BeyondTrucks has developed a new AI-powered route optimization tool it says is native to its transportation management system (TMS) and addresses issues related to poor load planning and dispatch decisions.
That’s an issue the company says costs the U.S. trucking industry $150-$200 billion annually.
“Knowledge and experience is invaluable in the industry, but it’s poorly scalable,” Hans Galland, CEO of BeyondTrucks, said during a press conference at the American Trucking Associations Management Conference & Exhibition. “Even if you have an optimization tool, the dispatcher makes constant adjustments and manual overrides that undermine the efficiency those tools were built to deliver in the first place.”
Galland said the new route optimization tool is built directly into the TMS and features a natural language interface that can adjust routing plans in real time based on conversational prompts. He claims it’s the industry’s first AI route optimization tool that’s embedded natively into the TMS.
Allowing humans to lean on their tribal knowledge to route trucks presents problems the AI tool addresses; human decisions scale poorly, they’re inherently biased, and they deliver suboptimal outcomes, Galland said.
Meanwhile, bolt-on optimizers that live on top of the TMS disrupt the dispatcher’s workflow, he added.
“The load planner has to export a list of loads from the TMS, import the file into the optimizer, it gets exported manually and imported manually back into the TMS,” he explained. “By the time that solution is delivered, usually something has changed, and the dispatcher goes back in manually and makes overrides. With us, that’s going to change because our solution is natively delivered in the TMS.”
Dispatchers can use common human language to prompt decisions. Examples: ‘Avoid I-95 due to inclement weather’ or ‘Prioritize deliveries to Walmart over Target’ or ‘Don’t dispatch Johnson to overnight loads because his wife is sick.’
John Carlsson, the data scientist behind the tool, gave some practical examples of when it would be effective. For instance, if one truck in the fleet’s pickups take longer than expected, the dispatcher can use the tool to assign that driver’s final pickup to another, so the driver doesn’t run out of hours or incur overtime costs.
Other options include specifying the trucks start their day at their furthest delivery points and work their way back to the depot.
“Food distribution is where we see the biggest optimization,” Carlsson said.
Galland said AI routing typically allows a fleet to manage the same number of trucks with half the dispatchers. However, he added, customers aren’t taking the opportunity to eliminate human dispatchers, but rather to “make people more influential and powerful with what they’re doing.”
“No one lets this type of talent go because it’s too valuable to the fleet,” he added. “We’ve seen fleets reallocate the time of their dispatchers to take better care of the driver, communicate with them, and become driver care managers instead of load assignment managers.”
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