CHAPTER 3 Forecasting Performance Evaluation and Reporting

Process improvement begins with process measurement. But it can be a challenge to find the right metrics to motivate the desired behavior. A simple example is provided by Steve Morlidge (in an article later in this chapter) for the case of intermittent demand:

When 50% or more of the periods are zero, a forecast of zero every period will generate the lowest average absolute error—irrespective of the size of the nonzero values. Yet forecasting zero every period is probably the wrong thing to do for inventory planning and demand fulfillment.

There are dozens of available forecasting performance metrics. Some, like mean absolute percent error (MAPE), represent error as a percentage. Others, like mean absolute error (MAE), are scale dependent; that is, they report the error in the original units of the data. Relative-error metrics (such as Theil’s U or forecast value added (FVA)) compare performance versus a benchmark (typically a naïve model). Each metric has its place—a situation where it is suitable to use and informative. But there are also countless examples (many provided in the articles below) where particular metrics are unsuitable and lead decision makers to inappropriate conclusions.

After Len Tashman’s opening overview and tutorial on forecast accuracy measurement, this chapter provides a critical exploration of many specific metrics and methods for evaluating forecasting performance. It covers some innovative ...

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