MIT SCM Symposium 2024

In the U.S. trucking industry, freight brokerages act as vital intermediaries between shippers and carriers, but they face financial risks due to write-offs from unpaid services. Despite the recognized importance of mitigating these financial risks, the sponsoring company does not currently have a predictive model to assess the likelihood and magnitude of write-offs, making it challenging to prevent financial losses before they occur. This study tackles this issue by analyzing shipment data and historical write-off incidents to identify key predictors of financial write-offs. Utilizing logistic and linear regression models, it quantifies the risk associated with each shipment, enabling the brokerage to prioritize transactions with lower risk profiles. The analysis revealed that specific shipment characteristics, such as mode of transportation, significantly influence the likelihood and magnitude of write-offs. Predictive models developed in this study were able to accurately forecast the probability of write-offs, offering a tool for more informed decision-making. The findings demonstrate the potential for predictive modeling to significantly reduce financial risks for freight brokerages by enabling preemptive identification of high-risk shipments. By applying this predictive approach, freight brokerages can enhance their financial stability and operational efficiency, contributing to the overall health of the trucking industry's economic ecosystem.