What exactly changes on 1 July 2026?
Carriers receive a charge per kilometre driven on the Dutch road network. The rate depends on the vehicle type and the CO2 emissions according to manufacturer data. That sounds straightforward, but the reality is more complex. Because operations differ significantly between carriers, there is no general indexation that already incorporates the levy. Every company must therefore calculate what the levy means per trip, per customer, and per product. At the same time, new flexible labour legislation came into force on 1 January 2026, pushing labour costs higher still. Industry association TLN confirms this is a stacking of costs. ING notes that the Dutch transport and logistics sector will continue to grow in 2026, but that margins vary considerably by activity.
Why is a driver shortage especially painful when costs are rising?
The shortage of drivers and planners is structural, as Timocom reports in its 2026 market overview. New inflow does not cover outflow. That means you cannot simply swap your people for cheaper alternatives, and planners do not have the time to manually review the cost of every trip. If you want the truck levy to be charged through to each customer, you need to be able to calculate that automatically, not with a spreadsheet someone maintains between orders. The capacity pressure in the sector makes inefficient administration twice as costly: you pay for the additional levy costs and for the hours your planner spends trying to work them out.
What does rising cost prices mean for your data infrastructure?
The truck levy makes visible something that was already there: many transport companies have no real-time view of the actual cost price per trip. Trip data, vehicle class, load, kilometres driven, and applicable rates sit in separate systems or are only consolidated after the fact. Evofenedex signals that supply chain professionals in 2026 must invest in chain visibility precisely to absorb shocks. That starts with structured operational data: knowing per order what it costs to deliver it, and applying that rate directly in the quote or customer contract. Without that structure, cost increases are difficult to pass on without losing customers, because you do not know exactly where the pain lies.
How do AI Workers help protect margins?
Once the core system is in place, you can build an AI layer around it that takes over the repetitive calculation work. Think of automatically retrieving the correct levy rates per vehicle, linking trip data to customer contracts, and flagging when an order risks being accepted below cost price. This is not self-learning magic; it is structured calculation work that an employee currently does manually. Bonsai AI Workers run on existing systems and take over that administrative work, so planners focus on the decision, not the data entry. For companies whose core system is due for replacement, a full rebuild as a Digital Twin is the other route: a modern system with cost price logic built in from the start, not bolted on afterwards.
When is this not the right solution?
If the data itself is messy, automation does not help straight away. An AI Worker that calculates levy costs based on incorrect vehicle classes or incomplete trip records will produce wrong results faster. The first step is then to clean and structure the source data, not to build a worker around it. That is an honest point: automation amplifies what is already there, good or bad. Those who do not have their operational data in order need to start there. Only then does an AI layer make sense.