unit -iii_demand forecasting
TRANSCRIPT
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DEMAND FORECASTING
UNIT - III
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SO
WHAT
IS
DEMAND
FORECASTING?
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Demand ForecastingDemand forecastingis a technique of
predicting or estimating demand in future on
the basis of the behaviour of factors which
affect the demand.
It is just not simple guessing game but it
involves use of various scientific techniquesplus proper judgement & acumen on the parts
of decision-makers.
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Demand forecasting is a specific type offorecasting, which enables the manager to
minimize elements of risk and uncertainty The likely future event has to be given form and
content in terms of projected courses of variable,i.e. is forecasting.
The manager can conceptualize the future indefinite terms.
Forecasting customer demand for productsand services is a proactive process of
determiningwhat products are neededwhere, when, and inwhat quantities.Consequently, demand forecasting is acustomerfocused activity.
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Factors involved in Demand Forecasting1. How far ahead
i) Short run
ii) Long run
2. Undertaken at three levels:
a. Macro-level
b. Industry level eg., trade associations
c. Firm level
3. Should the forecast be general or specific (product-wise)?
4. Problems or methods of forecasting for new vis--vis well
established products.5. Classification of products producer goods, consumer durables,
consumer goods, services.
6. Special factors peculiar to the product and the market risk and
uncertainty. (eg., ladies dresses, political stability)
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Features It is in terms of specific quantities
It is undertaken in an uncertain atmosphere.
A forecast is made for a specific period of time whichwould be sufficient to take a decision and put it into action.
It is based on historical information and the past data.
It tells us only the approximate demand for a product in thefuture.
It is based on certain assumptions.
It cannot be 100% precise as it deals with future expecteddemand
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Significance of Demand Forecasting Production Planning.
Sales forecasting.
Control of business. Inventory control.
Pricing Policy
Stability.
Labor requirement Growth and long-term investment programmes.
Economic planning and policy making
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SCOPE OF DEMAND
FORECASTINGLevels of forecasting
-- Macro level
-- Industry level-- Firm level
In macro level, it takes into account the aggregatessuch as NI, expenditure, IIP etc., while estimatingdemand.
At industry level, the forecasting is made for the wholeindustry.
At the firm level it involves forecasting the firms
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DETERMINANTS FOR DEMAND
FORECASTING
1. Capital goods Goods required for furtherproduction of goods
Demand for capital goods is deriveddemand
- Replacement demand
- New demand
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2. Durable consumer goodsGoods usedcontinuously for a period of time
1. Buy vs. Replacement decision
2. Family Characteristics
3. Special attached facilities
4. Prices & credit facilities
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3. Non-durable consumer goods
Commodities which are used in a singleact of consumption
Demand for these goods is
influenced by- Disposable income ofpeople/Purchasing Power
- Price of the commodity
- Size and characteristics of population/Demography
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LENGTH OF FORECASTING
1. Short Term: up to 12 months, determine salesquota, inventory control, production schedules,budgeting & planning cash flows.
2. Medium Term: from 1 2 years, determinethe rate of maintenance, schedule of operation &budgetary control over expenses.
3.L
ong Term: from 3 10 years, determineapital expenditures, personnel requirement,financial requirements, raw materialrequirements & scope of R&D programmes.
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Short-term Forecasting Purposes: Production scheduling
Evolving a sales policy.
Determining price policy. Evolving a purchase policy of raw material.
Fixation of sales targets & incentives.
Determining Short-term Financial Planning. Evolving suitable advertising & promotion
program
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Long-Term Forecasting Business Planning for new unit or
expansion of an existing unit.
Man powerPlanning.
Long-term financial planning.
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CRITERIA FOR GOOD DEMAND
FORECASTING
1. Accuracy
2. Plausibility
3. Durability
4. Availability
5. Economy6. Simplicity & ease of comprehension
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FORECASTING DEMAND FOR
NEW PRODUCTS
Joel Dean suggested the followings for
forecasting demand for new products.
Project the demand for the new product as
an outgrowth of an existing old product
Analyse the new product as a substitute for
some existing old product
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Estimate the rate of growth & ultimate level of
demand for the new product on the basis of the pattern
of growth of established products
Estimate the demand by making direct enquiries from
the ultimate purchasers, either by the use of samples
or on a full scale.
Offer the new product for sale in a sample market.
Survey consumer reaction to a new product indirectly
through the eyes of specialised dealers who are
supposed to be informed about consumers need &
alternative opportunities.
