Everything depends on when the vessel arrives
A port's operation is scheduled around vessel arrivals: berth allocation, pilotage, labour gangs, cranes, yard space and landside transport. A vessel arriving late disrupts all of it, and the disruption cascades.
Declared arrival times are frequently inaccurate. They are estimates made by the vessel, subject to weather, congestion elsewhere and commercial decisions, and they are often optimistic.
Predicting actual arrival
- Vessel position and speed where AIS data is available
- Historical accuracy of this operator's and this vessel's declared times
- Weather and sea state along the route
- Congestion at the previous port, which propagates
- Season, since weather delays are seasonal
- Tidal windows constraining when arrival is possible at all
The second point is often the strongest and least used. Some operators declare reliably and some do not, and that pattern is stable enough to predict from - a port's own arrival history is frequently more informative than any live feed.
What better prediction enables
| Prediction | Decision it improves |
|---|---|
| Arrival window | Berth allocation, pilot booking |
| Handling duration | Labour and crane scheduling |
| Cargo mix on arrival | Yard space and equipment preparation |
| Landside collection timing | Gate scheduling, congestion |
Handling duration is worth its own attention. It varies with cargo mix, vessel configuration, weather on the day and which gang is working, and planning on an average causes overruns that cascade into the next vessel.
Yard and equipment
Container yard planning benefits from predicting dwell time - how long a container will remain before collection. Containers predicted to leave soon can be positioned accessibly, reducing unproductive moves.
Unproductive moves are a substantial hidden cost, consuming equipment time and fuel to shift containers that are only in the way. Dwell prediction from customer, cargo type, documentation status and history reduces them.
Equipment maintenance is the other clear application. Cranes and handlers are capital-intensive, heavily used and expensive when they fail mid-operation, which makes them good candidates for condition monitoring from existing control data.
Data sharing is the hard part
Much of the useful information sits with other parties - shipping lines, agents, hauliers, customs. Ports frequently cannot see what they need to predict well.
That makes data sharing arrangements as important as the modelling. A port with modest analytics and good visibility of inbound cargo detail will outperform one with sophisticated models and poor data, every time.
The declared arrival time is a plan. The operator's history of hitting it is the prediction.