Tools
Predictive maintenance ROI calculator
Punch in a few numbers from your plant. See the annual cost of unplanned downtime, a realistic annual saving and payback of a predictive maintenance rollout. No login, nothing leaves your browser.
20%
Typically 15–30% on a well-scoped case. Do not assume 50%+.
Advanced assumptions
Current annual downtime loss
PLN 2,400,000
Annual saving
PLN 450,000
Payback
3 mo.
Net after year 1
PLN 330,000
Net after 3 years
PLN 1,230,000
0 – 36 mo.
Insight
AI/CBM does not eliminate downtime, it shifts it from unplanned (expensive) to planned (cheap). Model on realistic reduction, not marketing 60%.This is an educational estimate. Real outcome depends on sensor-data quality and machine selection.
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Methodology
- Annual loss today = critical machines × downtime cost per hour × unplanned hours per machine per year.
- Annual saving = loss × realistic reduction − annual run cost.
- Payback (months) = implementation cost ÷ (annual saving / 12).
- Year 1 / 3-year net subtract implementation cost from cumulative savings.
Defaults reflect typical ranges for mid-size Polish plants. Every assumption is editable.
FAQ
- How to estimate downtime cost per hour?
- Lost product margin + non-productive labour + penalties + restart cost.
- Why cap the reduction at 40%?
- Realistic reduction is 15–30%. Higher numbers rarely survive production.
- Predictive vs preventive vs reactive?
- Reactive = after failure. Preventive = on schedule. Predictive = on sensor data and risk model.
- What matters more than the model?
- Data quality, machine selection and the maintenance decision process.