The impact of customer habit knowledge on last-mile logistics costs and satisfaction

A significant challenge in the optimization of last-mile delivery is the high incidence of unsuccessful delivery attempts. Each failed delivery increases operational costs due to additional transport attempts, while also contributing to traffic congestion and emissions. Moreover, failed deliveries reduce service quality and customer satisfaction. This article examines how more precise user information can reduce failed delivery attempts and proposes a methodology for integrating higher-quality customer data through the utilization of user habit profiles. It also conducts a study and experimentation to analyze how both the available historical information on customer habits and real-time data contribute to minimizing the rate of failed deliveries, thereby reducing the cost of last-mile logistics. Computational experiments are carried out, considering several factors such as types of delivery points, the duration set for the time windows, the available information about the delivery points, or the maximum waiting time. Simulated data were used in the experimentation. The findings suggest that the use of profile information improves performance in 60-minute time windows, reducing costs by 17.35%. Furthermore, implementing the proposed strategy could lower the failed delivery rate by over 45% for home deliveries based on availability curves, in contrast to selecting morning or afternoon delivery time slots, and achieve an extra reduction of up to 10% for combined home and convenience point delivery locations.