What's finally making optimization work in the real world
By Hans Galland, Mahriah Alf and Matias Oberpaur
For decades, optimization has been the promised land of fleet operations—a magical algorithm that would deliver perfect routes, maximize efficiency, and transform your bottom line. The only problem? It rarely worked the way it was supposed to.
We sat down with three members of the BeyondTrucks team to understand why optimization has historically fallen short, and more importantly, why that's finally changing. What emerged was a frank discussion about data, disruption, and the gap between academic perfection and operational reality.
For years, fleet optimization has promised lower costs, higher utilization, and smarter dispatching. Yet the industry has largely been disappointed. Most fleets still rely on spreadsheets, gut-driven routing decisions, and disconnected dispatch tools—even in 2025.
So what’s been blocking optimization from delivering on its potential?
It turns out the answer is simple: you can’t optimize what you can’t see.
You Can’t Optimize in the Dark
Until recently, most TMS platforms existed in isolation from the systems that actually power real-time operations. ELDs, telematics, mobile apps, customer portals, and fleet management systems all generate critical data—but the TMS has historically had no way to ingest, unify, or act on it. Without that live, multi-source visibility, any optimization effort is doomed before it starts.
Optimization algorithms can only make smart decisions if they know what’s happening right now—not what was true at 8:00 AM when a plan was first created.
Why Legacy TMS Architectures Can’t Deliver Optimization
Even when data is technically available, traditional systems weren’t designed to harness it. Legacy TMS platforms were built around lists, tables, and manual inputs—not visual tools that help dispatchers make dynamic, complex decisions. Algorithms require integrations, engineering lift, and complicated configuration just to function. And when does reality change? The plan must be rebuilt—usually too slowly to matter.
In other words, optimization became an academic exercise: mathematically brilliant, operationally useless.
Dispatchers eventually gave up fighting the system and went back to manual work. The technology didn’t fail because its math was wrong—it failed because it didn’t work in the real world.
Fleet Optimization Isn’t a Feature. It’s a Strategy.
For the first time, the industry has the data foundation, real-time visibility, decision intelligence, interface design, and configurability required to make optimization not just possible—but profitable. The fleets that embrace this shift won’t just reduce costs. They’ll redefine their competitive position.
Fleet optimization is not finally working because AI got better.
It’s working because the industry finally got ready for it.