Ricerc@Sapienza

The present project proposal aims to define a methodology to automate the process of load forecast in the logistics of food and other perishable products using classification algorithms of machine learning. In last years, the increase in the use of B2C logistics and transportation is encouraging the companies to rethink deeply logistic network and, consequently, logistics management.
Logistics networks play a crucial role to ensure customer satisfaction and fulfillment but can cause a too high level of expired products, and consequently costs and wastes. To improve the b2C logistics for perishable products one key factor is the prediction of the optimal amount of product to load on every shipment, route, destination. In fact, with the forecast of the optimal amount of perishable product (e.g. food) to load, it is possible to reduce food waste and non-value-added activities, with their associated costs and with the possibility to occupying human resources in more profitable activities, so a shrewder general costs management. Moreover, predicting the optimal load allows the optimization in the use of warehouses, centralized and locally, that can be considered a limited resource. The local warehouses commonly have limited size, so the quantity to offer to the customer should be optimized; moreover, the centralized warehouses of the logistics network serve several local distribution centers and their inventory strongly affects the performance of the selling service. The present research aims to use real case data, acquired from a specific B2C scenario (e.g. high-speed railway catering service), to develop a machine learning system and to propose a methodology able to forecast the optimal amount of food to load in the logistics operations.