Managerial Economics

Demand Forecasting

Demand Forecasting It means expectation about future course of

the market demand for a product based on statistical data about past behavior and empirical relationships of demand determinants

Types: Short term Long term Passive & Active Forecasts

Short Term Forecasting It normally relates to a period not exceeding a

year Benefits of Short term forecasting

Evolving a Sales Policy Determining Price Policy Fixation of Sales Target

Long Term Forecasting It refers to the forecasts prepared for long

period during which the firm’s scale of operations or the production capacity may be expanded or reduced Benefits of Long term forecasting Business Planning Manpower Planning Long-Term Financial Planning

Factors involved in Demand Forecasting

Undertaken at three levels:a. Macro-levelb. Industry level eg., trade associationsc. Firm levelShould the forecast be general or specific (product-

wise)?Problems or methods of forecasting for “new” vis-à-vis

“well established” products.Classification of products – producer goods, consumer

durables, consumer goods, services. Special factors peculiar to the product and the market –

risk and uncertainty.

Criteria of a good forecasting method

1. Accuracy – measured by (a) degree of deviations between forecasts and actuals, and (b) the extent of success in forecasting directional changes.

2. Simplicity and ease of comprehension.3. Economy.4. Availability.5. Maintenance of timeliness.

Presentation of a forecast to the Management

1. Make the forecast as easy for the management to understand as possible.

2. Avoid using vague generalities.

3. Always pin-point the major assumptions and sources.

4. Give the possible margin of error.

5. Omit details about methodology and calculations.

6. Make use of charts and graphs as much as possible for easy comprehension.

Various macro parameters found useful for demand forecasting

1. National income and per capita income.

2. Savings.

3. Investment.

4. Population growth.

5. Government expenditure.

6. Taxation.

7. Credit policy.

Significance of Demand Forecasting Production Planning Sales Forecasting Control of Business Inventory Control Growth and Long Term Investment Program Economic Planning and Policy Making

Sources of Data Primary: which are collected for first time for

purpose of analysis Secondary : are those which are obtained

from someone’s else records

Survey Methods

Consumer Survey Opinion PollMethods

Complete Enumeration

SampleSurvey

ExpertOpinion

Market Studies&

Experiments

DelphiMethod Market Test

LaboratoryTest

Techniques of Demand Forecasting

Techniques of Demand ForecastingStatistical Methods

Time series analysisEconometric

Methods

Smoothing techniques

Least Square Method

Regression Method

Simple

Multivariate

Consumption level

method

Consumer Survey Methods Complete enumeration Method: All potential users of

product are contacted and are asked about their future plan of purchasing the product in question

Limitations Very expensive in case of widely dispersed market Consumers may not know their actual demand and may

br unable to answer query Their plans may change with a change in factors not

included in questionnaire

Contd… Sample Survey: Only a few potential

consumers and users selected from relevant market are surveyed

Method is simpler, less costly and less time consuming.

Surveys are done to understand market demand, tastes ad preferences, Consumer expectations etc

Opinion Poll Method Aim at collecting opinions of those who are

supposed to possess the knowledge of the market e.g sales representatives, sales executives, consultants and professional marketing experts

This method includes Expert opinion Delphi method

Expert opinion Under this method each expert is asked independently to

provide a confidential estimate and results could be averaged.

Experts may include executives directly involved in the market such as suppliers, distributors or dealers or marketing consultants, officers of trade association etc.

Advantage is that there is no danger that group of experts develop a group- think mentality. Moreover, forecasting is done quickly and easily without need of elaborate need of statistics.

Delphi Method

This method is an attempt to arrive at a consensus on some issues by questioning a group of experts repeatedly until the responses appear to converge along a single line or the issues causing disagreement are clearly defined.

Generally a panel consisting 9 to 12 experts A coordinator is required for the process

Market Experimentation

Test marketing A test area is selected, which should be a representative of the

whole market in which the new product is to be launched. A test area may include several cities having similar features i.e.

population, income levels, cultural and social background, choice and preferences of consumers

Market experiments are carried out by changing prices, advertisement expenditure and other controllable variables influencing demand

After such changes are introduced in the market, consequent changes in demand over a period of time are recorded.

Contd… Experiments in laboratory or consumer clinic

method Under this method consumers are given some money to

buy in a stipulated store goods with varying prices, packages, displays etc.

They are also requested to fill a questionnaire asking reasons for the choices they have made

The experiment reveals the consumers responsiveness to the changes made in prices, packages and displays.

