Demand forecasting involves predicting future demand to aid in production planning, resource allocation, and financial planning. The objectives of demand forecasting are to develop suitable production policies, reduce purchase costs, determine pricing strategies, and plan advertising. Common methods of demand forecasting include qualitative techniques like surveys and quantitative techniques like time series analysis, regression analysis, and barometric forecasting which analyze past demand data and economic indicators. Accurate demand forecasting requires in-depth knowledge of products, customers, and the business environment.

1. DEMAND FORECASTING
MEANING
IMPORTANTS
OBJECTIVES
METHODS

2. What is forecasting all about?
Demand for Mercedes E We try to predict the
Class future by looking back
at the past
Predicted
demand
looking
Time back six
Ja Fe Mar Apr May Jun Jul Aug months
n b
Actual demand (past sales)
Predicted demand

3. WHY DEMAND FORECASTING
• Planning and scheduling production
• Acquiring inputs
• Making provisions for finances
• Formulating pricing strategy
• Planning Advertisement

4. OBJECTIVES
Short –term Forecasting
To evolve a suitable production policy
To reduce the cost of purchase
To determine appropriate price policy
To set sales targets and establish control
To forecast short-term financial
requirements

5. OBJECTIVES
Long –term Forecasting
 Planning of a new unit or expansion of an
existing unit
 Planning of long-term financial requirements
 Planning of man-power requirements

6. Levels of Forecasting
• Macro level
• Industry level
• Micro level

7. STEPS IN DEMAND FORECASTING
• Determination of the objectives
• Sub-dividing the task
• Identifying of demand determinants
• Selection of the method
• Collection of Data
• Estimation and interpretation of result
• Reporting

8. METHODS OF DEMAND FORECASTING
QUALITATIVE QUANTITATIVE
TECHNIQUES TECHNIQUES
 Survey Method  Barometric Techniques
 Direct Interview Method  Time Series Analysis
 Collective opinion
 Regression Method
 Delphi Method
 Controlled Experiments

9. SURVEY METHOD
• Surveys are conducted to collect information
about the future plans of the potential
consumers
• A firm may launch a new product, if the suvey
indicates that there is a demand for that
particular product in the market.

10. Direct Interview Method
Consumers are contacted directly to ask them what they
intend to buy in future
Collective opinion
The opinions of those who have the feel of the market, like
salesman, professional experts, market consultants etc.
Advantages
•Simple
•Quick
•Low cost
•Reliable
Disadvantages
Personal judgements may go wrong
Useful only foe short-term forecasting

11. DELPHI METHOD
• Applied to uncertain areas where past data or future
data are not of much use
• Some expert in an area will be contacted with
questionnaires
• A co-ordinator collect all the opinions
• Each expert will be supplied with responses of other
experts without revealing their identity
• Expert may revise his opinion, if needed
• Process will be repeated so that all experts come to
an agreement

12. CONTROLLED EXPERIMENTS
• Studies and experiments in consumers
behavior are carried out under actual market
conditions
• Three or four cities having similarity in
population , income level, cultural and social
background ,occupational distribution , taste
etc.. are chosen
• Various demand determinants like price,
advertisement , expenditure etc are changed
one by one and these changes on demand are
observed

13. QUANTITATIVE TECHNIQUES
BASED ON DATA AND ANALYTICAL TECHNIQUES
Barometric Techniques
Time series Analysis
Regression Method

14. BAROMETRIC FORECASTING
 based on the observed relationships between
different economic indicators
It can be divided into three groups
 Leading indicators
 Coincident indicators
 Lagging indicators

15. Leading Indicators
• which run in advance of changes in demand for a particular
product
•an increase in the number of building permits
granted which would lead to an increase in demand for
building-related products such as wood, concrete and so on
Coincident Indicators
•occur alongside changes in demand
•an increase in sales would generate an increase in demand for
the manufacturers of the goods concerned
Lagging Indicators
•run behind changes in demand
New industrial investment by firms which will only invest in
new production facilities when demand is already firmly
established.

16. TIME SERIES ANALYSIS
• Used to predict the future demand for a
product based on the past sales and demand.
 Simple moving average
 Weighted moving average
 Exponential smoothing

17. Time series: simple moving average
In the simple moving average models the forecast value is
At + At-1 + … + At-n
Ft+1 =
n
t is the current period.
Ft+1 is the forecast for next period
n is the forecasting horizon (how far back we
look),
A is the actual sales figure from each period.

18. Example: forecasting sales at Kroger
Kroger sells (among other stuff) bottled spring water
Month Bottles
Jan 1,325
Feb 1,353 What will
Mar 1,305 the sales
Apr 1,275 be for
May 1,210 July?
Jun 1,195
Jul ?

19. What if we use a 3-month simple moving average?
AJun + AMay + AApr
FJul = = 1,227
3
What if we use a 5-month simple moving average?
AJun + AMay + AApr + AMar + AFeb
FJul = = 1,268
5

20. Time series: weighted moving average
We may want to give more importance to some of the
data…
Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n
wt + wt-1 + … + wt-n = 1
t is the current period.
Ft+1 is the forecast for next period
n is the forecasting horizon (how far back we look),
A is the actual sales figure from each period.
w is the importance (weight) we give to each period

21. Time Series: Exponential Smoothing (ES)
Main idea: The prediction of the future depends mostly on
the most recent observation, and on the error for the latest
forecast.
Smoothi
ng
constan
Denotes the
t alpha importance of the past
α error

22. Exponential smoothing: the method
Assume that we are currently in period t. We calculated the
forecast for the last period (Ft-1) and we know the actual
demand last period (At-1) …
Ft = Ft −1 + α ( At −1 − Ft −1 )
The smoothing constant α expresses how much our
forecast will react to observed differences…
If α is low: there is little reaction to differences.
If α is high: there is a lot of reaction to differences.

23. Linear regression in forecasting
Linear regression is based on
1. Fitting a straight line to data
2. Explaining the change in one variable through changes
in other variables.
dependent variable = a + b × (independent variable)
By using linear regression, we are trying to explore which
independent variables affect the dependent variable

24. Linear Regression Model
• Shows linear relationship between dependent
& explanatory variables
– Example: Diapers & # Babies (not time)
Y-intercept Slope
^
Yi = a + b X i
Dependent Independent (explanatory)
(response) variable variable

25. Example: do people drink more when it’s
cold?
Alcohol Sales
Which line best
fits the data?
Average
Monthly
Temperature

26. The best line is the one that minimizes the
error
The predicted line is …
Y = a + bX
So, the error is …
εi = y i - Yi
Where: ε is the error
y is the observed value
Y is the predicted value

27. Conclusion
• Accurate demand forecasting requires
– Product knowledge
– Knowledge about the customer
– Knowledge about the environment