Improving OEE: measure honestly, then optimize

1 · Measure without flattery

Manual downtime logs systematically overstate OEE — short stops vanish, speed losses hide. Automated capture is the entry ticket: dedicated performance-tracking systems (TeepTrak's hardware, running in 450+ plants, is a purpose-built example; MES and machine-data platforms also serve) give you the honest baseline. Expect the honest number to be lower than the reported one — that gap is the opportunity.

2 · Read the loss tree

OEE = availability × performance × quality, and the fix differs per factor. Availability losses → maintenance and changeover work (see the downtime playbook). Performance losses (micro-stops, speed) → process ML and golden-run guidance. Quality losses → root-cause ML on scrap (see the scrap guide). Plants that skip this step buy the wrong tool for their actual constraint.

3 · Convert points to euros before you buy anything

The OEE ROI calculator turns your target into annual margin — the number that survives a budget meeting. One point on a constrained line is pure margin; on an unconstrained line it's cost-per-unit, which is real but sells differently.

4 · Then let ML move the ceiling

With honest measurement and a loss tree, ML platforms have something to optimize: golden-run replication stabilizes shifts, root-cause discovery removes recurring losses, predictive maintenance protects availability. The measurement layer feeds them all — which is why it comes first.