Demand forecasting involves using statistical data and analysis to predict future demand for a product. There are different types of forecasts including short term (less than 1 year), long term, and passive vs active. Short term forecasts help with sales, pricing, and target policies while long term helps with planning. Demand can be forecast at the macro, industry, or firm level. Statistical methods include time series analysis, regression analysis, and smoothing techniques like moving averages and exponential smoothing. Accurate demand forecasting is important for production, inventory, investment, and economic planning.

1. Managerial Economics
Demand Forecasting

2. 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

3. 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

4. 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
Benefitsof Long term forecasting
 Business Planning
Manpower Planning
Long-Term Financial Planning

5. Factors involved in Demand Forecasting
Undertaken at three levels:
a.Macro-level
b.Industry level eg., trade associations
c.Firm level
Should 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.

6. 1.
Criteria of a good forecasting
Accuracy – measured by (a) degree of deviations between forecasts
and actuals, and (b) the extent of success in forecasting directional
changes. method
2.Simplicity and ease of comprehension.
3.Economy.
4.Availability.
5.Maintenance of timeliness.

7. 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.

8. 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.

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

10. 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

13. 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

14. 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

15. 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

16. 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.

17. 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

18. Market Experimentation
Test marketing
A testarea 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
Aftersuch changes are introduced in the market, consequent changes
in demand over a period of time are recorded.

19. 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.

20. 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

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

22. 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

23. 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

24. Illustration
Suppose 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?

25. 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.

26. 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.

27. 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

28. 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.

29. 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

30. 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

31. Moving average Method
In order to decide which of these moving averages
forecasts is better 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.

32. Three-quarter Moving Average forecasts

33. Five Quarter Moving Average forecasts

34. 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

35. 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

36. 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

37. Illustration : Exponential Smoothing

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

39. 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

40. 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)

41. 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

43. 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

44. 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
Estimating Linear Trend-Least Squares
∑ST = a ∑T + b ∑T2
Method

45. 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.

46. Estimation of Trend Equation

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

48. Problems: Demand Forecasting
1. Using method of least
squares, fit straight line
trend and estimate the
annual sales of 1997.

49. 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

50. 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:

51. Thank You