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    ROI Calculator

    CV Quality Inspection ROI Calculator

    Model the financial impact of replacing manual end-of-line inspection with on-edge computer vision.

    AI

    Quick Answer

    This calculator estimates the annual value of computer-vision inspection from your own defect rate, unit value, inspection cost and throughput. The detection and escape-reduction defaults are planning assumptions for scoping, not measured results; replace them with your line's own baseline. The dominant variables are unit value and the true cost of an escape reaching a customer.

    Inputs

    Your Numbers

    Results

    Illustrative Annual Value

    Escapes avoided per year6,400
    Escape-cost savings$256,000
    Labor reallocation value$210,000
    Total annual value$466,000

    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.

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

    Assumptions

    How This Illustrative Estimate Is Calculated

    • • Escape value uses unit value as a proxy. For automotive or medical devices, multiply by 3 to 10x to include warranty, recall, and brand exposure.
    • • Inspector reallocation assumes redeployment to higher-value work (root-cause, sampling, audits), not headcount reduction.
    • • Excludes capex for cameras, edge GPUs, and integration. NeoBram pilots a single board variant or SKU in 4 to 6 weeks before line-wide rollout.

    Direct answers

    Before you rely on this number

    What defect rate should I enter — measured or estimated?

    Use your measured internal reject rate, not the escape rate to customers. Escapes are what you did not catch, so they are systematically under-reported. If you only have customer return data, treat the result as a lower bound on the true opportunity.

    Does computer vision replace human inspectors?

    In most industrial deployments it re-tasks them. Vision handles high-volume repeatable checks consistently; people handle ambiguous cases, root-cause work and process adjustment. Model the saving as inspector time redeployed rather than headcount removed, unless you have already agreed otherwise.

    What makes an escape expensive enough to justify this?

    Unit value is usually less important than what happens downstream. A low-value part in a safety-critical assembly, a recall-triggering defect, or a component that fails after installation carries cost far beyond the unit itself. Enter the fully-loaded consequence, including warranty, rework, logistics and customer credit.

    How much labelled image data is needed?

    Enough examples of each defect class you intend to detect, including the rare ones — which is usually the binding constraint. Where a defect is genuinely rare, the practical route is anomaly detection against normal product plus targeted collection over the first production months, not waiting for a balanced dataset.

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