Demand Forecasting methods for Project - Printable Version

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Demand Forecasting methods for Project - Manish Jain - 07-29-2016

After gathering information about various aspects of the market and demand from primary and secondary sources, an attempt may be made to estimate future demand. A wide range of forecasting methods are available to the market analyst



Survey methods


Jury of executive opinion method: Very popular in practice, this method calls for the pooling of views of a group of executives on expected future sales and combining them into a sales estimate.

Delphi method: This method involves converting the views of a group of experts, who do not interact face – to – face, into a forecast through an iterative process.



Consumer survey method 
This is direct approach and consumers are approached and asked to express their opinion of particular product 

Sale forecast composite 
This method of sales forecasting relies on the judgement of sales executive in direct sales of product. 


Statistical methods 


Trend projection method: Very popular in practice, the trend projection method involves extrapolating the past trend onto the future.

Exponential smoothing method: In exponential smoothing, forecasts are modified in the light of observed errors.
Moving average method: According to this method, the forecast for the next period represents a simple arithmetic average or a weighted arithmetic average of the last few observations.



Regression Technique

A regression model is an equation relating a dependent variable to many independent variables. For example, anticipated sales (dependent variable) may be expressed as a function of independent variables like disposable income of consumers, price relative to the price of competitive products, level of advertising etc.. and the relationship can be expressed as



Y=a1 + (b1.x1) + (b2.x2) + (b3.x3) + …………..(bn.xn)


where Y represents sales
a1, b1, b2 bn are constants and x1, x2 … .xn are independent variables which affect the dependent variable Y. With the time series data collected, a relationship is established as above. After establishing the relationship, values for the independent factors for the future period are estimated. Substituting these values in the relationship, estimate for the future years can be made.

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