Forecast Metrics Toolkit



This template is provided as a reference to calculate the health of the forecast for a Brand and/or SKU over time relying on 12 observations.  There are a variety of metrics provided by both academics and software providers causing a lot of confusion about what each of these means.  Through this template, we aimed at clarifying twenty forecast metrics and illustrated the correct method to calculate these metrics

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This template can be used as a model diagnostic to evaluate the fitted model.  There are several metrics available to use as a model diagnostic including R-squared, Running Mean Absolute Deviation, Weighted Mean Absolute Percent Error, etc.  You can also track forecast bias using the Forecast Bias measure, the Tracking Signal and the Mean Percent Error.

This template can also be used to calculate the observed forecast error for the same SKU if you have a history of Lag forecasts available for the same SKU.

Based on our consulting experience we believe that using these metrics will be able to help industry professionals to judge the health of their forecast and understand their demand plans in a better way.

The calculations are self-explanatory. Refer to the particular comments.

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