TL;DR: A residential plumbing company in Dallas-Fort Worth had 22 techs covering a 60+ mile service area. Dispatchers routed jobs based on "who's available" rather than who's closest or best qualified. Techs wasted 90+ minutes daily in unnecessary driving. We built an agent that tracks real-time location, matches skills to job requirements, factors traffic, and recommends optimal assignments. Drive time dropped 28 minutes per tech per day. Job completion increased 18%. The senior tech called the owner to say he actually had time for lunch.
Passing Each Other on the Highway
Mike runs a residential plumbing company in the Dallas-Fort Worth metroplex. 34 employees. 22 field technicians. Service area spanning Tarrant, Dallas, Collin, and Denton counties. Over 60 miles across.
The dispatch situation was chaos that looked like order.
Two dispatchers, both experienced, both doing their best. Jobs came in. They'd check the board, see who was available or finishing up soon, and assign the next job.
Simple enough. Except it wasn't working.
A tech would finish a job in Plano and get dispatched to Fort Worth. Meanwhile, another tech finishing in Irving got sent to McKinney. They'd literally pass each other on the highway, each driving 45 minutes to a job the other one could have reached in 15.
"I spend more time driving than fixing pipes," one of the senior techs told Mike. That wasn't quite true mathematically. But it felt true. And when your best people feel like they're wasting their day in traffic, you've got a problem.
The Math Nobody Was Tracking
Mike had a vague sense that routing could be better. He didn't know how much better until we measured it.
90+ minutes per day in unnecessary drive time. Per tech. That's across the whole team, obviously some worse than others, but the average was brutal.
25% of dispatches were suboptimal. Not disasters—the job still got done—but a different tech could have gotten there faster.
$4,200 per month in excess fuel costs. Gas adds up when you're driving in circles.
15% fewer jobs completed than the team actually had capacity for. They weren't slow. They were stuck in traffic.
The dispatchers weren't incompetent. They were working with incomplete information. They could see who was available. They couldn't see where everyone was in real time. They couldn't calculate drive times accounting for current traffic. They couldn't remember which techs had which certifications.
For more on how to calculate what inefficiencies like this actually cost, we've built a framework.
The Skill Matching Problem
Here's what made this harder than simple distance optimization.
Not every tech could do every job.
Tankless water heaters? Only 5 techs trained on those. Send the wrong person and they'd have to call for backup or reschedule the customer.
Commercial jobs? Specific insurance requirements. Only certain techs qualified.
Gas line work? Separate certification. 8 techs had it, 14 didn't.
Water heater installations vs. drain cleaning vs. leak detection—different equipment needed, different skill sets.
The dispatchers knew most of this. They had a mental map of who could do what. But under pressure, with phones ringing and jobs stacking up, they'd sometimes send whoever was available and deal with the mismatch later.
"Later" usually meant an unhappy customer, a wasted trip, or a tech standing in someone's garage calling the office saying "I can't do this job."
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