By Paul Hague, Director, B2B International Ltd
SELECTING THE FORECASTING METHOD
Forecasts attempt to predict the future; therefore it is necessary that the forecast is expressed in appropriate values and in time periods which are appropriate to the exercise. Values should ideally be inflation free, such as numbers of products, tonnes or gallons. Monetary values can be used but they should be expressed at current prices. Alternatively, the figures could be converted to an index. The steps of time over which the forecast is made can be as short as days, weeks or months (as in a sales plan) but more often they are in quarters or years.
A forecast can never be absolutely correct except by coincidence given that the future is always uncertain. It is usual, therefore, to qualify a forecast. Different predictions could be given, based on different assumptions or there could simply be an upper and lower limit either side of the best estimate. The finished forecast should be supported by a description of the underpinning assumptions and their effect. The method of preparing the forecast should also be described. In general, a simple approach is better than a sophisticated but complex method where the user needs to wrestle with the concepts.
Forecasting methods can be divided into two major types: objective methods comprising various statistical approaches and subjective methods based on surveys of opinion. The table below shows the most commonly used forecasting methods and how they are suited to different forecasting periods and applications.
HISTORICAL ANALOGY
History often repeats itself. If the researcher believes that a product will follow a similar pattern to one which is already established and well-documented, the earlier analogous trend becomes a base for predicting the future.
This philosophy lies behind the ‘product life cycle’ which argues that most products face recognisable stages of youth, maturity and decline. Identifying the position of a product at any one point in time in its life is critical, as it enables the researcher to predict future sales and, if necessary, rejuvenate or replace the product. The time span from youth to old age could cover 20 or more years and within that period, product modifications distort the smooth lines of the time curve. Interference with the curve is created by economical cycles, legislation and social changes so that it is sometimes difficult to make practical use of the product cycle, even though theoretically it is a useful concept.
Nevertheless, while it may be difficult to plot an entire life cycle for a product, it may be possible to recognise short sections of it. Only a few years ago, a product launched in America would set a trend which could manifest in the UK a short while later. Today, product launches tend to be global such that the iPod and the latest chip are available to everyone almost everywhere at the same time. Of course, allowance must be made for cultural and economic differences. Product launches which have proved successful in Europe cannot necessarily be expected to be repeated in developing nations.
MOVING AVERAGES
Moving averages smooth the peaks and troughs in data, creating a trend line which cuts the centre of the figures. They are a suitable method for predicting from a series of data which has shown regular historical patterns and where there is a long series. Thus they are a suitable means of predicting seasonal sales and those with an evident cycle. They are not suitable for predicting events in rapidly changing markets, where there is a short time series of data or where account has to be taken of a recent major event (such as the imposition of a new tax or law which affects a market).
Part 3 of this white paper will be published on Tuesday 15th August
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