An information-sharing and cost-aware custom loss machine learning framework for 3PL supply chain forecasting

Supply chain forecasting methods have traditionally been developed from the perspective of manufacturing companies, which historically held dominant roles within supply chain dynamics. However, the growing importance of third-party logistics providers (3PLs) calls for forecasting approaches tailored to their unique operational needs. This paper presents a novel forecasting framework specifically designed for 3PLs to accurately predict the truck space required for transporting their customers’ products. Unlike conventional methods, the proposed approach directly forecasts truck space demand by utilizing data obtained through information-sharing technologies to train machine learning models. Furthermore, a customized loss function is introduced for the first time, explicitly accounting for the asymmetric costs associated with overestimating and underestimating truck utilization. The framework was validated through a real-world case study involving a 3PL operating in the food sector. The results demonstrated significant improvements over traditional forecasting techniques, underscoring the benefits of integrating machine learning, information sharing, and a tailored loss function to enhance both predictive accuracy and cost-efficiency.