Combining machine learning with human knowledge for delivery time estimations

Abstract

Although machine learning algorithms outperform humans in many predictive tasks, their quality depends much on the availability of sufficient and representative training data. On the other hand, humans are capable of making predictions based on “spontaneous” transfers of knowledge from other domains or situations in cases where no directly relevant experiences exist. This can be seen very well in the task of predicting lead times in goods transport, where sudden disruptions or shortages may occur that are not reflected in historical data, but known to a well-informed human. If the variation can be anticipated and more accurate lead times estimated, proactive measures can be taken to decrease the impact. Therefore, we describe three novel approaches for delivery time predictions, combining a machine learning model with human input. The proposed logic covers two phases, learning based on actual delivery data and capturing human knowledge to cover exceptional situations not reflected in historical data. The proposed models and the resulting estimates were evaluated using deliveries from a retail company. It was found that the pure machine learning model delivers better results than a combination of humans and machines. On the one hand, this is caused by the complexity of incorporating human knowledge into the algorithm in a suitable way. On the other hand, it is also due to the tendency of humans to over-generalise the impact of certain events. Thus, although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, our systematic qualitative analysis of the results presents insights for future development in this area.