Sales Forecasting – other Methods

The simple projection method is the one in which the current year’s forecast is arrived at by simply adding an assumed growth rate to last year’s sales; some firms go by the industry growth rate and project the sales; some others take the growth rate achieved by the no. 1 firm in the industry. Another formula as shown below is also used by some firms:

Next year’s sales =

= (This year’s sales)2 / Last year’s sales

Only where the year-by-year sales are stable and show an increasing trend, this formula will provide a reasonably reliable estimate.

Merits and Demerits Projection Method: The simple projection method provides a rough and ready forecast. However, sometime the forecast arrived at by this method can be wide off the mark, the method assumes that (1) past sales is the only factor influencing future sales(2) sales will always be growing, year by year The method does not provide for changes that may take place in a market. However, when the forecasting situation is relatively simple, and when the firm is in the mature stage of its business without too much of growth or decline, and when the external changes are not violent either, the simple projection method will serve the purpose. It has the advantage of being inexpensive.

Extrapolation Method: Extrapolation is also a projection/trend method, but is a bit complex compared to the simple projection method. It involves the plotting of the sales figures for the past several years and stretching of the line or the curve as the case may be. The extrapolation will give the figures for the coming years. Extrapolation basically assumes that the variables will follow their previously established pattern. Accordingly, this method will be effective where the pattern of past movement has been relatively steady and abrupt disruptions are unlikely in the future. In other words, the assumption is that the past will show the future.

Moving Averages Method: This method helps eliminate the effects of seasonality and other irregular trends in sales while forecasting future sales. The method delivers a time series of moving averages. Each point of to time series is the arithmetical or weighted average of a number of preceding consecutive points of the series. If seasonal effects are present in the demand pattern of the product, a minimum of two years sales history is needed for applying this method.

Exponential Smoothing: Exponential smoothing is yet another projection method used for sales forecasting. It is similar to moving average and is used fairly extensively. It too represents the weighted sums of all past numbers in a time series, with the heaviest weightage placed on the most recent data. This method is particularly useful when forecasts of a large number of terms are made. It is not necessary here to keep a long history of past data. The method can have a stable response to changes and the responses can be adjusted as required. This method is also adaptable for trend correction and smoothing of forecast errors. Exponential smoothing is one of the most accurate statistical techniques available for forecasting.

Time Series Analysis: Another statistical method that is extensively used in sales forecasting in the time series analysis, also known as trend cycle analysis. A time series is a set of chronologically ordered raw data, for example, the monthly sales of a given product for several continuous years. Time series helps to identify and explain:

(i) Systematic variation or ‘seasonal’ variation, which arises due to seasonality in the series of data.
(ii) Cyclical patterns that repeat themselves every two or every three years and so on.
(iii) Trends in the data
(iv) Growth rates of these trends.