Carrier selection: Is data valuable for shippers?

In recent years, the complexity of last mile freight transportation management has increased. Shippers are facing demand fragmentation and often rely on a set of external carriers for the delivery to customers. Different carriers are characterized by different costs, service level and range of delivery services. Therefore, selecting the right carrier for each delivery is crucial for shippers' efficiency and effectiveness. However, research has largely overlooked this issue. This study represents a first attempt to address the carrier selection problem through the application of machine learning. Considering the real-world data of a shipper, a machine learning model is developed and its application is simulated to assign each delivery to the most suitable carrier based on delivery features and carriers' historical lead time performance. Results show that significant performance improvements can be achieved compared with the approach currently used by the shipper, highlighting the potential for further research in this area.