Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?

We determine whether statistical model forecasts of SKU level sales data can be improved by formally including past expert knowledge in the model as additional variables. Upon analyzing various forecasts in a large database, using various models, forecast samples and accuracy measures, we demonstrate that experts' knowledge, on average, apparently is not associated with variables which are systematically omitted from the statistical models. We also find that the formal inclusion of past judgment can be helpful in cases when the model performs poorly. This can lead to an improved interaction between models and experts, and we discuss the design features of a forecasting support system.