Making transport planning more effective by improving resource utilisation and reducing unnecessary trips.
Arbor-IALogistics
Problem
Traditional transport planning generates waste every day: underutilised vehicles, ineffi cient routes, and avoidable mileage. The result is higher costs and poorly optimised operations.
Solution
An optimisation algorithm analyses shipments, identifi es the best possible combinations, and supports planners in their day-to-day decisions, cutting unnecessary journeys and empty runs.
Benefits
Lower transport operating costs
Fewer kilometres and fewer trips translate into direct cost savings, with a measurable impact on the bottom line.
Better utilisation of fleet and available resources
Vehicles are used to their full capacity, reducing redundancy and optimising the allocation of containers and tractor units.
Greater reliability and control over plannin
The system supports planners in daily decision-making, reducingreliance on manualjudgement and increasing service predictability.
Scalability and adaptability
The model isdesigned to grow and adapteasily to more complex networks, highervolumes, and new operationalconstraints, withoutrequiring a complete redesign.