Investigating the added value of integrating human judgement into statistical demand forecasting systems

Abstract

Whilst the research literature points towards the benefits of a statistical approach, business practice continues in many cases to rely on judgmental approaches for demand forecasting. In today's dynamic environment, it is especially relevant to consider a combination of both approaches. However, the question remains as to how this combination should occur. This study compares two different ways of combining statistical and judgmental forecasting, employing real-life data from an international publishing company that produces weekly forecasts on regular and exceptional products. Two forecasting methodologies that are able to include human judgment are compared. In a 'restrictive judgement' model, expert predictions are incorporated as restrictions on the forecasting model. In an 'integrative judgment' model, this information is taken into account as a predictive variable in the demand forecasting process. The proposed models are compared on error metrics and analysed with regard to the properties of the adjustments (direction, size) and of the forecast itself (volatility, periodicity). The integrative approach has a positive effect on accuracy in all scenarios. However, in those cases where the restrictive approach proved to be beneficial, the integrative approach limited these beneficial effects. The study links with demand planning by using the forecasts as input for an optimization model to determine the ideal number of SKUs per Point of Sale (PoS), making a distinction between SKU forecasts and SKU per PoS forecasts. Importantly, this enables performance to be expressed as a measure of profitability, which proves to be higher for the integrative approach than for the restrictive approach.