Four Reasons For Bad Forecasting

Forecasts never seem to be as accurate as we would like them to be or need them to be. Mike Gilliland, the talented forecaster that SAS managed to get hold off, draws the reasons for this.

“While there are plenty of consultants and software vendors willing to take that money in exchange for lots of promises, are these promises ever fulfilled?” Mike indicates there are at least four types of reasons why our forecasts are not as accurate as we would like them to be.

The first is software that doesn’t have the necessary capabilities, has mathematical errors, or uses inappropriate methods. It is also possible that the software is perfectly sound but due to untrained or inexperienced forecasters, it is misused.

Untrained, unskilled, or inexperienced forecasters exhibit behaviors also affect forecast accuracy. One example is over-adjustment, which happens when a forecaster constantly adjusts the forecast based on new information. Research suggests that much of this fiddling makes no improvement in forecast accuracy and is wasted effort.

The third reason for forecasting inaccuracy is process contamination by the biases, personal agendas, and ill-intentions of forecasting participants. Instead of an unbiased best guess at what is going to happen, the forecast comes to represent what management wants to see happen.

Finally, bad forecasting can occur because the desired level of accuracy is unachievable. The nature of the behavior determines how well we can forecast it – and this applies to demand for products and services just as it does to tossing coins.

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