1. Demand Forecasting
By Prof. Jharna Lulla

2. Demand Forecasting
Demand 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.

3. Forecasting Steps Identification of the objective
Determining the nature of goods under consideration
Selecting a proper method for forecasting
Interpretation of results

4. Role of Macro-level forecasting in demand forecasts
Various macro parameters found useful for demand forecasting:
National income and per capita income.
Investment.
Population growth.
Taxation.
Credit policy.

5. Purposes of Forecasting
Purposes of short-term forecasting
Appropriate production scheduling.
Reducing costs of purchasing raw materials.
Determining appropriate price policy
Setting sales targets and establishing controls and incentives.
Evolving a suitable advertising and promotional campaign.
Forecasting short term financial requirements.
Purposes of long-term forecasting
Planning of a new unit or expansion of an existing unit.
Planning long term financial requirements.
Planning man-power requirements.

7. Qualitative Methods or the Survey Method
Definition: To obtain information about the intentions of the spenders through collecting experts’ opinion or by conducting interviews with the consumers.

8. Types of Qualitative methods
Expert Opinion Method:
Panel consensus
Delphi method
Hunch method
Consumer Survey Method:
Complete Enumeration Survey
Sample Survey
End-Use Method

9. Expert Opinion Methods
Panel Consensus method: If the forecast is based on the opinion of several experts then the approach is called forecasting through the use of panel consensus
Advantages: This kind of forecasting minimizes individual deviations and personal biases.
Disadv: Some powerful individual could have influenced the consensus.

10. Expert Opinion Methods
Delphi method: In this method a panel of experts is individually presented a series of questions pertaining to the forecasting problem. Responses acquired from the experts are analyzed by an independent party that will provide the feedback to the panel members. Based on the responses of other individuals, each expert is then asked to make a revised forecast. This process continues till a consensus is reached or until further iterations generate no change in estimates.
This method thus takes care of the disadvantage of panel consensus method.

11. Consumer Survey methods
Definition: The most direct method of estimating demand in the short- run is to conduct the survey of buyers’ intentions. The consumers are directly approached and are asked to give their opinions about the particular product.
It is of three types:
Complete Enumeration method
Sample Survey method
End-Use Method

12. Consumer Survey methods
Complete Enumeration method: This method is based on a complete survey of all the consumers for the commodity under consideration. It resembles the Census Data Collection which considers the entire population. For Example: if there are n consumers and their probable demands for commodity X in the forecast period are x1, x2, x3 ……xn, the sales forecast will be : X= X1 + X2 + X3 …………….+Xn

13. Consumer Survey methods
Sample Survey method: Few consumers are selected to represent the entire population of the consumers of the commodity consumed. The total demand for the product in the market is then projected on the basis of the opinion collected from the sample. This method gives good results especially for new products and brands. The sample size should neither be too small or too big.

14. Consumer Survey method
End Use survey:
A commodity that is used for the production of some other finally consumable food is also known as an intermediary good.
A survey is planned of the intermediary users and the estimated demands from all segments of intermediary users are added.

15. Econometric model
An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining a particular economic phenomena
quantitatively and qualitatively formulated

16. Trend Method
These are generally based on analysis of past sales patterns. This method is used in case the sales data of the firm under consideration relates to different time periods. There are five techniques of the trend or mechanical extrapolation method:

17. Trend Method
1. Fitting a trend line by Observation: this method is elementary, easy and quick. It requires merely the plotting of actual sales data on a chart and then estimating by observation where the trend lines lies.

18. Trend Method
2. Trend through least square method: This method is a mathematical procedure for fitting a line to a set of observed data points (S,T). This technique uses statistical formulas rather than visual estimates, to find the trend line which best fits the available data. Example Sales: a=b ( year number) Or S= a+BT Where a and b have been calculated from past data and T is the number of year for which forecsat is to be done

19. Trend Method
3. Time- Series Analysis: This is an extension of Linear regression which attempts to build seasonal and cyclical variations into the estimating equation. Thus, Sales = A (trend) + b (season) + c (cycle)+ d Where a,b,c and d are constants calculated from past data.

20. Trend Method
4. Moving Average and Annual Difference Method: A moving average of order K is obtained by adding yearly demands for successive years of K number of years and dividing it by K. Thus the moving avearge of order 5 at year t=1/5 (Dt-2+ Dt-1+ Dt + Dt+1 + Dt+2) where Dt is demand in years t.

21. Trend Method
5. Exponential Weighted Moving Average Method: A progressively smaller weight is given to the more distant as compared to a more recent past years.

22. Barometric Techniques
It is based on the idea that future can be predicated from certain events occuring in the present. The barometric techniques involve statistical indicators, usually time series which when combined in certain ways provide indications of the direction of change in the economy or specific industries.

23. Barometric Techniques
Leading indicators: These tend to reflect future market changes. Coincident Indicators: These are the indicators which coincide with or fall behind general economic activity or market trends. Lagging Indicators: Lagging indicators confirm long-term trends, but they do not predict them. Example manufacturer’s stock level and consumer credit outstanding.

24. Regression Analysis
It is the method undertaken to measure the relationship between two variables where correlation appears to exist
Regression Analysis Equation :
B= Unknown parameters
X= Independent variable
Y=Dependent variable

25. Example : Y= X where Y= sales of tractors in thousand units X= Index of farm income For X=200 we get Y = 21.9 Therefore we can forecast a sale of 21,900 units

26. Limitations(Qualitative)
Method
Limitation
Application
Expert opinion
May be affected by influence of one person
Can be used when extensive statistical calculation is not required
Complete enumeration survey
Costly and time consuming
Can be used for census collection
Sample survey method
Only a few consumers views are taken into account
Can be used when a high degree of accuracy is not required
End use survey
It requires complex and diverse calculations
Can be used for forecasting the demand of primary products like milk.

27. Limitations(Quantitative)
Methods
Limitations
Applications
Time series
True results as long as time series projection moves in the same direction
Used only if data exhibits a persistent growth trend.
Econometric model
Assumes relationships established in the past will continue in future
Provides useful results when statistical data is reliable
Input output analysis
To help understand inter- industry relation
Exponential smoothing
Uses only recent data for analysis
Can be used in the fashion industry.
Regression analysis
Useful only if the relation between the statistical data of variables can be found
can be used when there is a relation between two variables.

28. The End
Thank You