Widely used methods
of automated forecasting for production and inventory control contribute to the
severity of recessions. We describe an approach to forecasting that should reduce
the damage caused by the use of current statistical packages when encountering
a substantial change in business conditions.

Assume that you are
a manufacturer. It is early 2008 and you have been reading about a possible recession.
How should you go about forecasting your production and inventory?  Should you use your knowledge about bad
times ahead, or just rely on the statistical models that churn out forecasts
for the 5,000 thousand items under your control. If you do want to use your
knowledge about the products, how can you do it?

If you are like most
managers, your statistical models will not serve you well. They lack crucial
information that you have.

One type of
information that managers have relates to expectations about trends. Armstrong
and Collopy (1993) use the notion of “causal forces” – the influences on demand
for a product -- to describe expectations. They identified five types of time-series:
growth, decay, opposing, regressing, supporting, and unknown. For example, a
growth series is one where managers expect the trend to be upwards no matter
what the historical trend has been. Similarly, a decay series is one where they
expect the trend to be downward, perhaps because a currently successful product
will be replaced by a superior product.

Armstrong and
Collopy found that college students with little knowledge about a subject area
(say automobile sales) were able to quickly identify the causal forces for a
wide variety of time series. Of course, it would probably be better to use
people who have expertise in the relevant areas.

production and inventory forecasting models assume that the causal forces always
support the historical trends. In fact, Armstrong and Collopy were unable to
find any time-series where the causal forces always support the trend.

Although the
assumption of supporting causal forces is unfounded, the world often looks as
though the trends are always supported
by causal forces. Consequently, when the good times roll, standard forecasting
packages perform well. However, when the historical trends are contrary to the
expected causal forces, forecast errors become large and contribute to
oversupply in recessions and shortages in recoveries.

To deal with this
and related problems, Collopy and Armstrong developed and published a set of 99
rules as the basis of a method they dubbed “rule-based forecasting (RBF).”
Although a number of RBF programs have been developed privately, there is no
commercial package. While applying RBF without software is onerous, one simple
and inexpensive rule can achieve much of the benefit of RBF by reducing errors
when forecasting contrary series.

Here is the rule: When a time series is identified as
“contrary,” do not extrapolate a trend.

Once the causal forces
have been identified for each series (or sets of series), a simple rule can be
introduced to compare the expected trend with the historical trend. When the
expected and historical trends are “contrary” to one another, set the trend
forecast to zero. The rule was tested for using a variety of economic data. It
was also tested using data on epidemics in China and on U.S. Navy manpower
planning. The contrary series rule improved accuracy in every test. The gains
were especially large for contrary series with strong causal forces; errors
were cut by half.

When a recession is
anticipated (or perhaps even in its early stages), a large number of formerly growth
series should, on the basis of expectations, be re-coded as decay, thus leading
many series to be classified as contrary. Had firms used the contrary series
rule, they would have substantially reduced their production and inventories on
many items well before the statistical models realized that a downturn was
underway. Eventually, the recession will end, and here again, use of the
contrary series rule will aid the firms, as they will be able to build
inventories to meet demand.

Contrary series also
affect prediction intervals because their errors tend to be larger in the
direction of the trend expected based on the causal force. This will have an
impact on setting the desired inventory levels. A quick solution is to shift
the prediction intervals in the direction of the causal forces. This would help
to move quickly to reduce inventories in anticipation of a downturn– and to a
larger inventory when a recovery is expected. These seem like sensible things
to do, but statistical models will not figure that out on their own.

You must ask your
statistician to insert a contrary series rule along with your judgments on the
causal forces relevant to the types of time-series your are dealing with. Some
software providers have told me that they will happy to do this if clients ask.

By now, your time
series are heading south, so you missed an opportunity. But if you add this
capability to your forecasting package now, you will be in a position to
respond quickly when the recession is expected to end – more quickly than those
who have not used this approach. It takes a long time for statistical models to
realize that a recession is underway.

This structured use of managers’ knowledge
almost always leads to better forecasts for production and inventory control. When
the economy is in recession or is recovering from one, it is especially useful.
Do not let your statisticians tell you otherwise.

11.0pt>Note: For more information on this topic, see J. Scott Armstrong &
Fred Collopy (1993), Causal Forces: Structuring Knowledge for Time-series
Extrapolation, Journal of
, 12, 103-115. In addition, adjustments must be made for
assessing prediction intervals for contrary series. For this, see J. Scott
Armstrong & Fred Collopy (2001), Identification of Asymmetric
Prediction Intervals through Causal Forces, normal>Journal of Forecasting, 20,  273-283. Full text of these
papers can be obtained at ForPrin.com. They provide all that your statistician
(or forecasting software provider) needs to know. We talked to some software
providers; they say that they will be happy to make the adjustments if their
clients ask. Information about software providers can be obtained from