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PRESENTATION OF A
FORECAST TO THE MGMT1. Make the forecast as easy for the mgmt to
understand as possible
2. Avoid using vague generalities
3. Always pin point major assumptions & sources
4. Give the possible margin error
5. Avoid making undue qualification
6. Omit details about methodology and calculation7. Make use of charts and graphs as much as
possible for easy comprehension.
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METHODS OF DEMAND
FORECASTING
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CONSUMER SURVEY METHOD Least sophisticated method
Customers are directly contacted to find out theirintentions to buy commodities in the near future
usually for one year.
Most useful when bulk of the sales is made to the
industrial producers.
Intentions recorded through personal interviews, mail
or post service, telephone interviews and
questionnaires. Three types of Consumer Survey
Complete Enumeration Method
Sample Survey Method
End use Method or Input-Output
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Sales Force Opinion Method The salesmen are questioned & their response or
reactions aggregated.
This method is very cheap & easy. It has the advantage of first hand knowledge of the
salesmen.
This method is quite useful for forecasting
demand for new products. This method is otherwise known as Reaction
survey method.
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Disadvantage of Sales force Opinion Salesmen could generally understand the
situations only near-future forecasting, therefore it
is useful for a short period. Salesmen are ignorant of broader economic
changes in the market which has to consider while
forecasting.
Salesmen can be affected by either by congenital
optimism or congenital pessimism.
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EXP
ERT OP
INION Here experts in the field has been asked for
estimating their likely sales.
Experts include executives directly involvedin the market, such as distributor, dealers,suppliers, industry analyst, specialist mktgconsultants, trade associations officers.
Each expert is asked independently toprovide confidential estimate & resultscould be averaged.
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DELPHI METHOD
It is developed at Rand Corporation of theUSA in the late 1940s.
It was developed by Olaf Helmer, Dalkey,
& Gordon The forecasters are given the forecasts and
assumptions of other experts, and a final
report is compiled with the combined
consensus of the experts.
It is more popular in forecasting non-
economic rather than economic variables
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Advantages:
Facilitates the maintenance of anonymity of
the respondents identity through out the
course.
This technique saves time & other resources
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MARKET SURVEY METHOD
CONTROLLED EXPERIMENTSDifferent determinants of demand are varied and price
quantity relationships are established at different points of time
in the same market or different markets.
Only one determinant varied ; others kept constant.
SIMULATED MARKET SITUATION
An artificial market situation is created and consumer
clinics selected. Consumers are asked to spend time in an
artificial departmental store and different prices are set for
different buyer groups.
The responses to the price changes are observed and
necessary decisions taken.
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Limitation of Controlled
Experiments
Expensive & time consuming
Risky because they may lead to unfavorable
reaction on dealers, consumers, &
competitors
Difficulty in planning the study
Difficult to satisfy the condition of
homogeneity of market.
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Fitting TrendL
ine by Observation It is easy and quick method to project the demand.
It involves the plotting of annual sales on a graph
and then estimating just by observation where thetrend line lies. The line can be simply extended to
a future period and corresponding sales forecast
read against that year.
As this methods lacks scientific temper, it could
not able to estimate in detail.
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L
east Square Method Based on analysis of past sales patterns
Shows effective demand for the product for
a specified time period
The trend is estimated by using the Least
Square Method.
This system of forecasting is considered
naive because it doesnt explain the
reason for the change.
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A producer of soaps decides to forecast
the next years sales of his product.
The data for the last five years is as
follows:
YEARS SALES INRs.LAKHS
1996 45
1997 52
1998 48
1999 55
2000 60
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The data is plotted on a graph:
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The equation for the straight line trend is
Sales = a + b T (or year no.)
Where a & b are constants representing intercept
& slope respectively.
To determine value of a & b, following twoequations need to be solved.
S = Na + b T (Eq.1)
ST = a T + b T2 (Eq.2)
Where N is no of years, months etc for which data is available
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Substituting the above values in the normal equations:
260=5a +15b (Eq.3)
813=15a + 55b (Eq.4)
solving the two equations,
a = 42.1 , b = 3.3
YEARS SALESRs.
LAKHS (S)
T T2 ST
1996 45 1 1 45
1997 52 2 4 104
1998 48 3 9 144
1999 55 4 16 220
2000 60 5 25 300
N=5 S=260 T=15 T2=55 ST=813
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Therefore, the equation for the straight line
trend is
S=42.1 + 3.3TUsing this equation we can find the trend values for
the previous years and estimate the sales for theyear 2001 as follows:
Thus, the forecast sales for year 2001 is Rs.61.9 lakhs.
