Almost every plant we visit reports an OEE. Very few can explain how the losses were classified, who decided a stop was planned, or why short stops never appear in the data at all.
Request Free Diagnostic →A plant reports 78% OEE. Two hours on the floor with a stopwatch suggests something closer to 55%. Nobody falsified anything. Micro-stops under five minutes were never recorded, changeovers were classified as planned downtime, and speed loss was invisible because the rated speed in the system was set years ago to a number the machine has never achieved.
This matters beyond reporting accuracy. If your OEE is wrong, every capital decision built on it is wrong too. Plants buy a fourth machine when the three they own are running at half their real capability, because the number said capacity was exhausted.
So we do not start by improving OEE. We start by making it true. That is uncomfortable, because the honest number is almost always worse than the reported one, and somebody has been presenting the reported one to headquarters.
A loss classification your team applies consistently: planned versus unplanned, setup, micro-stops, speed loss, quality loss. Ambiguity in the taxonomy is what makes OEE meaningless.
We verify the design speed each asset is measured against. Stale or optimistic rated speeds silently corrupt every availability and performance calculation downstream.
Operator-level cleaning, inspection and lubrication routines, with clear boundaries on what operators own versus maintenance. Reduces the failure classes that cause most unplanned stops.
Moving from run-to-failure to a planned regime sized against criticality — and against realistic spare-parts lead times, which in Brazil are a genuine constraint on strategy.
Structured analysis on the few losses that dominate the Pareto, with countermeasures assigned to named owners and verified against baseline.
Scrap and rework are OEE losses that most plants track in a separate system, so nobody sees the total. We consolidate them into one picture.
We measure a sample of your lines ourselves and compare it against what your system reports. The gap between the two is usually the most valuable output of the whole diagnostic. No cost.
Taxonomy, rated speeds, collection routine, and who records what. Until this is stable, improvement work has no reliable baseline to be measured against.
Almost always a small number of failure modes drive most of the loss. We work those with the maintenance and production teams rather than spreading effort thin.
Operators take on defined daily routines. This is a cultural change more than a technical one, and it needs the supervisor layer aligned first.
Same measurement method, same definitions. A gain that only exists because the definition changed is not a gain, and we will say so.
Manual collection with an unambiguous taxonomy and a disciplined routine. It is less convenient than automated capture and, done properly, no less accurate. It also forces the definitional clarity that automated systems let you skip.
It depends entirely on your process type, product mix and asset age, and any consultant quoting a universal target number is selling something. What we commit to is an honest baseline and a Pareto of where the loss actually sits.
That is often exactly when it is worth it. A high reported OEE alongside missed delivery dates or unexplained overtime usually indicates a measurement problem rather than a performance one.
It can, if introduced as a transfer of work. We introduce it as a boundary definition — operators take defined routine tasks so maintenance can move from reactive firefighting to planned work. That framing matters.
We can advise on requirements once your process and taxonomy are stable. We do not resell software and we have no vendor relationships, so the requirement list comes from your operation.
Tell us about your operation in Brazil. We reply within one business day — no cost, no commitment.