Improving Rail Visibility for Ocean Carriers

In the world of global commerce, steamship lines carry millions of containers filled with essential commodities, many of which move throughout North America by rail. However, a major challenge persists: visibility gaps in container location and event tracking. Without accurate, real-time information, delivering cargo on time and within budget becomes a significant hurdle for ocean carriers. Centralized visibility platforms that integrate data from various rail carriers, combined with machine learning and near-real-time dashboards, can address these gaps and help ocean carriers stay on top of container movements.

The key issue with rail shipments is the spotty nature of event feeds, which often report only a handful of events once a container leaves the port or inland ramp. This fragmented data requires ocean carriers to navigate multiple websites to track a container’s location. A centralized, trusted feed offering granular event data can enhance visibility, helping carriers better forecast arrivals and export volumes, ensuring timely coordination between container pickups and motor carriers.

To further optimize container management, machine learning can be applied to the estimated time of arrival (ETA) for containers at inland ramps, continually reassessing the ETA with each step of the journey. This process, known as re-tripping, uses algorithms to analyze the container’s current location and past patterns to refine arrival predictions. In addition, having a centralized resource to track critical events such as pickup times, notifications, and outgate times can help ocean carriers avoid costly detention fees, ultimately leading to significant cost savings on shipments.

Source: Inbound Logistics