AI predictive maintenance
A manufacturing business where this lands in Operations.
02 — What is actually going wrong
Machines are maintained on a calendar and fail on their own schedule. You are simultaneously servicing equipment that did not need it and getting caught by the one that did. An unplanned stoppage mid-run costs the repair, the idle labour, the late delivery and the customer's confidence.
How it works
Sensors on the critical machines stream vibration, temperature and current draw. A model learns each machine's normal signature and flags the drift that precedes failure, with enough lead time to schedule the intervention into a planned window instead of a crisis.
What good looks like
Unplanned downtime hours fall while total maintenance spend stays flat or drops, because work moves from calendar-driven to condition-driven. Maintenance stops being an emergency function.
03 — The problem, in money
- Monitored machine_count12 machine_count251 machine_countPlaceholder — not a validated benchmarkStarting point
- Unplanned downtime hours per machine per year40 hours250 hoursPlaceholder — not a validated benchmarkStarting point
- Cost per downtime hour1,200 USD10,050 USDPlaceholder — not a validated benchmarkStarting point
- Downtime downtime_reduction0.3 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
04 — The options
05 — The call
Build it. A custom system is the right move here — the payback justifies owning it rather than renting a compromise.
06 — How this goes wrong
- Instrumenting everything. Sensor the three machines whose failure actually stops the line; the rest is capex with no story.
- Needing failure history you do not have. Anomaly detection needs months of baseline, and remaining-useful-life models need examples of actual failures — expect a long, boring data collection phase before any value.
- Alerts nobody acts on. If maintenance has no slack to respond to a warning, prediction converts to noise within a fortnight.
- Counting avoided downtime you cannot demonstrate. The savings are real but the counterfactual is unprovable, which is why this scenario carries the longest ramp in the library.
07 — How you would actually do it
- 01Rank machines by cost-per-hour of stoppage, not by age or repair frequency.
- 02Instrument the top three and collect baseline data for at least a quarter before modelling.
- 03Start with threshold and anomaly alerting; earn the right to remaining-useful-life prediction later.
- 04Agree the response protocol with maintenance before the first alert fires.
- 05Track unplanned downtime hours as the single headline metric.
- 06Re-baseline after any major overhaul — the machine's signature changes.
08 — Tools worth looking at
Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.
09 — Run it on your numbers
Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.
10 — The evidence · what others report
Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.
11 — Questions owners ask
Is this your business?
Get your free AI Opportunity Score — the same first pass I run at the start of every Audit.
Get Your Free Score