Unit -Iii_demand Forecasting

8/8/2019 Unit -Iii_demand Forecasting

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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:

Documents

Unit -Iii_demand Forecasting