OEE and predictive maintenance stall on data, not algorithms

Plant teams rarely lack data. Machines report states, the MES logs events, the CMMS holds years of work orders. Yet OEE reviews still argue about whose numbers are right, and predictive-maintenance pilots produce alerts nobody schedules.

One model from line to ledger

In the executive cockpits we built for an automotive lubricants joint venture and an automotive trading and supply-chain house, more than forty persona views — from the board to plant and depot — run on one reconciled enterprise model¹. The point is not the number of screens; it is that a plant manager and a CFO look at the same figures.

• Break OEE into availability, performance and quality losses by line and shift
• Join downtime to production plans and cost, so lost hours have a value
• Feed failure predictions into the CMMS work list, not a separate dashboard
• Plan spares for critical assets from the same signals

Where to start

Choose one line or asset class with a clear downtime cost, agree the OEE loss definitions, and connect MES, CMMS and the production plan before training any model.