23% fewer miles per stop. 49% more production per work day. Same team.
A multi-technician pest control operation on FieldRoutes was running routes that looked functional but were quietly capping production and wasting miles every single day. Here is what we found, what we changed, and what happened.
−23%
Miles per stop
Miles traveled per completed service call — across the full fleet
−15%
Cost per stop
Drive time cost per service call, through tighter route sequencing
+49%
Production per work time
Production output per technician per work day after implementation
+1–2
More stops per tech/day
Average additional completed appointments per technician per day
Before we touched a single route, we measured what was actually happening.
Route optimization only means something if you know where you started. We pulled the baseline from FieldRoutes directly — no manual exports, no estimates — and established three KPIs that would serve as our measurement standard throughout: miles per stop, production per work time, and cost per stop.
Measured
Miles per stop
Total daily route miles divided by stops completed — the core routing efficiency metric
Measured
Production per work time
Average daily production per technician tracked against total time on route
Measured
Cost per stop
Drive time and fuel cost allocated across each completed service call
What was holding the algorithm back.
Before running any optimization, we catalogued every constraint in the system — not to remove them all, but to understand which ones were real business requirements and which ones had simply accumulated over time.
Preferred Times
Customers assigned specific service windows — many of which were configured by default or habit rather than actual customer need.
Preferred Technicians
Customers linked to specific technicians by preference — some reflecting genuine relationships, others set by habit or historical assignment rather than current territory logic.
Preferred Weekdays
Day-of-week locks on recurring subscriptions. These are especially limiting because the algorithm must maintain recurring patterns — a Wednesday customer cannot simply be moved to Thursday without operational impact.
Theoretical ceiling
First we removed all constraints — to see the maximum possible improvement.
Running the algorithm with zero constraints shows the absolute ceiling: what could be achieved if every customer could be scheduled freely. This number is not the goal — it is a reference point. The gap between the baseline and this ceiling shows where value is being left behind.
The key question: who actually needs an exact time promise?
Preferred time constraints — especially exact-time windows — are the most restrictive for route planning. We worked with the operation to go through their customer list and identify which time windows were genuine commitments versus defaults that had never been reviewed.
Preferred weekday constraints are particularly limiting because they interact with recurring frequency patterns — a customer locked to Wednesdays cannot be shifted without rescheduling all their future appointments. The fewer of these locks in the system, the more efficiently the algorithm can organize recurring routes.
How Exact Time vs. Preferred Time affects route efficiencyMultiple configurations. One decision.
With constraints reduced to only what was genuinely necessary, we ran the algorithm across multiple technician configurations. Each scenario was evaluated against the same KPIs — so the comparison was apples to apples.
| Scenario | Team | Miles/stop | Stops/tech/day | Drive time | Production/day |
|---|---|---|---|---|---|
Baseline (no optimization) | Current team | Reference | Reference | Reference | Reference |
Full team — constraints reduced | Current team | ↓ improving | ↑ improving | ↓ improving | ↑ improving |
Approved scenario — optimized | Current team | −23% | +1–2 stops | −15% | +49% |
Minus one technician (simulated) | Current − 1 | Better | ↑ higher/tech | ↑ higher load | Stress-tested |
The highlighted scenario was the one approved by the operator. Changes were implemented directly in FieldRoutes after sign-off.
Same team. Significantly better numbers.
After implementation, the operation measured the same KPIs from the baseline. The improvements were consistent across the fleet — not a single outlier technician, but a structural shift in how routes were organized.
−23%
Miles per stop
Miles traveled per completed service call — across the full fleet
−15%
Cost per stop
Drive time cost per service call, through tighter route sequencing
+49%
Production per work time
Production output per technician per work day after implementation
+1–2
More stops per tech/day
Average additional completed appointments per technician per day
What this means in practice
The same team covered the same territory — but with routes that reflected how the operation actually worked. Production per work time improved by 49%, and the operation gained meaningful capacity without adding a single technician, truck, or additional work hours.
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