OEE

OEE (Overall Equipment Effectiveness) explained simply

Imagine your machine had a video game power-up – but only if it is running, fast, and producing without errors. OEE is this power-up meter: a percentage value that shows how well your equipment is running compared to its ideal state.

If it is always available, running at full speed and only producing good parts, the OEE approaches 100% – which in practice is extremely rare. Typical values of 60% to 80% are common, and quickly show where the potential for improvement lies. This is your entry ticket to making data visible and achieving real efficiency gains – especially in the IIoT environment, where sensors and real-time data come into play.

Background information

OEE (Overall Equipment Effectiveness) is a key figure for measuring the productivity of machines, equipment and production processes. It is calculated as the product of the three main factors: availability, performance and quality rate.

  • Availability measures what proportion of planned production time actually runs – without failures, setup times or unplanned stops.
  • Performance evaluates the actual production speed in relation to the theoretical ideal cycle time – including effects from short stops or reduced cycle speeds.
  • Quality records the proportion of defect-free parts in relation to total production – scrap or rework are counted as losses.

Multiplying these factors results in a percentage value (e.g. 0.866 × 0.93 × 0.913 ≈ 73.6%). This makes it clear: even small deviations – for example in quality – drive the OEE value significantly down. In practice, OEE is often referred to as “world class” at 85%, but this may vary depending on the industry – measurement definitions and context are decisive.

In the IIoT context, OEE plays a central role in data utilisation: connected sensors, MES systems or IIoT platforms such as AWS IoT or HiveMQ enable real-time recording of availability, performance and quality. This allows OEE values to be calculated almost live and targeted interventions to be made when overruns or quality problems occur. Strategically, OEE supports initiatives such as TPM (Total Productive Maintenance) and lean manufacturing by ensuring that employees, shopfloor teams and management share a common key figure – as a basis for identifying sources of loss and continuous improvement.

The six big loss categories (“Six Big Losses”)

The Six Big Losses were originally formulated as part of Total Productive Maintenance (TPM) and today form an important basis for analysing OEE losses. They can be assigned to three areas:

Availability losses:

  • Equipment failures (e.g. technical malfunctions, defects): these lead to longer unplanned downtimes and directly affect availability.
  • Setup and adjustment losses (e.g. format change, tool change): time-consuming adjustments reduce the available machine time.

Performance losses:

  • Idling and short stops (e.g. material jams, sensor errors): these often underestimated disruptions interrupt the cycle flow.
  • Reduced speed (e.g. due to inferior material or cautious operation): here the machine runs below its maximum speed.

Quality losses:

  • Startup losses (e.g. scrap after start-up or setup): production start often generates parts that do not yet meet the requirements.
  • Process-related scrap (e.g. form errors, rework): these are defects during regular operation.

By consistently recording these types of losses, it is possible to address the causes in a targeted manner – whether through training, process changes or technical modernisation.

Limits and misunderstandings of OEE

Despite its usefulness, OEE is not a cure-all. A common misconception is to use it as the sole key figure for evaluating efficiency. OEE is a relative measure – it relates current performance to an assumed ideal state. However, this ideal state may be unrealistic, for example if the maximum machine speed is only achievable under ideal laboratory conditions. This creates a distorted impression of actual performance.

Another misunderstanding concerns comparability: OEE values are only meaningful if definition and measurement methodology are consistent. Different interpretations – for example what counts as “planned downtime” or “NIO part” – quickly lead to apples-to-oranges comparisons between sites or machines.

In addition, OEE does not take economic benefit into account: a line with 85% OEE may be less profitable than one with 60% if, for example, material costs, batch sizes, energy consumption or throughput time are not considered. Therefore, OEE should always be viewed in the context of other KPIs such as TEEP, ROI or CO₂ footprint.

Further information and links

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