Selecting
the Appropriate Technique
As you will see, there are many
techniques available for forecasting purposes, which makes it difficult for
people to select the most appropriate technique. In fact, there is rarely one
best technique for any given forecasting situation and manytimes it is advisable
to use a combination of quantitative and judgmental techniques (see the section
on Combining Forecasting Techniques).
In general, selection
of an appropriate technique can be guided by the focal product’s
stage in its life cycle.
For example, forecasting sales of emerging
products which have little or no sales history must rely on more judgmental
techniques. As the product becomes more mature and more data is available, simple
time series models become more useful. Causal models can ultimately be used
with a rich data history. To see the relationship between choice of technique
and product life-cycle stage, see Table A1 and
A2.
In general, selection
of an appropriate technique can be guided by considering the
following key factors about the forecasting situation.
1. Forecast Horizon:
Basically, you want to make sure that the technique allows you to pick up changes
that might occur during the forecast time interval. For example,
Short-term: < 3 months
In the short-term, seasonal fluctuations
and randomness are the major influences on sales. Because forecasts at many
firms are typically for periods greater than 3 months, short-term forecasting
methods are not emphasized in this guide.
Medium-term: 3 months to 2 years
These medium-term forecasts require
that fluctuations of a medium-term nature (e.g., economic and competitive conditions)
are accounted for by the technique. Since cyclical change and trend are important
factors in this time frame, techniques such as regression analysis and time-series
methods are useful.
Long-term: > 2 years
Here, the major consideration
is with expected trends, as well as economic, competitive, and technological
conditions which can only be estimated subjectively. Judgmental methods are
usually best employed here.
2. Data requirements. Techniques differ
by virtue of how much data is required to successfully employ the technique.
For example, Box-Jenkins models require many data points while judgmental techniques
require little or none.
3. Pattern of past data. The pattern of
a product’s previous sale history is an important factor to consider. While
the major pattern is the trend, there are also cyclic and seasonal patterns
to consider. Certain techniques are best suited for capturing the different
patterns in the data. In Table A1 and A2,
it is shown how well each technique captures various pattern elements in the
data.
4. Explanatory requirements. Whereas some
techniques are based purely on the pattern of past data and may do quite well
at forecasting, manytimes these are not useful by themselves since it is difficult
to explain the forecast to others who wish to understand the causal factors
underlying the forecast. Certain techniques (e.g. regression, leading indicators,
judgemental methods) are particularly well suited to incorporating causal relationships.
Table A1 and A2
also cross-lists the various techniques described in this guide with the above
factors. In addition to the stage in the product’s life cycle, use these factors
to fine-tune your selection of an appropriate set of techniques.
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