A Better Way to Assess the Quality of Demand Forecasts

Steve Morlidge

Foresight: The International Journal of Applied Forecasting, 2015, issue 38, 15-20

Abstract: In his last Foresight article, ?Measuring the Quality of Intermittent Demand Forecasts? in the Spring 2015 issue, Steve showed that certain common error metrics?the MAE and MAPE?can readily lead to the selection of inappropriate forecasting methods when demands are intermittent. Intermittent demands are a special case of asymmetric (skewed) distributions, for which Steve proposed an alternative metric, the bias-adjusted mean absolute error (BAMAE). In this article Steve makes the argument that the BAMAE can serve as the most valid and appropriate error metric for any distribution of demands, symmetric or asymmetric. It is well worth giving this metric some thought. Copyright International Institute of Forecasters, 2015

Date: 2015
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