Most infrastructure programs track what already broke—not what's about to. These 10 predictive maintenance KPIs give infrastructure managers the forward-looking signals they need to prevent failures, justify CapEx, and prove ROI. Check each KPI your program currently tracks. Book a Demo to see how iFactory auto-calculates every metric below in real time.
The 10 KPIs: Your Tracking Checklist
Tick each KPI your program actively measures today. Every gap is a blind spot in your infrastructure oversight.
Tracks how long assets operate before a failure event. A declining MTBF trend over 12 months is the earliest warning sign of accelerating structural fatigue. Target: trending upward Q-o-Q; any single-quarter drop of 15%+ triggers an inspection.
Measures how fast your team diagnoses and resolves a structural failure. AI pre-routes diagnosed failure data to field technicians before they arrive on site, slashing MTTR. World-class target: under 4 hours for critical asset failures.
AI composite of vibration, corrosion, load stress, thermal, and historical repair data — scored 0–100 and updated in real time. AHS above 75 = healthy; 50–74 = monitor closely; below 50 = work order auto-triggered.
Physics-of-Failure AI model that projects how many years an asset has left at its current degradation rate. Prevents premature multi-million dollar replacements and unlocks emergency CapEx before failures occur — not after.
The AI's direct prediction — expressed as % likelihood of failure within 24 hours, 7 days, or 30 days. Turns AI from a black box into a labor-prioritization engine. Any asset exceeding 40% PFP in a 30-day window triggers automatic work order escalation.
Percentage of your highest-risk structural nodes that are actively monitored. A portfolio with 60% sensor coverage has a dangerous blind spot. Target: minimum 80% on Tier 1 critical assets; 100% within 18 months of program launch.
Single ratio that reveals your entire program's maturity at a glance. A 70% reactive program is burning emergency premiums and public safety. World-class programs achieve 90%+ planned maintenance. Industry average sits at just 55–65%.
Holds your predictive platform accountable — did predicted failures actually occur? Monitoring accuracy monthly ensures the model keeps improving. Mature models reach 85–95% accuracy. Any month below 80% triggers model recalibration.
Reveals which structures are consuming disproportionate budget. Maintenance cost should stay below 2.5% of asset replacement value annually. Above 4% signals end-of-life candidacy — a data point that prevents over-investing in a dying asset.
If 100 inspections produce only 8 work orders, 92 were wasted field labor. AI-routed inspection programs achieve 40–60% conversion vs. 8–15% for reactive programs. This KPI directly proves PdM ROI to leadership.
How the 10 KPIs Stack Together
These metrics form three layers of intelligence. Tracking only one layer gives you an incomplete — and potentially dangerous — picture.
