Prediction of service time for home delivery services using machine learning

Abstract

With the rise of ready-to-assemble furniture, driven by international giants like IKEA, assembly services were increasingly offered by the same retailers. When planning orders with assembly services, the estimation of the service time leads to additional difficulties compared to standard delivery planning. Assembling large wardrobes or kitchens can take hours or even days while assembling a chair can be done in a few minutes. Combined with the usually vast amounts of offered products, a lot of knowledge is required to plan efficient and exact delivery routes. This paper shows how an artificial neural network (ANN) can be used to accurately predict the service time of a delivery based on factors such as the goods to be delivered or the personnel providing the service. The data used include not only deliveries with assembly of furniture, but also deliveries of goods without assembly and delivery of goods requiring electrical installation. The goal is to create a solution that can predict the time needed based on criteria such the type of furniture, the weight of the goods, and the experiences of the service technicians. The findings show that ANNs can be applied to this scenario and outperform more classical approaches, such as multiple linear regression or support vector machines. Still existing problems are largely due to the provided data, e.g., a large difference between the number of short and longer duration orders, which made it harder to accurately predict orders with longer duration.