Using OEE losses to choose maintenance work

• OEE = Availability x Performance x Quality; maintenance most directly drives Availability.
• The number is only as good as the downtime coding underneath it.
• Chase the losses, not the score: ask which assets and causes pull OEE down.
• Combine OEE loss with criticality and cost to prioritize reliability work.

OEE multiplies three factors, each expressed as a percentage:

• Availability = run time / planned production time. This is the maintenance lever: breakdowns, slow repairs, and changeovers erode it.
• Performance = actual output / theoretical maximum output during run time. Speed losses and minor stops live here.
• Quality = good units / total units produced. Scrap and rework erode it.

Worked example (illustrative): a line available 90% of the time, running at 95% of rate, producing 99% good units has an OEE of 0.90 x 0.95 x 0.99, roughly 85%. Because the factors multiply, a weak factor drags the whole score down, which is why availability problems are so costly. To run the numbers for one of your own machines, try the free OEE calculator.

Maintenance most directly affects availability, and availability is only as trustworthy as your downtime data. Capture downtime by line, asset, cause, shift, and linked work order. Weak or generic downtime codes produce a believable-looking OEE number that no one can act on.

Distinguish planned downtime (scheduled maintenance, changeovers) from unplanned downtime (breakdowns). The split tells you how much of your availability loss maintenance can attack directly.

The useful question is not what the OEE number is this week. It is which assets and which failure causes are pulling it down, and what work would move it. An OEE dashboard that cannot drill into the contributing assets is a scoreboard, not a tool.

Tie each availability loss back to a work order and a cause code so the conversation shifts from the score to the specific bad actors behind it. In MaintenanceIQ, Availability is derived from the breakdowns you log, and Performance and Quality come from shift logs picked up from your machine sensors, or entered by the supervisor where a machine has no sensor, so the loss breakdown and the worst-OEE asset ranking draw on the same records as your maintenance work.

OEE loss is most powerful when combined with asset criticality and cost. A machine with modest OEE loss but high criticality and expensive failures may deserve attention before a higher-loss but trivial asset.

Use the three together, OEE loss, criticality, and maintenance cost, to decide where planners and technicians spend their limited attention. That keeps reliability work aimed at the losses that actually matter to the plant.

Comparing OEE across very different lines as if the numbers were equivalent; OEE is best used as a trend within one asset or line.

Treating OEE as a worker-productivity score rather than an equipment-effectiveness signal, which erodes the honest data collection the metric depends on.

Optimizing the score instead of the losses, for example by loosening the theoretical rate so performance looks better.