Operations planning tasks require sales forecasts at different levels of aggregation. This leaves firms with a choice. Top-level forecasts can be made based on top-level data or, instead, in a bottom-up fashion, where lower-level forecasts are summed up to create a top-level forecast. Lower-level forecasts can be based on lower-level data, or can instead be computed in a top-down fashion, where a forecast is generated based on top-level data and then apportioned to the lower-level. Our study investigates the performance of such forecasting processes through a behavioral lens. A key performance driver across the investigated forecasting processes is the correlation structure among lower-level time-series. We identify two relevant biases that affect forecasting performance: a propensity to neglect distal time-series information in lower-level forecasts, and a propensity for random judgment errors. Our results show how these biases (dis)aggregate as the forecasting process “moves” between levels of aggregation. This allows us to characterize the demand environments in which an organization should favor one forecasting process over the other. (Joint work with Enno Siemsen and Douglas J. Thomas.)