Y 1996 = 42.1+3.3(1) = 45.4
Y 1997 = 42.1+3.3(2) = 48.7
Y 1998 = 42.1+3.3(3) = 52.0
Y 1999 = 42.1+3.3(4) = 55.3
Y2000
=
42.1+3.3(5)=
58.6Y 2001 = 42.1+3.3(6) = 61.9
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MOVING AVERAGES METHOD A moving average is an average that is updated or recomputed
for every new time period being considered. Each MA is based on values covering a fixed time interval,
called period of moving average & is shown against the
centre of the period.
When period of MA is odd, the successive values of movingaverages are placed against the middle value of concerned
group of times. For example, if n=7 the first moving average
value is placed against middle period i.e. 4th value; the second
MA value is placed against time period 5 & so on.
When period of MA is even, then there are two middle
periods. By using centering technique, a two period avg of the
moving avg is taken & place them in between the
corresponding time period.
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YEAR SALES IN
Rs.LAKHS
1993 12
1994 15
1995 14
1996 161997 18
1998 17
1999 19
2000 20
2001 22
2002 25
2003 24
These are the annual sales
of goods during the period
of 1993-2003.
We have to find out thetrend of the sales using (1)
3 yearly moving averages
and (2) 4 yearly moving
averages and forecast thevalue for 2005.
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3 yearly period:The value of 1993 + 1994 +1995
12 +15+14 = 41 written at the capital period 1994 of the years
1993, 1994 and 1995YEAR SALES (Rs.
LAKHS)
3YEARLY
MOVING
TOTAL
3YEARLY
MOVING
AVG. TREND
VALUES
1993 12 - -94 15 41 41/3= 13.7
95 14 45 45/3= 15
96 16 48 48/3 =16
97 18 51 51/3 =17
98 17 54 54/3 = 18
99 19 56 56/3 = 18.7
2000 20 61 61/3 = 20.2
01 22 67 67/3 = 22.3
02 25 71 71/3 = 23.7
03 24 - -
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4 YEARLY MOVING AVERAGES
YEAR. SALES (Rs.
LAKHS)
4YEARLY
MOVING TOTAL
MOVING TOTAL
OF PAIRS OF
YEARLYTOTAL
4YEARLY
MOVINGAVG.
TREND VALUES
93 12 - - -
94 15 - - -
95 14 120 120/8 = 15
96 16 128 128/8 = 16
97 18 135 135/8 = 16.998 17 144 144/8 = 18
99 19 152 152/8 = 19
00 20 164 164/8 = 20.5
01 22 177 177/8 = 22.1
02 25 - -
03 24 - - -
57 = 93 + 94 +95 + 96 = 12 + 15 + 14 + 16
120= 57 +63, 128 = 16 +65 and so on.
120 is total of 8 years and so the avg. is calculated by dividing 120 by 8
57
63
65
70
74
78
86
91
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The trend values from the previous tables can be
plotted on a graph as follows:
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4 months moving averageMonths SALES IN
Rs.LAKHS
Jan 1056Feb 1345
Mar 1381
April 1191
May 1259
Jun 1361
Jul 1110
Aug 1334
Sept 1416
Oct 1282
Nov 1341
Dec 1382
4 months moving average
= (1056+1345+1381+1191)/4
= 1243.25
Which will be the forecast for
the month of May
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Months SALES IN
Rs.LAKHS
Average Error
Jan 1056Feb 1345
Mar 1381
April 1191
May 1259 1243.25 15.75
Jun 1361 1294 67
Jul 1110 1298 -188
Aug 1334 1230.25 103.75
Sept 1416 1266 150
Oct 1282 1305.25 -23.25
Nov 1341 1285.5 55.5
Dec 1382 1343.25 38.75
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Exponential Smoothing It is used to weight data from previous time
periods with exponentially decreasing importancein the forecast.
It is one of the popular approach for short termforecasting.
Weight assigned to each value reflects degree ofimportance of that value.
More recent values being more relevant forforecasting, these are assigned greater weight thanprevious period values.
Weights (w) lies between zero & one.
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F t+1 = w. Xt + (1-w) . Ft
Where
F t+1 the forecast for next time period t+1
F t the forecast for current time period t
X t the actual value of present time period
w a value between 0 < w
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REGRESSION ANAL
YSIS Relationship is established between quantity
demanded being dependent variable and one or
more independent variable such as income, priceof the related goods, price of the commodity under
question, advertisement cost, etc.
