Does experts' adjustment to model-based forecasts contribute to forecast quality?

We perform a large-scale empirical analysis of the question whether model-based forecasts can be improved by adding expert knowledge. We consider a huge database of a pharmaceutical company where the head office uses a statistical model to generate monthly sales forecasts at various horizons for various products in seven categories across thirty-five countries and where local managers can modify those model-based forecasts. To sensibly compare realizations and forecasts we develop a useful statistical methodology. Our main finding is that on average the model-based forecasts are about equally good with or without added expertise. We examine the possibility that the expert puts too much weight on his or her own contribution and we obtain strong evidence that this is the case.