Anna Sroginis, Robert Fildes and Nikolaos Kourentzes, ISF2018, 19th June 2018<\/p>\n
Despite the continuous improvements in statistical forecasting, human judgment remains essential in business forecasting and demand planning. Typically, forecasters do not solely rely on statistical forecasts, which are obtained from various Forecasting Support Systems (FSS); they also adjust forecasts according to their knowledge, experience and information that is not available to the statistical models. However, we do not have adequate understanding of the adjustment mechanisms, particularly how people use additional information (e.g. special events, promotions, strikes, holidays etc.) and under which conditions this is beneficial. To investigate this, we conduct experiments that simulate a typical supply chain forecasting process that additionally provides qualitative and model-based information about past and future promotional periods for retail products. Using laboratory experiments, we find that when making adjustments people tend to focus on several anchors: the last promotional uplift, current statistical forecast and contextual statements for the forecasting period. At the same time, participants ignore the past baseline promotional uplifts and domain knowledge about the past promotions. They also discount statistical models with incorporated promotional effects, hence showing lack of trust in algorithms. These results highlight the need for more fundamental understanding of processes behind human adjustments and the reasons for them since it can help to guide forecasters in their tasks and to increase forecast accuracy.<\/p>\n
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