How Troiano Waste Services got ahead of exhaust failures.
Preventive maintenance was already in place. The telematics were already running. Exhaust and aftertreatment failures still arrived as derates in the middle of a collection route, and the same units kept coming back through the shop. This is what changed when the data already coming off those trucks started predicting the failure instead of reporting it.
- Fleet
- 55 heavy-duty collection vehicles
- Based
- Portland, Maine. Hauling since 1976.
- Running
- Samsara telematics and CMMS repair history
- In scope
- Exhaust and aftertreatment systems
What the engagement produced.
fewer exhaust-related repairs, in the first 10 weeks
recovered in those 10 weeks, across 55 trucks
realized savings at the one-year review
Source: Troiano Waste Services, Maine
Measured across the pilot, on 55 trucks.
- Exhaust-related repairs
- down 41%
- Direct repair costs avoided
- $6,984
- Lost revenue avoided on prevented disruptions
- $11,200
Measured at the engagement review.
- Per truck, per year, across 55 trucks
- about $1,600
- Service disruptions avoided
- 4+ a week
- Basis
- Their own repair history
The challenge
Troiano was not short of data. Soot built up faster than passive regeneration could clear it, so DPF clogging kept recurring on the same units. The failures landed as derates on a collection day, which meant a roadside call, a missed route and rework in the bay.
Clogged filters also put EPA compliance at risk, and every unplanned repair drew on a technician pool the whole industry is short of. The alerts the shop already had told them a fault had happened. Nothing told them which truck was about to have one.
- 150+ exhaust incidents a year
- 10%+ of annual maintenance spend
- ~30 exhaust issues in a peak month
Source: Troiano Waste Services, Maine. The fleet position before the engagement.
The approach
Read-only on the data Troiano already had. Nothing went on the truck, and nothing changed about how the shop runs its day.
Data integration
We ingested the telematics feed, the repair history and the contextual fleet data directly, read-only. No manual prep, no new hardware, no export work for their team.
Physics-based AI modeling
The models track filter regeneration and soot buildup by vehicle type and duty cycle, alongside trouble codes. An alert fires only when the physics and the codes agree.
Alerts the shop can act on
Daily risk alerts arrive aligned to the PM schedule, with the recommended action for each vehicle. Technicians bundle the corrective work into planned shop time.
Hear it straight from the fleet.
For the shop, the biggest win was how simple this was for the technicians. They didn’t need to learn a new tool or change their routine.
Scott Lane Fleet Manager, Troiano Waste Services
Tensor Planet’s system connected directly with the data we already had, without requiring any manual prep work or new hardware.
Abel Cota Director of Information Systems, Troiano Waste Services
This initiative fits perfectly with our focus on innovation and efficiency. The financial results speak for themselves.
TJ Troiano COO, Troiano Waste Services
What it is like knowing about a breakdown before it happens.
Abel Cota, Director of Information Systems, Troiano Waste Services
How complex the integration really was.
Abel Cota, Director of Information Systems, Troiano Waste Services
