Predictive Maintenance Ticket Creation
Turn sensor trends into work orders before equipment fails. AI detects degradation patterns, creates tickets in ServiceNow, checks parts availability, and schedules maintenance — all before your first breakdown.
Your maintenance program is reactive and everyone knows it
Unplanned downtime costs the average manufacturer $260,000 per hour. Yet most maintenance programs are still reactive — wait for the machine to break, then scramble to fix it. The "preventive" alternative isn't much better: calendar-based PMs that replace parts on schedule whether they need it or not, while missing the failures that happen between intervals.
The irony is that the data to predict failures already exists. Vibration sensors, temperature probes, pressure transducers, current monitors — your equipment is constantly telling you how it feels. But that data sits in historians and dashboards that nobody watches 24/7. By the time a maintenance tech sees a trend, the bearing has already seized.
What's missing isn't data. It's the bridge between sensor trends and maintenance action — an AI that watches every sensor continuously, recognizes the patterns that precede failures, and creates a work order with the right parts and the right timing before anything breaks.
From sensor signal to work order — automatically
Sensor Monitoring
SectorFlow continuously ingests data from vibration sensors, temperature probes, pressure transducers, current monitors, and other IoT devices across your facility. Every reading is baselined against normal operating parameters for that specific piece of equipment.
Trend Detection
The AI identifies early-stage degradation patterns — gradual vibration increases, slow temperature drift, pressure oscillations — that indicate a component is wearing out. These trends are often invisible in daily monitoring but unmistakable when analyzed over weeks.
Pattern Matching
Detected trends are compared against historical failure data for that equipment type. The AI correlates sensor signatures with past breakdowns to estimate time-to-failure, identify the likely failing component, and assess production impact if the failure occurs.
Work Order Creation
When confidence exceeds your threshold, the AI automatically creates a work order in ServiceNow or SAP PM — complete with equipment ID, predicted failure mode, recommended repair actions, and estimated time-to-failure. No manual ticket entry required.
Parts & Scheduling
The AI checks parts availability in your inventory system, identifies the optimal maintenance window based on production schedules and crew availability, and attaches everything to the work order. Maintenance gets a complete package: what's failing, what's needed, and when to do it.
This isn't a dashboard — it's a maintenance intelligence engine
Every capability your maintenance team needs to move from reactive to predictive, built in from day one.
Vibration, Temperature & Pressure Trend Analysis
Continuously monitors all sensor types for early degradation signatures. Detects subtle changes — bearing roughness, thermal drift, seal wear — weeks before they become critical.
Historical Failure Pattern Matching
Compares current sensor signatures against your facility's failure history. When the AI recognizes a pattern that preceded past breakdowns, it acts — not waits.
Auto Work Order Creation
Creates fully detailed work orders in ServiceNow or SAP PM automatically — equipment ID, failure mode, repair steps, priority level, and estimated labor hours. Zero manual data entry.
Parts Availability Check
Automatically queries your parts inventory before scheduling maintenance. If the part isn't in stock, it flags the shortage and triggers a procurement request so nothing delays the repair.
Maintenance Window Optimization
Identifies the best time to perform the repair by cross-referencing production schedules, crew availability, and time-to-failure estimates. Minimizes production impact while ensuring timely intervention.
Cost Avoidance Tracking
Tracks every predicted failure against the cost of unplanned downtime it would have caused. Gives your maintenance team hard dollar figures to prove ROI — not just uptime percentages.
Connects to the systems you already run
ServiceNow
SAP PM
Jira
Splunk
Datadog
IoT Platforms
CMMS
Don't see your maintenance system? We integrate with any platform via API. Talk to us.
What maintenance teams are seeing
Reduction in unplanned downtime
Avg. annual cost avoidance
Avg. early warning before failure
Based on pilot deployments. Your results will depend on equipment age, sensor coverage, and maintenance maturity.
"We had a gearbox on Line 4 that showed a subtle vibration increase over three weeks. Our techs would never have caught it — it was well within the normal alarm threshold. The AI flagged it, created a work order, confirmed the replacement gear set was in stock, and scheduled the repair for our next planned downtime. When we opened the gearbox, two teeth were cracked. That would have been a $400K failure and three days of lost production."
— Maintenance Director, Automotive Parts Manufacturer
Frequently Asked Questions
See What Predictive Maintenance Looks Like for Your Facility
Book a 30-minute discovery call. We'll walk through the Predictive Maintenance flow with your equipment data and your maintenance workflows.
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