Combining SKU-level sales forecasts from models and experts

We study the performance of SKU-level sales forecasts which linearly combine statistical model forecasts and expert forecasts. Using a large and unique database containing model forecasts for monthly sales of various pharmaceutical products and forecasts given by about fifty experts, we document that a linear combination of those forecasts usually is most accurate. Correlating the weights of the expert forecasts in these linear combinations with the experts’ experience and behaviour shows that more experience and modest deviation from model forecasts gives most weight of the expert forecast. When the rate of bracketing increases, we notice a convergence to equal weights. We show that these results are robust across twelve different forecast horizons.