MAPE and Bias – Introduction
MAPE stands for Mean Absolute Percent Error – Bias refers to persistent forecast error – Bias is a component of total calculated forecast error – Bias refers to consistent under-forecasting or over-forecasting – MAPE can be misinterpreted and miscalculated, so use caution in the interpretation.
Accurate and timely demand plans are a vital component of a manufacturing supply chain. Inaccurate demand forecasts typically would result in supply imbalances when it comes to meeting customer demand. Forecast accuracy at the SKU level is critical for properly allocating resources.
When discussing forecast accuracy in the supply chain, we typically have one measure in mind: the Mean Absolute Percent Error or MAPE. However, there is a lot of confusion between Academic Statisticians and corporate Supply Chain Planners in interpreting this metric. Most academics define MAPE as an average of percentage errors over a number of products. Whether it is erroneous is subject to debate. However, this interpretation of MAPE is useless from a manufacturing supply chain perspective. The following discusses forecast error and an elegant method to calculate meaningful MAPE.
Definition of Forecast Error
Forecast Error is the deviation of the Actual from the forecasted quantity.
- Error = absolute value of {(Actual – Forecast) = |(A – F)|
- Error (%) = |(A – F)|/A
We take absolute values because the magnitude of the error is more important than the direction of the error.
The Forecast Error can be bigger than the Actual or Forecast but NOT both. An error above 100% implies a zero forecast accuracy or a very inaccurate forecast.
- Error close to 0% => Increasing forecast accuracy
- Forecast Accuracy is the converse of Error
- Accuracy (%) = 1 – Error (%)
How do you define Forecast Accuracy?
What is the impact of Large Forecast Errors? Is Negative accuracy meaningful?
Regardless of huge errors, and errors much higher than 100% of the Actuals or Forecast, we interpret accuracy as a number between 0% and 100%. Either a forecast is perfect or relatively accurate or inaccurate or just plain incorrect. So we constrain Accuracy to be between 0 and 100%.
More formally, Forecast Accuracy is a measure of how close the actuals are to the forecasted quantity.
- If the actual quantity is identical to Forecast => 100% Accuracy
- Error > 100% => 0% Accuracy
- More Rigorously, Accuracy = maximum of (1 – Error, 0)
