Predictive maintenance projects fail more often from a fuzzy business case than from a bad model. Before you build anything, it's worth sizing the opportunity honestly — here's the framework we walk clients through.
Not "downtime is expensive" — an actual number. Multiply average unplanned downtime hours per month by lost production value per hour, then add emergency repair premiums (rush parts, overtime labor) on top. Most manufacturers underestimate this by 30–40% because they only count the repair bill, not the lost output.
Some failures are genuinely random. Most aren't — they're preceded by measurable signals (vibration, temperature, load patterns) that nobody was capturing. A quick sensor audit will tell you which category most of your downtime falls into, and that ratio determines your realistic ROI ceiling.
The predictive model itself is often the cheapest part of the project. The real cost — and the real value — is in the data pipeline: getting sensor data flowing reliably, cleaned, and time-aligned before any model can learn from it.
In our experience, well-scoped predictive maintenance projects for mid-size manufacturers pay back within 10–14 months once the pipeline is live. If your business case requires payback inside 3 months to get approved, the project is being oversold somewhere.
One client cut unplanned downtime 40% and saved 15% on OPEX — but the business case that got the project approved was built before a single sensor was installed, using exactly this framework.
Want us to size this for your plant specifically? That's exactly what our free audit covers.
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