Judgmental adjustments to statistically generated forecasts have become a standard practice in demand forecasting, especially at a stock keeping units level. However, due to the subjective nature of judgmental interventions this approach cannot guarantee optimal use of available information and can lead to substantial cognitive biases. It is therefore important to monitor the accuracy of adjustments and estimate persistent systematic errors in order to correct final forecast. This paper presents an appropriate methodology for such analysis and focuses on specific features of source data including time series heterogeneity, skewed distributions of errors, and generally nonlinear patterns of biases. Enhanced modelling and evaluation techniques are suggested to overcome some imperfections of well-known standard methods in the given context. Empirical analysis showed that a considerable proportion of final forecast error is formed by a systematic component which can be pre- dicted. Proposed bias correction procedures allowed to substantially improve the accuracy of final forecasts. In particular, one-factor mod- els of the relationship between forecast error and adjustment were found to be a simple, robust and efficient tool for the given purpose.