Real-time shipment duration prediction

In today's world, global manufacturing requires parts supplied from countries all over the world. Suppliers and manufacturers are located thousands of miles away from the end customers. To focus on core competencies, Third Party Logistics (3PL) providers handle this supply chain aspect of companies. 3PL providers may estimate the delivery time and service level but the actual performance often deviates from their estimates without much liability from the 3PL. When a 3PL deviates, companies bear the unexpected supply chain costs since manufacturing resources operate under faulty estimates. We focus on parts and sub-assemblies that are procured from offshore suppliers. Using a datadriven approach, we forecast the shipment duration using travel-time information of previous shipments. Regression techniques and tree based models were used in this work. The model with highest coefficient of determination (R2) and lowest root mean square error (RMSE) was chosen for the final application. We found that random forest model was best suited for the task. Given this model, a company can now forecast the expected shipment duration independent of 3PL providers' estimates.