Methods of Demand

Forecasting

Demand ForecastingDemand forecasting is the activity of estimating the quantity of a product or

service that consumers will purchase

Demand forecasting involves techniques including both informal

methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets.

Demand forecasting may be used in making pricing decisions,

in assessing future capacity requirements, or in making

decisions on whether to enter a new market.

Steps in Demand Forecasting

Specifying the objective

Determining the time perspective

Making choice of method for demand forecasting

Collection of data and data adjustment

Estimation and interpretation of results

Techniques in Demand ForecastingDescription Qualitative Approach Quantitative Approach

Applicability Used when situation is vague & little data exist (e.g., new products and technologies)

Used when situation is stable & historical data exist(e.g. existing products, current technology)

Considerations Involves intuition and experience

Involves mathematical techniques

Techniques Jury of executive opinionSales force compositemethodConsumer market survey

Time series modelsCausal models

Qualitative methods

I. Jury of Executive Opinion

Very popular

Solicits the opinions of a group of managers on expected sales

Combining them into a sales forecast

Advantages• Expeditious method• It permits factors like economic

climate , consumer preferences to be considered.

• Appeals those managers who tend to prefer their judgment as compared to mechanistic procedures

Disadvantages• The biases underlying the subjective

estimates.• The reliability is questionable.

ii. Delphi techniqueA group of experts is sent a questionnaire to express their views.

Responses are summarized without disclosing the identity of the experts

Sent back to experts along with questionnaire to probe further.

The process is continued to one or two rounds till a reasonable agreement emerges in the view of experts.

Advantages• Intelligible to users• Seems to be more accurate and less

expensive than the traditional face to face group meetings

Disadvantages• Value of expert opinion is

questionable• Contribution of additional rounds

and feedback to accuracy is questionable

iii. Sales Force Composite Method

Each salesperson (for example for a territorial coverage) is asked to project their sales.

Since the salesperson is the one closest to the marketplace, he has the capacity to know what the customer wants.

These projections are then combined at the municipal, provincial and regional levels.

Time Series Projection Methods

I. Trend Projection Methods

Classical method of business forecasting

Concerned with study of movement of variables through time

Requires a long and reliable time-series data

Assumes that factors responsible for the past trends in the variable to be projected will continue to play their part in future in same manner and magnitude.

Three techniques-Graphical method, Least square method, Box-Jenkins method.

a) Graphical method

Annual sales data is plotted on graph paper and a line is drawn through the plotted points.

Then a free hand line is so drawn that the total distance between line and the points is minimum

Both the lines are extended ,where they meet is the point of forecasted sales for that year

Graphical method

2001 2002 2003 2004 2005 2006 2007 2008 2009 20100

5

10

15

20

25

30

PO

C

N

M S

2011

b) Least square method

A trend line is fitted to the time –series sales data with the aid of statistical techniques

The form of the trend equation that can be fitted to the time-series data is determined either by plotting the sales data or by trying different forms of trend equations for the best fit.

Linear trend: when a time series data reveals a rising trend in sales, then a straight line equation is formed.

Exponential trend: when the total sale has increased over the years at an increasing rate or at a constant percentage rate per time unit, then trend equation to be used is an exponential trend equation.

• Linear trendTrend equation to be fitted : S= a + bT where S = annual sales

a= intercept of relationshipb= slope of relationshipT= time variable

The co-efficients a and b are estimated by solving the following two equations based on the principle of least square

∑S = na + b∑T ∑ ST=a∑T +b∑T2

YEAR SALES T T ST

2001 10 1 1 10

2002 12 2 4 24

2003 11 3 9 33

2004 15 4 16 60

2005 18 5 25 90

2006 14 6 36 84

2007 20 7 49 140

2008 18 8 64 144

2009 21 9 81 189

2010 25 10 100 250

n = 10 ΣS = 164 ΣT =55 ΣT = 385 ΣST =1024

ESTIMATION OF TREND PROJECTION

2

2

• By substituting numerical values ,we get 164= 10 a +55 b ①

1024= 55 a+ 385 b ②• By solving 1 and 2 ,we get the trend equation

as : S= 8.26 +1.48 TSo for 2011,2012,2013 the estimates can be 2011 S= 8.26 +1.48(11) =24,540 tonnes 2012 S= 8.26+ 1.48(12)= 26,020 tonnes 2013 S= 8.26+ 1.48(13)= 27,500 tonnes

ii. Moving Average Method

The forecast for the next period is equal to the average of the sales for several preceding years.

• Where Fʈ +1= forecast for the next period• Sʈ= sales for the current period• n= period over which averaging is done

Fʈ +1= Sʈ + Sʈ-1+……+Sʈ-n+1 n

t Data(S1) Forecast for t + 1

1 28

2 29

3 28.5

4 31

5 34.2 f5=(28+29+28.5+31)/4= 29.1

6 32.7 f6=(29+28.5+31+34.2)/4=30.7

7 33.5 f7=(28.5+31+34.2+32.7)/4=31.6

8 31.8 f8=(31+34.2+32.7+33.5)/4=32.9

9 31.9 f9=(34.2+32.7+33.5+31.8)/4=33.9

Causal methods

I. Chain ratio method

The potential sales of a product may be estimated by applying a series of factors to a measure of aggregate demand.

• Total amount of coffee sales : 174.5 million units• Proportion of coffee used at home: 0.835• Coffee used at home :145.7 million units• Proportion of non-decaffeinated coffee :0.937 used at home• Non- decaffeinated Coffee used at home:136.5 million units• Proportion of instant coffee:0.4• Instant Non- decaffeinated Coffee used at home:54.6 million units• Estimated long run market share for maxim:0.08• Potential sales of maxim: 4.37 million units

example- General Foods estimated the sales for a new product a freeze fried instant coffee

• ii. Consumption level method

For products who are directly consumed

Basis of elasticity coefficients

Income elasticity: E1={(Q2-Q1)/(I2-I1)}{(I1+I2)/(Q2+Q1)} where E1= income elasticity of demand,Q1=quantity demanded in the base year,Q2= quantity demanded in following year,I1=income level in the base year,I2= income level in following year

• End use method

Estimating demand for intermediate products

Disadvantages-1) difficult to estimate the projected output levels of consuming industries

2) The consumption coeffients may vary from one time period to another

• iii. Leading indicator method

Leading indicators are variables which change ahead of lagging variables

Observed changes in leading predicts changes in lagging

Example: The change in level of urbanization helps to predict the change in the demand for ACs

The merit-it does not require to forecast of an explanatory variable.

The limitations- 1) difficult to find appropriate leading indicator and secondly lead lag relationship may not be stable over time .

iv. Econometric method

Mathematical representation of economic relationship derived from economic theory.

The primary objective is to forecast the future behaviour of economic variables

Two types economic models: single and simultaneous equation models

The single equation model assumes that one variable ,the dependent variable(explained variable) ,is influenced by one or more independent variable

STEPS IN CONSTRUCTION AND USE OF AN ECONOMETRIC MODEL

Advantages• Sharpens understanding of complex

cause-effect relationship• Provides basis for testing

assumption and for judging how sensitive the results are to changes in assumptions

Disadvantages• Expensive and data- demanding• First one needs to project values for

independent variable

Uncertainties in demand forecasting

Data about past and present markets

Lack of standardisation

Few observations

Influence of abnormal factors

Thank you

By-Tanu mehtaGarima

Anckupriya Vij