Predictive maintenance earns nothing until someone acts on it. The gap between an anomaly score and a dispatched work order — triage, parts, crew, window, sign-off — is where most programs stall. MAIA closes that gap: it fuses the prediction with inventory, shift, and production signals, drafts the full intervention with its cost-of-delay evidence, and routes it to the supervisor who owns the line.
Why predictions die in dashboards
The model flags bearing wear at 92% confidence; the dashboard displays it; three weeks later the line stops anyway. Nothing failed technically — what was missing was the decision layer: who should do what, by when, at what cost of delay, using which parts and which crew, inside which production window.
Fusing maintenance signals with operations signals is what turns a score into a plan: the same event reads differently when the substrate can see that the affected line has a changeover Thursday and the part is two days out.
Evidence for the trade-off
Maintenance decisions are economic decisions. Each drafted intervention carries its comparison — act now versus next window versus run to planned stop — so the supervisor approves a trade-off, not a hunch, and the decision joins the ledger with its reasoning intact.