Smart home devices and B2C e-commerce: a way to reduce failed deliveries

Purpose: B2C e-commerce is fast spreading all over the world. If compared to the offline market, it opens new challenges for companies, and one of these is higher complexity of the logistics activities. In particular, one of the most critical processes in the logistics field, due to its impact on both costs and service level, is the last-mile delivery – i.e. the “final leg” of the order fulfilment, aimed at delivering the products to the final consumer. More in detail, a very significant issue is that of the failed deliveries, i.e. the deliveries not accomplished due to the absence of the customer. They both imply high costs for e-commerce players – that need to re-schedule them – and have a negative impact on the satisfaction of customers. A way to face this issue could be scheduling the deliveries based on the probability to find customers at home. A promising alternative for gathering data on the customer presence is represented by Internet of Things devices, whose diffusion has been significantly growing in recent years. Design/methodology/approach: The solution presented in this paper aims at building presence profiles of customers based on data collected through smart home devices (e.g. smart home speakers) able to detect people presence at home during the day and along the week. In addition, the work develops the analytical formulation of a Vehicle Routing Problem that schedules deliveries, aimed at reducing not only the travelled distances – as it happens in traditional VRPs – but also the number of failed deliveries. More specifically, the routing algorithm is composed by two sub-stages. First, it carries out a pre-allocation of customer orders to specific time-windows, based on the probability of the customers to be at home when deliveries are performed. Second, the algorithm finds the sequence of customers to be visited during the day that optimises the routing. Findings: The application of the model to a case in Milan shows that the proposed solution implies a significant reduction of missed deliveries – about -16% – with respect to the traditional operating mode (in which the probability of finding the customer at home is not considered while scheduling the deliveries). Accordingly, even if the pre-allocation of customers based on probability increases the total travel time, the average delivery cost per parcel decreases. Value: This work provides both academic and managerial implications. On the academic side, it contributes to the literature while developing an innovative probability-based Vehicle Routing Problem that, differently from other existing works, exploits new technological trends (e.g. the diffusion of smart-home devices). On the managerial side, it proposes a novel solution for scheduling B2C last-mile deliveries that relies on the use of smart home devices, and that has a significant impact in both reducing operating costs and increasing service level