This thesis applies machine learning clustering techniques to the analysis of ocean freight transport flows, with the aim of identifying patterns and inefficiencies. The method is applied to the international logistics network of the Ferrero Group. The primary objective of this research is to identify inefficiencies within a portion of the supply chain, i.e. the distribution process, by examining three crucial performance indicators: the total storage days products spend in warehouses, the overall lead times from production to shipment, and the associated risk of aging stock that might lead to expiration. To this aim, two clustering methods, K-Means and DBSCAN, were applied, in order to group products and highlight similar logistics behavior.