← Where AI Pays
manufacturing · Plant ops · AutonomousBuild it
AI predictive maintenance
01 — The scenario
A manufacturing business where this lands in Operations.
02 — The problem, in money
$129,600$236,489,063/ yr at stake
Modeled- 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
03 — The options
Build$40K–$90KA custom system, owned outright.
Bin$0Do nothing — and accept the leak.
04 — The call
Build it
Build it. A custom system is the right move here — the payback justifies owning it rather than renting a compromise.
05 — 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.
Worth to your business
$129,600per year
Modeled — industry averagesRevenue$0Modeled
Cost savings$172,800Modeled
Hours / week0Modeled
Industry averages
12 machine_count
Industry avg: 12 machine_countPlaceholder — not a validated benchmark
40 hours
Industry avg: 40 hoursPlaceholder — not a validated benchmark
1,200 USD
Industry avg: 1,200 USDPlaceholder — not a validated benchmark
0.3 ratio
Industry avg: 0.3 ratioPlaceholder — not a validated benchmark
06 — The evidence · what others report
No hard numbers reported.
Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.
07 — Questions owners ask
Every figure is Modeled — computed from industry-average benchmarks against the scenario’s formula. Move the sliders to your own inputs and the numbers recalculate live. Nothing here is a promise; it is a starting estimate you can pressure-test.
The library is organized by department, outcome, effort and readiness so you can find what fits. Add scenarios to your plan, tune the inputs, and see the combined picture on your plan dashboard.
Want this to be a Projected number? That's what the Audit produces — my forecast on your real data, with my name on it.
Build means a custom system is the right move; Buy means an off-the-shelf tool wins; Bin means the math does not justify doing it at all. Binned scenarios can be favorited for reference but never enter a plan.
Is this your business?
Get your free AI Opportunity Score — the same first pass I run at the start of every Audit.
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