PMARIS Asset Reliability
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Levers · what management can move

Four levers separate the low-downtime windows from the high ones

Strategy, spare-parts availability, crew skill and PM discipline each split downtime by roughly 2x to 3x, and every one of those splits survives a Bonferroni-corrected test. Reactive runs 2.3x predictive, shortage 2.9x available, basic crews 1.8x expert.

ℹAssociation, not proof of cause. Reactive windows also start in worse health, so part of the gap is which assets end up reactive. The intervals below are study constant bootstrap results on the full file; the bars and curves are live.

Downtime by maintenance strategy, with 95% intervals study

Mean unplanned hours per observation. Non-overlapping intervals mean the ranking is reproducible. Preventive, condition-based and predictive overlap: the step that matters is leaving reactive.

Downtime by spare-parts status, with 95% intervals study

All three intervals are clear of each other: each step up in availability is a real reduction.

Downtime by technician skill, with 95% intervals study

Basic and intermediate crews sit clearly above advanced and expert.

Downtime falls as PM compliance rises

Mean downtime per observation, by PM-compliance decile (D1 = least compliant). Live.

Lever thresholds: below versus above study

Each lever split at a practical operating threshold. p-values from one-way ANOVA; η² is the share of downtime variance the split explains.

Revenue loss climbs steeply as health falls

Mean revenue loss per observation by Asset Health Index band. Live. Windows under 70 lose about 9x the 90+ band.

So what. Moving reactive windows onto any planned strategy, clearing spare-part shortages and lifting PM compliance above 80% each target a measured 2x gap. Those are budget lines, not hopes, and they apply at every site.