transportation sector often suffers from poor capacity Jun 23rd, 2021   [viewed 22 times]
Successful implementation of this aspect has been showcased by the Blume Global platform through their Street Turns feature. This feature leverages AI to optimize the truck’s capacity usage, thus ensuring efficient use of driver hours of service and reduction of fuel consumption and deadhead distance. simple process, where the truck is pretty much empty for the return journey from B to A and A to C. However, using AI tools, historical data gathered for various loads can be utilized to generate leads of potential loads in areas near drop-off locations, and carriers can be assigned loads, which are often managed by brokerage services that act on behalf of interested shippers. This will lead to improved truck utilization as the empty mile in is optimized to B to C only. We can evaluate carriers’ performance based on linear factors like on-time arrival, cost/unit weight, time to ship, etc. This is a very generic method of allocation of load to the shipper. The efficient method is to schedule a shipper based on the load type, cost, and available time factors. This is fathomable and feasible if we develop a continuous DataOps pipeline with an artificial intelligence model enabled on it. ML algorithms such as Gradient Boosting or Random Forest can train the model over the training set of the historical data. To reduce bias, the results can be aggregated from several iterative output models. The outcome is a list of carriers ranked in the order of their f1-score. It was observed that among all the features or factors dialed in, quantity distribution, weight distribution, total distance, and geographical locations tend to stand out as decisive features over time. Inefficient yard or dock management often results in drivers waiting for hours to get the shipment loaded or unloaded. This eats into their hours of service, which impacts revenue generation for carriers, creates congestion, and sometimes even results in accidents and fatalities on the road due to the driver’s aim to compensate for the lost time. This can be avoided with the help of the image recognition aspect of ML, where docking station images captured can be fed as features or input data to machine learning models to identify docking station utilization. More info: field service technician