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Top 7 Methods of Demand Forecasting | Managerial Economics


The following points highlight the top seven methods of demand forecasting. the methods are:

1. Survey of Buyer’s Intentions 2. Collective Opinion or Sales Force Composite Method 3. Trend Projection 4. Executive Judgment Method 5. Economic Indicators 8. Controlled Experiments 7. Expert’s Opinions.

Demand Forecasting Method # 1. Survey of Buyer’s-Intentions:

This is a short-term method of knowing and estimating customer’s demand. This is direct method of estimating demand of customers as to what they intend to buy for the forthcoming time—usually a year.


By this the burden of forecasting goes to the buyer. This method is useful for the producers who produce goods in bulk.

Still their estimates should not entirely depend upon it. This method does not hold good for household consumers because of their inability to foresee their choice when they see the alternatives. Besides the household consumers there are many which make this method costly and impracticable. It does not expose and measure the variables under management control.

Demand Forecasting Method # 2. Collective Opinion or Sales Force Competitive Method:

Under this method, the salesman are nearest persons to the customers and are able to judge, their minds and market. They better understand the reactions of the customers to the firms products and their sales trends. The estimates of the different salesmen are collected and estimates sales are predicted.

These estimates are revised from time to time with changes in sales price, product, designs, publicity programmes, expected changes in competition, purchasing power, income distribu­tion, employment and population. It makes use of collective wisdom of salesmen, departmental heads and top executives.



(1) It is simple, common sense method involving no mathematical calculations.

(2) It is based on the first-hand knowledge of salesman and the persons directly connected with sales.

(3) This method is particularly useful for sales of new product. It has the salesman’s judgment.



(1) It is a subjective approach.

(2) This method can be used only for short-term forecasting.

For long-term planning it is not useful.

Demand Forecasting Method # 3. Trend Projection or Time Trend of the Time Series:

This is the most popular method of analysing time series and is generally used to project the time trend of the time series. A trend line can be filled through the series in visual or statistical way by the method of least squares.

The analyst can make a plausible algebraic relation—may it be linear, a quadratic or logarithmic between sales on one hand and independent variable time on the other. The trend line is then projected into the future for purpose of extrapolation.


This method is most popular as it is simple and in-extensive and because of time series data often exhibits a persistent growth trend.



The basic assumption of this method is that the past rate of change of the variable under study will be continuing in future. This assumption gives good safe results till the time series exhibits a persistent tendency to move in the same direction.

When the burning point comes, the trend projection breaks down. Even though a forecaster could hope normally to be correct in most forecasts when the turning points are few and spaced at long intervals from each other.

In fact, the actual challenge of forecasting is in the prediction of turning points rather than in the trend projection. At such turning points the management will have to change and revise its sales and projection strategies most drastically.

There are four factors responsible for the characterization of time series.


They are:

1. Fluctuations and turning points.

2. Trend seasonal variations.

3. Cyclical fluctuations, and


4. Irregular or random forces.

The problem in forecasting is to separate and measure each of these factors.

This time series is expressed by the following equation:


where, O = observed data

T = a secular tend


S = a seasonal factor

C = cyclical element

I = an irregular movement.

The usual practice is to calculate the trend first from the basic data. The trend values are then taken out from the observed data (TSCI /T). The next step is to reckon the seasonal index that is utilised to remove the seasonal effect (SCI/S).

It is fitted through chain to the remainder that also gives the irregular effect. This approach to the breaking up of time series data is an analytical device of usefulness for the knowledge of the nature of business fluctuations.



(a) Analysis of movements would be in the order of trend, seasonal variations and cyclical changes.

(b) The effects of every component are not dependent on any other components.

Demand Forecasting Method # 4. Executive Judgment Method:

Under this method opinions are sought from the executives of different discipline i.e., marketing, finance, production etc. and estimates for future demands are made. Thus, this is a process of combining, averaging or evaluating in some other way the opinions and views of the top executives.


The main advantages of this method are:

1. The forecasts can be made speedily by analysing the opinions and views of top executives. The techniques is quite easy and simple.