Limitations of market experiment methods

Very expensive Being costly, carried out on a scale too small to permit

generalization with a high degree of reliability Based on short term and controlled conditions which

may not exist in an uncontrolled market Tinkering with price increases may cause a permanent

loss of customers to competitive brands

Types of data used in Statistical methods Time series data refer to data collected over a

period of time recording historical changes in price , income and other relevant variables influencing demand for a commodity

Cross sectional analysis is undertaken to determine the effects of changes like price, income etc on demand for a commodity at a point in time

Types of Statistical Methods Consumption level Method Time series Analysis (Trend Projection) Smoothing Techniques

Moving Averages Least Squares Method Exponential Smoothing Technique

Econometric Method Barometric Method

Consumption Level Method Under this method consumption level method may

be estimated on basis of co-efficient of Income elasticity and price elasticity of Demand

D* = D(1+M*.e) D* =Projected per capita demand D= Actual Per capita Demand M*= Percentage change in per capita income/price E=elasticity of demand

IllustrationSuppose Income elasticity of demand for chocolates is 3. In year 1995 per capita income is $500 and per capita annual demand for chocolates is 10 million in a city. It is expected that in year 2000 per capita income will increase by 20 % . Then projected per capita demand for chocolates in 2000 will be?

Time Series Analysis• It attempts to forecast future values of time series by examining

past observations of data• The time series relating to sales represent the past pattern of

effective demand for a particular product. Such data can be presented either in a tabular form or graphically for further analysis.

• The most popular method of analysis of the time series is to project the trend of the time series.a trend line can be fitted through a series either visually or by means of statistical techniques.

• The analyst chooses a plausible algebraic relation (linear, quadratic, logarithmic, etc.) between sales and the independent variable, time. The trend line is then projected into the future by extrapolation.

Time Series Analysis Popular because: simple, inexpensive, time series

data often exhibit a persistent growth trend. Disadvantage: this technique yields acceptable

results so long as the time series shows a persistent tendency to move in the same direction. Whenever a turning point occurs, however, the trend projection breaks down.

The real challenge of forecasting is in the prediction of turning points rather than in the projection of trends.

Time Series Analysis Reasons for fluctuations in time series data

Secular Trend : value of a variable tends to increase or decrease over a period of time

Cyclical Fluctuations are major expansions and contractions that seem to recur every several years

Seasonal variation refers to regularly recurring fluctuation in economic activity during each year

Irregular influences are variations in data series resulting from wars, natural disasters or other unique events

Four sets of factors: secular trend (T), seasonal variation (S), cyclical fluctuations (C ), irregular or random forces (I). O (observations) = TSCI

Trend Projection Simplest form of time series analysis is projecting

trend based on assumption that factors responsible for past trends in variable to be projected will remain same in future.

Trends refer to long term persistent movement of data in one direction-increase or decrease

Trend component of time series is the overall direction of the movement of the variable over a long period.

Reasons for studying Trends Studying secular trends permits us to project past

patterns, or trends, into the future In many situations studying the secular trend of a time

series allows us to eliminate the trend component from the series.

Methods for trend Projections: Least squares method

Smoothing Techniques Moving Average Exponential smoothing

Moving average Method This method assumes that demand in future year

equals the average of demand in past years Under this method 3 yearly,4 or 5 yearly etc moving

average is calculated by moving total of values in group of years(3,4,5)is calculated, each time by ignoring first entry and incorporating last one

For Three period Moving average the forecasted value of time series for next period is average value of previous three periods in time series

Moving average Method In order to decide which of these moving averages

forecasts is better ie closer to actual data root-mean-square-error (RMSE) is calculated for each forecast and using moving average that results in smaller RMSE

The greater the number of periods used in moving average the greater is the smoothing effect because each new observation receives less weight. Useful when time series data is more erratic.

Three-quarter Moving Average forecastsQuarter Firm’s Actual

Market Share (A)Three Quarter

Moving Average Forecast (F)

A-F (A-F)2

1 20 - -

2 22 - -

3 23 - -

4 24 21.67 2.33. 5.4289

5 18 23.00 -5.00 25

6 23 21.67 1.33 1.7689

7 19 21.67 -2.67 7.1289

8 17 20.00 -3.00 9

9 22 19.67 2.33 5.4289

10 23 19.33 3.67 13.4689

11 18 20.67 -2.67 7.1289

12 23 21.00 2.00 4

13 - 21.33 Total= 78.3534

Five Quarter Moving Average forecastsQuarter Firm’s Actual

Market Share (A)Fiv3 Quarter

Moving Average Forecast (F)

A-F (A-F)2

1 20 - -

2 22 - -

3 23 - -

4 24 -

5 18 -

6 23 21.4 1.6 2.56

7 19 22 -3 9

8 17 21.4 -4.4 19.36

9 22 20.2 1.8 3.24

10 23 19.8 3.2 10.24

11 18 20.8 -2.8 7.84

12 23 19.8 3.2 10.24

13 - 20.6 Total= 62.48

Three & Five year Moving Average Comparison RMSE= {(A-F)2 / n}1/2

RMSE = 78.3534/9 = 2.95

RMSE = 62.48/7 = 2.99

Thus Three Year Moving Average is marginally better than corresponding Five year

Exponential Smoothing A serous criticism of using moving averages in forecasting is that they give equal

weight to all observations in computing the average even though more recent observations are more important

