Downtime cost calculator: turn ‘we should buy a CMMS’ into a number

Why downtime cost beats vague ROI slides

Executives fund problems they can size. “We need better maintenance software” loses to “this line burns roughly $X per year in unplanned downtime.”

A downtime number:

• Makes software cost look small or large in context
• Focuses the pilot on the line that actually hurts
• Gives you a baseline to revisit after work-order and PM discipline improves
• Ends feature-matrix arguments that never touch money

The calculator on this page is a planning tool — not an accounting close. Use it to frame the conversation, then validate with plant reality.

How the downtime cost formula works

The transparent core:

Annual downtime cost ≈ hours per period × periods per year × hourly downtime cost

Examples:

• 8 unplanned hours/week × 52 × $2,500/hour ≈ $1,040,000/year
• 30 unplanned hours/month × 12 × $1,800/hour ≈ $648,000/year

Optional ROI sketch:

Estimated annual savings ≈ annual downtime cost × expected reduction %

Be conservative on the reduction percentage. A CMMS does not erase physics; it improves visibility, ownership, PM completion, and repeat-failure attack. Double-digit reductions are a hypothesis to test on one line — not a vendor promise to put in a board deck without a pilot.

How to estimate hourly downtime cost honestly

Understating hourly cost makes the annual number look comfortable. Overstating it makes finance distrust the exercise. Build the hourly rate from pieces you can defend:

• Lost throughput / margin on units not produced during the stop
• Direct labor still paid while the line is down
• Overtime or weekend recovery to catch the schedule
• Scrap, rework, and startup waste after the restart
• Expedite freight or customer penalties when shipments slip
• Secondary stops on dependent equipment when that is material

If you only know a rough range, pick a midpoint and note the assumptions. A documented $1,500–$3,000/hour range is more useful than a fake precise $2,173.

Separate unplanned downtime from planned windows. Mixing sanitation or changeovers into “downtime cost” inflates the CMMS case and weakens credibility.

From annual downtime cost to CMMS ROI

Once you have an annualized downtime cost:

1. Compare it to facility software cost for the year (subscription + realistic internal time).
2. Ask what fraction of downtime is repeat failures and slow handoffs — the portion a CMMS can influence.
3. Apply a conservative reduction % as a what-if, not a guarantee.
4. Require a line pilot to see whether completion and history actually move.

ROI framing that helps:

• “If we cut 10% of $800k unplanned downtime, that is $80k — against a facility plan in the low five figures.”
• “If we cannot move the number after 60 days of owned work orders, we revisit scope — not feature checklists.”

Avoid miracle percentages. Plants respect modest, tested savings more than slide-ware.

What a CMMS can (and cannot) change

A CMMS can help you:

• Capture stops as owned work on the asset
• Surface overdue PM and open work before they become surprises
• Attach parts and history so MTTR drops on familiar failures
• Make shift handoffs less dependent on chat memory
• Focus weekly reviews on repeat offenders

A CMMS cannot:

• Replace unreliable equipment or missing spare strategy by itself
• Invent production windows that do not exist
• Guarantee uptime without operators and technicians changing habits
• Fix chronic under-staffing or impossible schedules

Software amplifies a loop. If nobody claims work, the calculator’s what-if savings will not appear.

Worked example for a mid-sized line

Illustrative only — replace with your numbers.

• Line: packaging line 2
• Unplanned downtime: 6 hours/week (averaged over the last quarter)
• Fully loaded hourly cost: $2,200/hour (margin + recovery overtime + typical scrap)
• Annual downtime cost: 6 × 52 × 2,200 = $686,400

What-if after CMMS discipline on that line (history, PM in windows, parts kitted):

• Assumed reduction: 12% of unplanned hours on repeat/organizational failures
• Estimated annual savings: 0.12 × 686,400 ≈ $82,400

That savings estimate is a hypothesis. The next step is not a bigger spreadsheet — it is a pilot that forces every meaningful stop into owned history for several weeks, then compares reality to the sketch.

Next steps after you have the number

1. Name the line that produced the estimate — buy for that pain, not for the whole plant abstractly.
2. Pilot a CMMS with critical assets and live work orders (see [CMMS implementation timeline](/guides/cmms-implementation-timeline)).
3. Attack repeat failures first using asset history — see [how to reduce equipment downtime](/guides/reduce-equipment-downtime).
4. Re-run the estimate after 30–60 days with better hour tracking from work orders.
5. Expand only after the costly line shows a tighter loop.

You already have the calculator above for the math. Use Corivo on the line behind the number so the next downtime conversation cites work orders — not anecdotes.