
entries becomes the forecast for the
next period
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Time Series Method• Simple moving average gives equal weight
to all observations, even though more
recent observations are likely to be more
important.
• Exponential smoothing overcomes this
problem.
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Barometric Forecast
. Barometric Forecast:
- When data indicates cyclical fluctuations
- To predict short term changes in economic
activity or turning points
- Barometric forecasting is done by NBER and
the Conference Board
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Barometric Forecast
- Related variables are categorised into 3
groups- Leading variables: those that change
before the actual change
- Coincident Variables: Change along with
variable
- Lag variables: Follow the event
- If leading variables are identified, easy to
predict actual variables
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Barometric Forecast
PEAK
C. Lagging
variable
Time
PEAK
Trough
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Barometric Forecast
Leading indicators :
• Building permits, new private housing units
• Number of loan applications
• New orders for durable goods for their
components and raw materials
• Index of consumer expectations
• Stock prices
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Barometric Forecast
Coincident indicators :
• Rate of unemployment
• GDP
• Industrial production
• Manufacturing and trade sales
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Barometric Forecast
• Lagging Indicators:
• Commercial and industrial loans
outstanding
• Change in consumer price index for
services
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Commonly used Macroeconomic Predictive
Indicators:
• Hiring
• Consumer spending
• Consumer confidence
• Purchase managers’ index
• Bank Lending
• Shipping activity
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Barometric Forecast
Problems:
• Only used for short term forecasting
• Difficult in identifying and getting data on
variables
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• Hiring, consumer spending, consumer
confidence, purchase managers’ index,
bank Lending, Shipping activity
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Econometric Modelling for
Forecasting
• Identifying and measuring the relationship
• Can be single variable or multivariate regression
model
• Single equation models for a firm’s demand;
Large multiple equation models for the entire
economy
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Regression Analysis
Steps in Regression Analysis
1.Identification of relevant explanatory/
independent variables
2.Collection of data on variable under forecast and
its determinants- can be time series or cross
section
3.Specifying the appropriate demand function for
Estimation (Linear, log linear etc)
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Regression Analysis
• Dx = a0+ a1Y+ a2 Px+a3Py+a4A+ε
• a1,, a2 , a3, a4: respective partial regression
coefficients- measure of elasticitymeasure both magnitude and direction of
change
• Ε: Error term shows effect of omitted
variables or any error in measurement
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Regression Analysis
4. Estimating the above function by using the
collected data on the variable and its
determinants to get the values of the coefficients
as well as coefficient of determination, R2.
Higher the R2 better the fit
5. Forecast for the variable, given the estimated
values of coefficients e.g., you can forecast
expected sales if you know the future Y, P, A P
of substitute etc
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Regression Analysis
• Not subjective like the qualitative methods
• Based on causal relationships and
produces accurate results
• Method is consistent
• Forecasts both direction and magnitude of
change BUT
• Uses complex calculations
• Costly and time consuming
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Simultaneous Equations
Method
• Involves specification of a number of
economic relationships, one for each
behavioral variable and its estimation.
• Method leads to a complete model which
can explain the behavior of all the
variables
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Risks in Demand Forecasting
• Inadequate analysis of the market- include
all potential users of a product
• Forecasting all drivers of market in each
segment
• Unforeseen events
• Petroleum industry
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Sums in Forecasting
Data for demand for watches for 5 years is
given, estimate demand for 2014:
Year 2005 2006 2007 2008 2009
No
120 130 150 140
160
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Sum in Forecasting
Year
X
Y
X2
Y2
XY
2005
1
120
1
14400
120
2006
2
130
4
16900
260
2007
3
150
9
22500
450
2008
4
140
16
19600
560
2009
5
160
25
25600
800
Total
15(ΣX) 700
(ΣY)
n=5
55
(ΣX2 )
99000
2190
(ΣXY) 37
Sums in Forecasting
Normal equation: Y= a + bX (i)
ΣY= na + b ΣX (ii)
ΣXY = aΣX + b ΣX2 (iii)
700= 5a + 15b (iv)
2190=15a + 55b (v)
Solving (iv) and (v) we get,
10 b= 90 b=9; Substituting the value of b in (iv)
700=5a+15* 9
5a=565
a=113
Y=113+9X. For year 2013, X will be 10.
Y 2013= 113 + 9 * 10
= 203 watches
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