The gold standard for modeling the future in a business environment is the designed experiment. Design of experiments (DOE) is a well-developed approach to planning and executing controlled manipulations.

Somewhat less respectable are models derived from historical data. It makes sense to utilize as much of this information as possible, but caution is required. Problems you may encounter are:

• Measurement error . Historical data are often recorded by untrained people, or the precision required for day-to-day use of the data may be wide compared to what you need for modeling. Errors that aren’t important in the data’s original use may wreak havoc on your model-building activity.

• Range restriction. Operational systems are deliberately controlled to minimize the effect of system variation on results, meaning that the allowance for variation of system parameters is very small. It is very possible that the response we are modeling will not be affected by variation of inputs in this range, but that doesn’t mean that the responses wouldn’t change if the inputs were varied over a larger range. The result is a model that gives misleading results by excluding important parameters.

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