Machine Learning & Intelligent Forecasting

We train and validate Machine Learning models using historical container movements and operational context data – such as inbound and outbound transport patterns, typical dwell behaviors, and external influencing factors. Instead of relying on assumptions, the models learn from real outcomes and quantify what is likely to happen next. This forecasting capability helps terminal teams make earlier, more confident decisions, reduce last-minute firefighting, and improve operational stability through better anticipation of demand and workload. Machine Learning can be added to INFORM’s optimization modules like the Yard Optimizer.