In dispatching, a perfect route plan built at 6 a.m. rarely survives contact with 9 a.m. traffic, a sick driver, or a carrier that goes dark. HERE Technologies is betting the fix is not a better plan, but a system that keeps learning from what actually happens after the plan leaves the office, according to a FreightWaves report from Home Delivery World.
Tour Planning Upgrades: Time, Territory, and Walking
Tour planning is one of HERE’s oldest services, in development for a decade. Bart Coppelmans of HERE Technologies told FreightWaves that new features are pushing adoption higher. “We started developing this ten years ago, but it’s really picking up in the market now as one of the best performing solvers, especially because of what we added last year,” Coppelmans said.
Chief among those additions is time-dependent optimization, which accounts for how traffic changes delivery capacity throughout the day. “At nine o’clock in the morning you can deliver fewer orders than at one o’clock in the afternoon because of traffic jams,” Coppelmans explained.
HERE also added driver-friendly overlapping tours, which cut down on territory conflicts drivers hate seeing on their routes, along with walk clustering, a feature that identifies when a driver should park once and deliver several stops on foot rather than repeatedly pulling in and out of a vehicle. “From one parking spot you can then deliver by walking to multiple different deliveries in a certain area, which might be more efficient than driving in and out of your vehicle,” Coppelmans said.
Last Meter Guidance Closes the Loop
None of that solves the deeper problem Coppelmans wanted to discuss: the gap between what dispatch plans in the morning and what a driver actually encounters in the field. “If you have a perfect plan by six in the morning, by nine it can already be different because of unexpected events — a driver getting sick, a carrier going dark or last-minute order changes,” Coppelmans said. “You need to be really dynamic and flexible, taking that into account.”
HERE’s answer is Last Meter Guidance, a client-side service that runs on a handheld device or driver app and collects sensor and positioning data from the field. “We’re automatically collecting sensor and probe positioning points, we have our own positioning stack,” Coppelmans said. “This service builds on top of that, really making sure we’re learning from the field. We’re collecting traces data from where the vehicle is parking, the walk path toward the building, flagging the building entrance and the final delivery end-point location.”
That data flows in both directions. Dispatchers get more accurate delivery windows, and drivers get parking and entrance guidance built on where previous drivers actually succeeded, not just where a map thinks a building’s front door is. “There’s no disconnect anymore,” Coppelmans said. “Drivers are more comfortable trusting what is being planned and can say, ‘Okay, this makes sense.’”
AI Route Optimization Learns to Explain Itself
Sitting on top of both services is what HERE refers to as a route optimization cognitive layer, a prototype agentic capability the company expects to move into closed beta later this year. Where the underlying tour planning API tells a dispatcher what to do, the reasoning layer is meant to tell them why.
“Why are these orders unassigned? Why are these two trucks going down the same street on the same day?” Coppelmans said. “It might be because of actual constraints, driving skills, or certain priorities.” The layer doesn’t stop at explanation. It is built to suggest fixes too, the kind of adjustments a veteran dispatcher makes on instinct.
Implications for Supply Chain Technology
For enterprise technology leaders, HERE’s approach represents a shift from static optimization to continuous learning. The combination of real-time field data from Last Meter Guidance with an explainable AI layer could reduce the friction between planning and execution that plagues last-mile logistics. By giving dispatchers both the plan and the rationale, HERE aims to increase trust in automated routing, potentially improving driver retention and customer delivery accuracy. The closed beta later this year will be a key milestone for logistics tech investors and CTOs evaluating AI-driven supply chain solutions.
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