2. No need of elaborate statistics:

There is no need of collecting elaborate. Statistics for the forecasts hence it is not much expensive.

3. Only feasible method to follow:

In the absence of adequate data is it the only feasible method to be followed.


The chief dis-advantages of the of this method are:


(1) No factual basis of such forecast:

There is no factual basis of such forecasts, so the method is inferior to others.

(2) No accuracy:

Accuracy cannot be claimed under this method.

(3) Responsibility for the accuracy of data cannot be fixed on any one.

5. Economic Indicators:

This method has its base for demand forecasting on few economic indicators.

(a) Construction contracts:

For demand towards building materials sanctioned for Cement.

(b) Personal Income:

Towards demand of consumer goods.

(c) Agricultural Income:

Towards demand of agricultural imports instruments, fertilisers, manner etc.

(d) Automobiles Registration:

Towards demand of car parts and petrol. These and other economic indicators are given by specialised organisation. The analyst should establish relationship between the sale of the product and the economic indicators to project the correct sales and to measure as to what extent these indicators affect the sales. To establish relationship is not an easy task especially in case of New Product where there is no past records.


Following steps may be remembered:

(a) If there is any relationship between the demand for a product and certain economic indicator.

(b) Make the relationship by the method of least squares and derive the regression equation. Supposing the relationship is Linear the equation will be of the form y = α + bx. There can be curvilinear relationship also.

(c) Once the regression equation is obtained any value of X (economic indicator) can be applied to forecast the value of Y (demand).

(d) Past relationship may not recur. Therefore, need for value judgments are felt. Other new factors may also have to be taken into consideration.


The limitations of economic indicators are as follows:

(1) It is difficult to find out an appropriate economic indicator.

(2) For few products it is not good, as no past data are available.

(3) This method of forecasting is best suited where relationship of demand with a particular indicator is characterised by a Time Lag, such as construction contracts will give consequence to demand for building materials with some amount of Time Lag.

But where the demand does not Lag behind the particular economic index, the utility is restricted because forecast may have to be based on projected economic index itself that may not result true.

Demand Forecasting Method # 6. Controlled Experiments:

Under this method, an effort is made to ascertain separately certain determinants of demand which can be maintained, e.g., price, advertising etc. and conducting the experiment, assuming etc., and conducting the experiment, assuming that the other factors remain constant.

Thus, the effect of demand determinants like price, advertisement packing etc., on sales can be assessed by either varying them over different markets or by varying them over different time periods in the same market.

For example:

Different prices would be associated with different sales on that basis the price, quantity relationship is estimated in the form of regression equation and used for forecasting purposes. It must be noted that the market divisions here must be homogeneous with regard to income, tastes etc.

Such experiments have been conducted widely in the USA and were successful. This is a new experiment. This is quite new and less applied.

The main reasons for non-application of this method so far as follows:

1. The method is expensive and time consuming.

2. It is risky because it may lead to un-favourable reactions on dealers, consumers and competitors.

3. It is not always easy to determine what conditions should be taken to be constant and what factors should be regarded as variable, so as to separated and measures their influence on demand.

4. It is hard to satisfy the homogeneity of market conditions. In-spite of these drawbacks, controlled experiments have sufficient potentialities to become a useful method for business research and analysis in future.

Demand Forecasting Method # 7. Expert’s Opinions:

Under this method expert’s opinions are sought from specialists in the field, outside the organisations or the organisation collects opinions from such specialists; views of expert’s published in the newspaper and journals for the trade, wholesalers and distributors for the company’s products, agencies and professional experts.

These opinions and views are analysed and deductions are made therefrom to arrive at the figure of demand forecasts.


The advantages of this method are:

(1) Forecasts can be done easily and speedily.

(2) It is based on expert’s views and opinions hence estimates are nearly accurate.

(3) The method is suitable where past records of sales are not available.

(4) The method is economical because survey is done to collect the data. The expenses of seeking the opinions and views of experts are much less than the expenses of actual survey.


The important dis-advantages of this method are:

(1) Estimates for a market segment cannot be possible.

(2) The reliability of forecasting is always subjective because forecasting is not based on facts.

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