1. DEMAND
FORECASTING

2. Demand Forecasting
 Is to predicate the future situations of business
Why necessary ?
 To minimise risk & uncertainty in business
INTRODUCTION

3. Forecasting by Ford Motor
 Web-based car configurator (build your car configurator)
Predict demand for certain cars and features
 When compared with actual sales

4. Techniques of Demand Forecasting
1.Qualitative Method
2.Quantitative Method

5. Qualitative Method
 Obtain Information about the likes & dislike of
Consumer.
 Suited for Short term Demand Forecasting.
 Demand Forecasts for new products can be made
only by Qualitative Techniques.

6. Expert Opinion Method
1. Panel Consensus :
 If the Forecasting is based on the opinion of several Experts,
then it is known as Panel Consensus.
 This kind of Forecasting minimize Individual deviations &
Personal biases.
2. Delphi Method :
 In this method seeks the opinion of groups of Expert
through mail about the expected level of Demand

7. Consumer Survey Method
 This method is based on a complete Survey of all the
consumers for the commodity under consideration.
 Interview or Questionnaires are used.
1.Consumer Sample Survey:
Only few Customers Selected & their views Collected.
Advantages :
Short Term Projections
Does Not Cost Much
Work Quickly
Gives Excellent Results, if used carefully.

8. 2. Consumers End Use Survey :
 This method Focuses on Forecasting the demand for
intermediary Goods.
 Under this method, the sales of a Product are projected
through a survey of its end users.
3. Complete Enumeration Method :
 In this method records the data & aggregates of consumers
 If the data is wrongly recorded than Demand Forecasting
going wrong, than this method will be totally useless.

9. SURVEY
BANK NAMES RATE FOR
SAVING
BANK
ACCOUNTS
Kotak Mahindra Bank 6%
ICICI Bank 4.5%
HDFC Bank 4%
State Bank of India 4%

10. Quantitative method
(statistical Method)
1. Time series analysis.
 It is used to estimate future demand.
 This method is based on obtaining the historical data regarding the
demand for the product , so as project future occurrences.
 The data obtained are chronologically arranged.
 Based on the data plotted on the graph , a line or curve is drawn.
The time series data would indicate different types of fluctuations which can be
classified as
 Secular Trend :- Long run increase or decrease in the series.
 Cyclical Variations :-The rhythmic variations in the economic series.
 Seasonal Variations :- The variations caused by weather patterns social
habits such as festivals etc.
 Random Fluctuations :- The irregular and unpredictable shocks to the
system, such as war, nature catastrophes etc.
When forecast is to be made the seasonal, cyclical, random variations are
eliminated from the collected data leaving behind the secular trend only.

11. 2. Moving Average.
 The method of moving average is useful when the market demand is
assumed to remain fairly steady over time.
Moving Average =Demand in the previous n month
n
3. Exponential smoothing.
 In this techniques more recent data are given more weightage.
 This is based on the argument that the more recent the observations , the
more its impact on future ,therefore it is given more weightage.
4. Index Numbers :-
 The Index no. offers a device to measures changes in a group of related
variable over a period of time.
 In Index no. base year is given the value of 100 and then expenses all
subsequent changes as a movement of this no's .
 The most commonly used is the laspeyes Price Index.

12. 5.5. Regression Analysis.Regression Analysis.
 This method is undertake to measure the relationship between two
variables where correlation appears to exist.
 E.g. The age of the air condition machine and the annual repair
expenses.
 This method is purely based on the statistical data.statistical data.
6.6. Econometric Model.Econometric Model.
 The econometric model is used to express the most probable
interrelationship between a set of economic variables according to
economic theory and statistical analysis.
 Being analytical in nature and process oriented in approach they
throw more light on problems of a theoretical and statistical nature.
LimitationsLimitations
 The assumption that the relationship established in the past
will continue to prevail in the future.

13. 7. Input-Output Analysis.
 The Input-output forecasting is based on a set of table
that explain the inter-relationship among the various
components of the economy.
 E.g. CarCar , Increase or Decrease in the demand of car
lead to increase in the production and also affects its
other products.

14. CRITERIA FOR DEMAND FORECASTING
1.ACCURACY
2.PLASIBILITY
3.SIMPLICITY
4.DURABILITY
5.FLIXIBILITY
6.AVAILIABILITY OF DATA
7.ECONOMY
8.QUICKNESS

15. Thank you