Key takeaways
Short answer: Predictive maintenance uses condition data to forecast when an asset will fail, so you act just in time. Prescriptive maintenance goes a step further: it not only predicts the failure but recommends the specific action — repair now, adjust a parameter, reschedule. Prescriptive requires predictive to already work, plus richer data and decision models, so it is a later-maturity capability. See also condition based vs time based maintenance.
Predictive maintenance forecasts the timing of failure from condition data — vibration trending up, temperature drifting, oil degrading — so you intervene just before failure rather than on a fixed calendar. It answers "when," turning condition monitoring into a timed warning.
Prescriptive maintenance answers "when" and "what to do about it." On top of the failure forecast, it weighs options and consequences and recommends a specific action — replace now, derate the machine, reschedule the order. It needs decision models and far richer data than a simple forecast.
Predictive maintenance flags that a bearing will likely fail in about ten days. A good outcome — but the planner still has to decide what to do: replace at the weekend, slow the line to extend its life, or reschedule the critical order running next week. Prescriptive maintenance makes that call, recommending "reschedule order 482 to line 2 and replace the bearing Friday," because it knows the order book, the spare availability and the cost of each option. Predictive gave the warning; prescriptive gave the plan.
Predicting a failure is one model; recommending the best action requires understanding costs, alternatives and constraints across the whole operation. It demands data maturity and organisational trust most plants are still building — which is why it sits beyond predictive on the maturity curve.
Get condition-based and predictive working and trusted first. Prescriptive on a shaky predictive foundation just automates bad advice — confident recommendations built on unreliable forecasts. Walk before you run: earn trust in the predictions before letting a model prescribe the action.
1. Chasing prescriptive before predictive works. You automate recommendations on top of unreliable forecasts.
2. No trusted condition data. Both capabilities collapse without a reliable signal.
3. Recommendations nobody can act on. A prescription that ignores real constraints gets ignored.
4. Treating it as all-or-nothing. Most value comes from solid predictive long before full prescriptive.
Both protect Availability by avoiding unplanned breakdowns. OEE and downtime data are also the feedstock that trains and validates predictive and prescriptive models — the better your loss data, the better the forecast and the recommendation built on it.
Fabrico captures the condition and downtime data that predictive maintenance is built on, giving you the trusted foundation any prescriptive capability requires. Book a demo to see the data behind prediction.
It adds action recommendations on top of prediction — a distinct, more demanding capability.
Yes — prescriptive builds on a working, trusted predictive foundation.
Richer data and decision models that understand costs, options and constraints.
Condition-based, then predictive, then prescriptive — in that order.
It would automate recommendations on top of forecasts you do not yet trust.
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