AI-driven optimization
for a transportation
management platform

As businesses look for ways to enhance their performance, supply chain management, and particularly transportation management, is becoming one of the main areas of focus. However, orchestrating an efficient transportation process is fraught with numerous complexities.

One of our clients - a leading AI-driven transportation management platform -  was looking for an ML solution that improves the accuracy of some of the platform’s functionalities in the scope of Estimated Time of Arrival (ETA), On-time Probability (OTP), and Deadhead Miles Reduction.

deepsense.ai developed a solution that leverages machine learning to improve selected KPIs. By analyzing factors such as weather conditions, truck/trailer information, driver hours of service, and rest periods, the system calculates the probability of a delay and estimates the arrival time. This part is an input to the Deadhead Miles Reduction feature which also takes into account distances, the type of fleet, hours of service, and restrictions related to delivery units and the load type. The solution facilitates the enhanced allocation of loads and trucks, thereby reducing unnecessary trips and resulting in considerable financial savings. 

The implemented ML algorithms led to higher efficiency in terms of Estimated Time of Arrival (ETA) and On-time Probability (OTP). The platform noted a 57% improvement in ETA and a 37% reduction in the number of false delays.

The Deadhead Miles Reduction functionality achieved up to 24% fewer empty miles than the baseline solution.