Model a specific incident — or a typical one — against your own revenue, labor, and recovery costs. Adjust the fields on the left and watch the breakdown update instantly on the right. No wizard, no signup. Part of a 5-tool suite — see tabs above.
Per-incident cost × incidents/year. See the Cost Index for how this compares across incident types and company sizes.
Get a copy of this breakdown sent to your inbox — useful for sharing internally. No blur, no gate — this is just so you don't lose it.
This model uses your own inputs, not a universal industry average, to price out a specific incident. Four components anchor the math:
Repair/recovery cost is a direct passthrough of what you enter. The secondary-cost multiplier reflects that the same dollar of scrap or penalty tends to carry more downstream risk for a quality or safety event (recall, regulatory, reputational exposure) than for a straightforward equipment fix — currently 1.4× for safety incidents, 1.25× for quality defects, 1.1× for supply chain disruption, 1.05× for power outages, and 1.0× for equipment and software/OT failures. Annualized exposure is a simple linear projection (per-incident cost × incidents/year) rather than a compounding model — unlike technical debt, recurring operational incidents don't reliably compound year over year, so we don't pretend they do.
This is an educated estimate, not an audit. Figures are directional (typically ±30–40%) and derived entirely from the numbers you enter. We have no access to your production data, maintenance records, or financials — nothing here constitutes a financial, engineering, or insurance assessment.
Use this to frame an internal conversation about where downtime is actually costing you money and where a reliability investment would pay for itself — not as a precise forecast or a substitute for a formal operational assessment.
This calculator is one of five free, vendor-neutral tools for quantifying and acting on operational downtime.
The hub page — headline benchmarks, cost components, and links into the full suite.
Open Overview →Benchmark tables by incident type, duration, and company size for SMB manufacturing.
View the Index →Deep dive on each incident category — cost drivers, typical duration, and mitigations.
Browse Types →MTTR, MTBF, OEE, RTO/RPO, and the rest of the operational-reliability vocabulary, plant-floor edition.
Read the Glossary →Divide annual revenue by your actual scheduled production hours per year (not calendar hours) — e.g., two shifts × 5 days × 50 weeks ≈ 4,000 hours. A $20M/yr plant on that schedule runs at roughly $5,000/hr. If output varies a lot by product line, use the line's own revenue and hours instead of the whole plant's.
Most lines don't return to full output the instant power or the machine comes back — there's a warm-up, requalification, or re-sequencing period. Ignoring it is one of the most common ways teams understate what an incident actually cost.
Select "No — sent home / reassigned" and the labor-cost component drops to zero, since that cost isn't actually incremental. If they're doing lower-value work instead of being fully idle, you could still estimate a partial cost manually and add it to the repair/recovery field.
It only applies to the scrap/rework and penalty inputs, not your whole total — the idea is that the same dollar of scrap carries more downstream tail risk (recall, regulatory action, reputational damage) for a quality or safety event than for a routine equipment fix. It's a modeling judgment call, not a cited statistic — adjust your penalty/scrap inputs directly if you think it overstates or understates your situation.
Yes — just scale your revenue-per-hour and headcount inputs down to that workstation or cell's actual output and staffing, rather than the whole plant's.