OEE Lab / CMMS / Monitoring ROI Calculator

CMMS / Monitoring ROI Calculator

Model the payback of machine monitoring and a closed-loop CMMS against your current downtime losses. Numbers update as you type.

Your case

Estimate conservatively - a business case is stronger when the assumptions are defensible.

Not sure? Use the Downtime Cost result.
Industry benchmark for manual → automatic monitoring with root-cause: 25–35%.

The investment

Setup = sensors, integration, onboarding.
Net savings - per year
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After the annual software cost.
paybackfast← months →slow
Payback
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ROI / yr
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Savings / yr
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3-year net
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This is the closed loop Fabrico is built for.

Reduction only happens when you can see the true cause and act on it. Fabrico reads OEE from the machine, shows the cause on video, and auto-routes the work order - the measure-and-act loop these numbers assume.

See Fabrico's closed loop
OEE Lab is built and operated by Fabrico. Our tools stay free either way.

How to build the ROI case

The math is simple and defensible:

  • Annual savings = current downtime/loss cost × expected reduction.
  • ROI = (annual savings − annual software cost) ÷ annual software cost.
  • Payback (months) = (setup + first-year software) ÷ (monthly savings).

Use a conservative reduction figure. Even at the low end of the 25–35% benchmark, a single line's downtime cost usually dwarfs the software cost - which is why payback typically lands inside a year.

Where do the savings actually come from?

Fewer and shorter stops (more uptime), less scrap at restart, and less idle paid labor - plus catching the recurring failure modes before they repeat. Quantify the starting point with the Downtime Cost and Hidden-Factory tools.

Is 25–35% reduction realistic?

It's a commonly cited benchmark for moving from manual logging to automatic monitoring with root-cause visibility. It's a benchmark, not a guarantee - model your own conservative number.

What's the catch?

Monitoring alone doesn't reduce anything - you have to act on what it shows. The ROI assumes a closed loop: measure, diagnose the true cause, and route a fix.

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