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    ROI Calculator

    Predictive Maintenance ROI Calculator

    Estimate annual savings from reducing unplanned downtime on critical rotating equipment.

    AI

    Quick Answer

    This calculator estimates annual savings from reducing unplanned downtime, using your own asset count, downtime hours, cost per hour and an assumed reduction. The default reduction is a planning assumption, not a measured result; achievable reduction depends on failure modes, sensor coverage, data history and whether maintenance can act on the warning in time.

    Inputs

    Your Numbers

    Pumps, compressors, motors, turbines

    Lost production + repair + penalties

    Results

    Illustrative Annual Value

    Baseline annual downtime cost$3,000,000
    Downtime hours avoided per year210 h
    Estimated annual savings$1,050,000

    Illustrative estimate only — not a quote, forecast, guarantee or verified customer outcome. Replace every default and assumption with your own approved baseline before making an investment decision.

    Book a Strategy Call

    For a decision-grade business case, replace the assumptions with approved operating and cost data.

    Assumptions

    How This Illustrative Estimate Is Calculated

    • Savings = assets x hours x cost x reduction. The reduction percentage is yours to set: it should come from your own failure history and sensor coverage, not from a published industry figure.
    • Cost per hour should include lost throughput, expedited repair, and contractual penalties. For oil and gas, add deferred production at current commodity prices.
    • Numbers exclude implementation cost. NeoBram typically delivers a production pilot in 6 to 8 weeks and full enterprise deployment in 3 to 6 months.

    Direct answers

    Before you rely on this number

    What reduction percentage should I actually enter?

    Derive it from your own history rather than a published figure. Count the unplanned stops on the assets you would monitor over the last two years, then judge how many showed a detectable precursor — vibration drift, temperature rise, a repeated operator note — far enough ahead to act. That proportion is your realistic ceiling, and only part of it converts into avoided downtime.

    Does this work without vibration sensors?

    Partly. Historian tags, motor current, temperature, and structured maintenance logs can support useful failure prediction on many assets. Sensor coverage mainly determines which failure modes are visible at all: a model cannot predict a mode it has no signal for, regardless of how much history you hold.

    Why does the estimate exclude implementation cost?

    Because implementation cost depends on integration scope, deployment boundary and how much data preparation your historian needs — variables this calculator does not ask for. Treat the output as gross annual benefit, then subtract a scoped delivery estimate before making an investment decision.

    What usually stops predictive maintenance reaching production?

    Rarely model accuracy. The common blockers are unlabelled failure history, no agreed owner for acting on an alert, and no defined response when the model is uncertain. Settle who acts, within what window, and what happens on a false positive before building.

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