pt session 4. demand forecasting

8/7/2019 PT Session 4. Demand Forecasting

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IV. DEMAND FORECASTINGSource: Geetika, Salvatore


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Meaning

A tool to scientifically predict the likelydemand for a product for a particular

period of time in futureCategorization can be:By level of forecasting - firm, industry, andeconomy(macro) levelBy time period- short and long termBy nature of goods

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Demand Forecasting

To get an overview of the market and actproactively

To adjust production and avoid over production and under productionEssential for production scheduling,purchase of raw materials, arrangingfinance and advertising


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Q ualitative/ Subjective Methods

Used for short term forecasts when dataare not available

Also for supplementing quantitativeforecasts

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1. Consumers Opinion Surveys2. Sales Force Composite Method

3 . Expert Opinion- Group discussion andDelphi methods

4. Market Simulation

5. Test Marketing

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Surveys on economic intentions canreveal and can be used to forecast future

purchase of capital equipment, inventorychanges and major consumer expendituresComplete enumeration (census) vssampleQ uestionnaire, interview


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Buying Intentions of consumers has limited usebecause

- 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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Sales Force Survey- Easy, cost effective,reliable, ideal for short term forecasts

ButMay be biasesSales force may not have knowledge of

macro factors

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Expert Opinion Method :Group Discussion- Market consultants,

industry analysts with knowledge of product and the market conditions- P ersonal insights can be subjective

- To avoid the problem of dominantpersonality, Delphi method


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Delphi method: Developed by Rand Corporation in19 40s- arriving at consensus

- Anonymity

-Wide expertise-Effective when there is no urgencybut

-Difficulty in getting panelists-Requires understanding, skill and knowledge for

conceptualising, stimulating discussion andmaking inferences by researcher .


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But very costly as it requires actualproduction of the product

Time consumingMay be difficult to extrapolate in a largecountry like India with heterogeneouspopulation

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Q UANTITATIVE METHODS

1. Time Series Method- Nave forecastingVariables change with timeSources of variation in Time series :

Secular TrendSeasonal Changes

Cyclical FluctuationsRandom or irregular fluctuations

Total variation , say in sales, is the result of all four

factors operating together .


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Time Series Method

Trend ProjectionSimplest- projecting the past trend by

fitting a straight line to the data either visually or more precisely throughregression

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L east Squares Method

L east Squares Method- Most widely used timeseries method

Linear Equation of a straight line isY = 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 Yfor any value of X requires the values of a and b,for which 2 normal equations are prepared:


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L east Squares Method

Y = na + b X

XY=a X + b X2With 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:In this method, 3 (or 5) yearly / 3 (or 5) monthly/ 3 (or 5) day

moving averages of the required series is obtained. Themethod is used in progression by ignoring the first entryand incorporating the next one. A verage value of the last 3 (or 5) entries becomes theforecast for the next period

The best forecast is chosen with the help of Root MeanSquare Error (RMSE).

RMSE is an Exponential smoothing method .


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Time Series Method

Simple moving average gives equal weightto all observations, even though morerecent observations are likely to be moreimportant.Exponential smoothing overcomes thisproblem.

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B arometric ForecastB arometric Forecast :- When data indicates cyclical fluctuations, this

method alerts businesses to changes in overall

economic conditions- To predict short term changes in economic

activity or turning points

- Barometric forecasting is done by NBER andthe Conference Board


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B arometric Forecast

Related variables are categorised into 3 groups:

- L eading variables : Those that changebefore the actual change

- Coincident Variables : Change along withvariable

- L ag variables : Follow the event- If leading variables are identified, easy to

predict actual variables

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Barometric Forecast

PEAK

Time

C. Lagging

variable

TroughPEAK


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L eading indicators :Building permits, new private housing unitsNumber of loan applicationsNew orders for durable goods for their components and raw materialsIndex of consumer expectations

Stock prices(Population growth for demand for schooling,Original purchase of durable goods for

replacement demand)


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Coincident indicators :Rate of unemployment

GDPIndustrial productionManufacturing and trade sales


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L agging Indicators :Commercial and industrial loans

outstandingChange in consumer price index for services


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Merits:Barometric forecasting is 80 to 9 0% successfulin forecasting turning points in economic activity.Method aligns macro with micro economicsLess costly as it mostly depends on past data

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Problems:Can Only be used for short term forecasting

It is not always possible to identify or get data ona leading variable for the variable to be forecastVariability in lead time may be considerableThe method cannot predict the magnitude of the

changes.;Therefore, barometric forecasting should be used

in conjunction with other methods

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Econometric Modelling for

ForecastingIdentifying and measuring the relationshipCan be single variable or multivariate regressionmodelSingle equation models for a firms demand;Large multiple equation models for the entireeconomy

Identify relevant determinants ( explanatory/independent variables) of the variable to beforecastGet data and fit to equation to get coefficients


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D= a 0+ a 1Y+ a 2 Px+a 3 Py+a 4 A+Not subjective like the qualitative methods

Based on causal relationships andproduces accurate resultsMethod is consistent

Forecasts both direction and magnitude of change


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Uses complex calculationsCostly and time consuming


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Problems in Demand

ForecastingInadequate analysis of the market- includeall potential users of a product

Unforeseen eventsChanging fashions and tastesConsumer Psychology

Lack of expertiseLack of adequate data

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Sums in Forecasting

Data for demand for watches for 5 years isgiven, estimate demand for 201 3 :

Year 2004 2005 2006

2007

2008No 120 1 3 0 150 140 1 6 0


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Sum in Forecasting

Year X Y X 2 Y2 XY

2004 1 120 1 14400 120

2005 2 1 3 0 4 1 69 00 2 6 0200 6 3 150 9 22500 450

200 7 4 140 1 6 196 00 5 6 0

2008 5 1 6 0 25 25 6 00 800

Total 15( X)n= 5

700 ( Y)

55( X2 )

99 000 2190 ( XY)


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Sums in ForecastingNormal equation: Y= a + bX (i)

Y= na + b X (ii) XY =a X + b X2 (iii)7 00= 5a + 15b (iv) 21 9 0=15a + 55b(v)

Solving (iv) and (v) we get,10 b= 9 0 b =9; Substituting the value of b in (iv)

7 00=5a+15* 9 5a=5 6 5 a= 113

Y=113

+9

X. For year 2013

, X will be 10.Y 201 3 = 11 3 + 9 * 10= 20 3 watches

pt session 4. demand forecasting

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