It uses a weighted average of past data as basis for a forecast by giving heaviest weight to more recent information and smaller weights to observations in more distant past on assumption that future is more dependent on recent past than on distant past

The value of time series at period t (At) is assigned a weight (w) between 0 and 1 both inclusive, and forecast for period t (Ft) is assigned 1-w . The basic Equation :

Ft+1 = wAt + (1-w)Ft

Where Ft+1 = forecast for next period At = Actual value of time t (most recent actual data) Ft = forecast for present period w = weight ie smoothing constant

Contd.. Rules of Thumb: When magnitude of random variations is large, w is

taken as lower value so as to even out the effects of random variation quickly

When magnitude of random variations is moderate, a large value is assigned to w

It has been found appropriate to have w between 0.1 and 0.2 in many systems

To identify best forecast amongst many arrived from different values of W,RMSE is used and forecast having least RMSE is considered as best

Illustration : Exponential SmoothingTime period Actual Sales

(Rs ‘000)

Forecasted Sales

1 60

2 64

3 58

4 66

5 70

6 60

7 70 63

8 74

9 62

10 74

Contd.. Forecast sales of time period 8,9and 10 Take a smoothing constant w= 0.2

Econometric Methods Combine statistical tools with economic

theories to estimate economic variables and to forecast intended economic variables

An econometric model may be a single equation regression model

Types of Econometric Method Regression Method

Regression Method It attempts to find out relationship between

dependent and independent variables It is a statistical technique for obtaining the

line that best fits data points It is obtained by minimizing sum of squared

vertical deviations of each point from regression line and method used is called Ordinary Least Squares method (OLS)

Contd… Linear Equation Y= a +bX Where X and Y are averages

Objective of regression analysis is to estimate linear relationship ie a and b

a = Y-bX b = N∑XY – (∑X) (∑Y)

N ∑X2 - (∑X)2

Year t Advertising Xt (million-

Rs)

Sales

Yt (000 units)

X2 XY

1 5 45 25 225

2 8 50 64 400

3 10 55 100 550

4 12 58 144 696

5 10 58 100 580

6 15 72 225 1080

7 18 70 324 1260

8 20 85 400 1700

9 21 78 441 1638

10 25 85 625 2125

N = 10 ∑X = 144 ∑Y=656 ∑X2=2448 ∑XY=10254

Estimating Linear equation b = 10(10254) – (144)(656)

10(2448) – (144)2

b = 2.15 a = Y – bX where Y & X are averages Y = 34.54 + 2.15X It means that an increase of Rs 1 million in ad

expenditure will bring an increase of 2.15 thousand units in sales ie 2,15000 units

Estimating Linear Trend-Least Squares Method When a time series data reveals rising trend

for e.g. in sales then equation is: S= a +bT where a and b are estimated using

following two equations ∑S= na + b∑T ∑ST = a ∑T + b ∑T2

Illustration: Suppose that a local bread manufacturer company wants to assess demand for its product for years 2002,2003 and 2004. for this purpose it uses time series data of its sales over past 10 years.

Year Sales of Bread(000 in tonnes)

1992 10

1993 12

1994 11

1995 15

1996 18

1997 14

1998 20

1999 18

2000 21

2001 25

Estimation of Trend Equation

Year Sales T T2 ST

1992 10 1 1 10

1993 12 2 4 24

1994 11 3 9 33

1995 15 4 16 60

1996 18 5 25 90

1997 14 6 36 84

1998 20 7 49 140

1999 18 8 64 144

2000 21 9 81 189

2001 25 10 100 250

n=10 ∑S=164 ∑T=55 ∑T2 = 385 ∑ST= 1024

Contd…. 164 = 10a + 55b 1024 = 55a + 385b S = 8.26 + 1.48T For 2002, S2 = 8.26 + 1.48(11) = 24,540

tonnes

Problems: Demand Forecasting1. Using method of least

squares, fit straight line trend and estimate the annual sales of 1997.

Year Sales( lacs in Rs)

1991 45

1992 56

1993 78

1994 46

1995 75

Contd.. 2. Suppose number of

refrigerators sold in past 7 years in a city is given in table. Forecast demand for refrigerator for year 2002 and 2003 by calculating 3-yearly moving average

Year Sales

1995 11

1996 12

1997 12

1998 13

1999 13

2000 15

2001 15

Contd.. 3. Estimate demand for

sugar in 2003-04 if population in 2003-04 is projected to be 70 million by using method of least squares to estimate regression equation of form: Y= a+ bX

Data on Consumption of Sugar:

Year Population

(millions)

Sugar consumed (000’tonnes)

95-96 10 40

96-97 12 50

97-98 15 60

98-99 20 70

99-2000 25 80

2000-01 30 90

2001-02 40 100

Thank You