Intermodal terminals experienced significant volume growth in 2024, according to the Intermodal Association of North America, increasing 8.5% compared to the previous year. The growth was primarily driven by a 13.9% surge in international container movements, which can create inefficiencies and increased costs for terminals. Terminal operators faced added operational complexity, congestion, and bottlenecks due to factors like increased dwell times and limited yard capacity.
As container volumes continue to grow, data has become more critical to helping terminals navigate daily challenges. Operational reporting and dashboards immediately come to mind in such contexts, but staying competitive today requires more—tools that are more insightful, predictive, and actionable. Better governance, usage, and application of a terminal’s data can increase efficiency and decrease costs while minimizing capital investment in infrastructure. But where does one begin, and what should be prioritized as we embark on this new data-inspired journey?
Understanding data
Terminal operating systems (TOS) and the myriad of other technologies in use at a terminal generate a vast amount of data. Much of it is transactional, which is generated from an event or activity such as in-gates, equipment moves, train arrivals, and demurrage payments. It is often related to master data, which is relatively stable over time and core to operations, such as assets like chassis, containers, terminal layouts, container types, and customer information like BCOs.
Then, there’s operational data like yard inventory, equipment status, and shift schedules. Add in log data from sensors, TOS, and security logs, and one can quickly become overwhelmed by the sheer volume and complexity. Much of this data is used to calculate analytics for reporting, performance measurement, and decision-making.
What it means to be real-time
Many terminals will extract a limited subset of that data, feed it into a data warehouse, and use it to produce reports. That’s a bit like driving to a national park in a neighboring state using a printed map. You can plan your route based on the roads as they existed when the map was printed and estimate travel time by distance, but that’s about it. The information is static and useful for some planning, analytics, and identifying long-term trends.
Now, let’s compare that to GPS navigation. The system suggests a route based on real-time traffic, accidents, and road closures for optimal navigation. You know exactly when to turn, onto which road, and how much longer the trip will take based on not only distance but factors like speed limits and even weather. You’re warned about upcoming congestion and receive suggested shortcuts with an estimate of potential time savings. You can configure whether you want to take toll roads, avoid highways, or add some stopovers. The information updates in real-time for immediate impact and optimizes every second of your route.
Who wants to go back to the 1990s? Using GPS over a paper map is not a difficult choice but requires a more sophisticated approach. For intermodal terminals, it means feeding all the different types of data into something more than a data warehouse.