IndustrialServices

When technician utilization and work order data live in separate systems, service profitability becomes a guessing game.

We unify your FSM, ERP, and scheduling data so operations leaders can see technician capacity, work order backlog, and service contract margin in one place, updated daily.

27%
field utilization gain

Close the gap between field and office: real-time technician utilization, work order throughput, and asset reliability in one unified view.

The Challenge

The operational reality of industrial services

01

Technician imbalance nobody sees in time

Some technicians are over-scheduled while others sit underutilized. The imbalance costs money on both ends and nobody has a live view to fix it.

02

Service contract margins drift without tracking

Contract margins look fine on paper until someone does the math job by job. By then, several contracts are already losing money.

03

Asset and maintenance data in separate systems

Asset uptime sits in one system. Maintenance costs sit in another. Finance has neither. Nobody can see the full picture of what each asset costs to run.

04

Dispatch runs on gut feel

Dispatch decisions are made on experience and habit rather than route optimization, technician skill matching, or live availability data.

Key Metrics

The KPIs we surface for industrial services firms

Technician utilization
Work order completion rate
Service contract gross margin
First-time fix rate
Mean time to repair (MTTR)
Asset uptime
Customer SLA compliance
Revenue per technician
Travel time as % of billable
Backlog by service line
Capabilities

How we help industrial services firms

01

Dispatch optimization and route planning

Route optimization models that reduce travel time, match technician skills to job requirements, and improve daily dispatch efficiency.

02

Real-time technician utilization dashboards

Live views of technician utilization, work order queues, and SLA status. Dispatch managers see the full picture without making phone calls.

03

Service contract profitability tracking

Job-level cost tracking against contract value. Know which contracts are profitable, which are breaking even, and which are losing money.

04

Predictive maintenance models

Equipment failure prediction built on service history, asset age, and condition data. Shift from reactive to proactive maintenance.

05

AI agents for work order triage

AI triage that reads incoming work orders, scores priority, matches technician skills, and suggests dispatch assignments automatically.

Integrations

Some of the systems we connect with

These are some of the platforms we connect. We integrate with almost any system. If you run it, we can likely work with it.

ServiceMax
ServiceTitan
FieldEdge
Salesforce Field Service
IFS
SAP S/4 HANA
Microsoft Dynamics 365 Field Service
IBM Maximo
ServiceMax
ServiceTitan
FieldEdge
Salesforce Field Service
IFS
SAP S/4 HANA
Microsoft Dynamics 365 Field Service
IBM Maximo
Use Cases

Where this applies

01

Mechanical services: dispatch optimization across 80 technicians

An HVAC and mechanical services firm dispatched 80 technicians from a central office using a combination of phone calls and a basic scheduling spreadsheet. Average travel time was 38% of billable hours.

Outcome

We integrated ServiceTitan with a route optimization model. Travel time dropped to 24% of billable, and the dispatch team reduced daily call volume by 60%.

02

Industrial firm: service contract margin by job

A multi-site industrial services company had 140 active maintenance contracts. Leadership assumed contracts were profitable. A bottom-up analysis revealed 22% were operating below break-even.

Outcome

We built job-level cost tracking against contract value in real time. The firm renegotiated 18 contracts and eliminated 4. Overall contract portfolio margin improved by 14 points.

03

Equipment services: predictive maintenance model

A firm servicing industrial equipment at client sites was responding reactively to equipment failures. Each unplanned callout cost 3 to 5x more than a scheduled service visit.

Outcome

We built a predictive maintenance model on equipment telemetry and service history data. The model flags high-risk assets 10 to 14 days before failure with 82% accuracy, enabling proactive scheduling.

Results

Outcomes you can expect

27%
Increase in field utilization
15%
Improvement in first-time fix rate
20%
Reduction in unplanned downtime
Live
Service contract margin tracking by job
FAQ

Frequently asked questions

Get Started

Know what your field operation is actually producing.

Book a free call and we will map out what industrial services analytics looks like for your business.