Support & Ops

Mean Time to Resolution

Paste an incident CSV. MTTR per severity, MTBF, monthly trend, and outliers.

mttr.console/9 incidents
03:26 PM
1.5hours100/100
Mean time to resolution
1.49h79%

Across 9 incidents spanning 3 months. Lower is better — benchmark P1 median is 24h.

MTBF
243.7h
Rate
3.3/mo
SEV1
2
Outliers
0

Incident CSV

id, opened, closed, severity, cause

Timestamps: ISO or YYYY-MM-DD HH:mm. MTTR = mean(closed − opened). MTBF = mean gap between a close and the next open.

By priority

MTTR vs benchmark
P0SEV1· Critical · 2
3.25h
median 4h19% better
P1SEV2· High · 3
1.49h
median 24h94% better
P2SEV3· Medium · 4
0.62h
median 72h99% better

Marker = industry median (Atlassian + PagerDuty 2024).

By category

root cause · mttr
deploy-regression
1.3h×3
config-drift
0.6h×2
database-failure
3.3h×2
third-party-outage
1.4h×1
capacity-exhaustion
0.5h×1

Percentile distribution

p50 → p99
p50
1.4h
p75
1.6h
p90
3.1h
p95
3.3h
p99
3.5h

p99 is the tail you negotiate SLAs against. If p99 ≫ p50, fix outlier classes first.

Monthly trend

volume · mttr
2026-01
1.92h×3
2026-02
0.92h52%×3
2026-03
1.64h79%×3

Outliers

> 3.7h

No outliers detected.

P0 (SEV1) Pareto

2 critical · root causes
database-failure
2 · 100% (cum 100%)

Marker = cumulative %. Causes that cross 80% are your highest-leverage fixes.