Based on this relationship, the demand trends are
forecasted. It can also used when we have more than one
independent variables.
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Principal advantage of this method is that
besides demand forecast, it explains why
demand has been at the level it is.
It is neither mechanistic like trend method
nor as subjective as the expert opinion
survey method.
Usually, time series data is used, but we
may use cross section data also.
As this method is also based on past data,the forecast will be unrealiable.
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REGRESSION METHODMethod ofLeast Squares
From the above data we can project the sales for 2010, 2011, 2012.
First we calculate the required values which are (i) Time Deviation,
(ii) Deviation Squares, (iii) Product of time deviation and sales.
YEAR 2005 2006 2007 2008 2009
SALES(Rs. In
crores)
240 280 240 300 340
YEAR (n) SALES (RS.
CRORE) (y)
TIME
DEVIATION
FROM MIDDLE
YEAR2007 (x)
TD SQUARED (x2) PRODUCT OF
TIME
DEVIATION &
SALES (xy)
2005 240 -2 4 -480
2006 280 -1 1 -280
2007 240 0 0 0
2008 300 +1 1 +300
2009 340 +2 4 +680
n = 5 y = 1400 x = 0 x2
= 10 xy = 220
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The equation is
Y= a + bx
a independent variableb exhibits rate of growth
a & b can be found out as follows:
a = y / n = 1400 / 5 = 280
b = xy / x2 = 220/10 = 22Now, applying values to the regression equation,
Y = 280 + 22x
Hence, sales projection from 2010-2012 can be ascertained.
2010 = 280 + 22 (3) = Rs.346 crores2011 = 280 + 22 (4) = Rs.368 crores
2012 = 280 + 22 (5) = Rs.390 crores
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Method of Simple linear Regression
The linear trend can be fitted in the equation
Sales = a + b (Price)
i.e. S=
a + bP
where in, a and b are constants.
b=
nSi Pi- (Si)(Pi)nPi2 (Pi)
2
a = Si - b Pin
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e.g. fit a linear regression line to the following data &
estimate the demand at price Rs.30
YEAR 81 82 83 84 85 86 87 88 89 90 91 92
PRICE
(Pi)15 15 12 26 18 12 8 38 26 19 29 22
SALES
(Si) in1000 units
52 46 38 37 37 37 34 25 22 22 20 14
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To find the values of a and b the following table is
constituted:Pi Si Pi2 Si2 Si Pi
15 52 225 2704 780
15 46 225 2116 690
12 38 144 1444 456
26 37 676 1369 962
18 37 324 1369 666
12 37 144 1369 444
8 34 64 1156 272
38 25 1444 625 950
26 22 676 484 572
19 22 361 484 418
29 20 841 400 580
22 14 484 196 308
Pi = 240 Si = 384 Pi2 = 5708 Si
2 =
13716
Si Pi =
7098
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b = nSi Pi- (Si)(Pi) = 12(7098)-(240)(384) = 0.641
nPi2
(Pi)2
12 (5708)-(240)2
a = Si - b Pi = [384-(240)(-0.641)] = 44.82
n 12
Thus the regression line is S= 44.82 - 0.641P
By assigning value 30 to P,
The corresponding sales level is
S=
44.82 0.641 (30)=
25.29 thousand units
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BAROMETRIC METHOD
Improvement over trend projection method
Events of the present are used to predict future
demand Basic approach- constructing an index of relevant
economic indicators
Leading indicators
Coincident indicators
Diffusion indices
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Simultaneous Equation Method
It involves the development of a completemodel which can explain the behaviour of all
the variables which the firm can control.
The number of equations equals the number ofdependent variables.
After the model is developed, it is estimated
through some appropriate method such as the
Least Square Method.
The model is then solved for each of variables.
It is very costly & time consuming.
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ARIMA Method
(Auto Regressive Integrated Moving Average)
Otherwise known as Box-Jenkin Technique.
This method combines smoothing method
with auto regressive method.
Used for short term forecasting.
There are five stages of analysis in the
method
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Five Stages of Analysis
Removal of the Trend
Model Identification
Parameter Estimation
Verification
Forecasting
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3. Parameter Estimation: Once a particularcombinations of the three elements is
identified, the method of least square isused to fit this model to the time series.
4. Verification: the goodness of the fit of the
estimated model is checked by analysingthe residuals it generates. If the residualsdont show any specific pattern is good fit.If it is not good fit, we need to repeat the
process by starting afresh from stage 2 &try to develop a new group ofcombinations
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5. Forecasting: