Overall equipment effectiveness

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The term overall equipment effectiveness ( GAE ) or Overall Equipment Effectiveness ( OEE ) or Overall Asset Effectiveness ( OAE ) describes a key figure created by the Japan Institute of Plant Maintenance . It is one of the results of the decades of development of the TPM concept (TPM: Total Productive Maintenance ). The overall system effectiveness is a measure of the added value of a system. With it, both the productivity of a plant and its losses can be displayed at a glance.

The OEE of a system is defined as the product of the following three factors:

  • Availability factor,
  • Power factor,
  • Quality factor.

Their value range is between 0 and 1 or between 0% and 100%.

The definition of the key figure cannot be found in any standard. It is tailored very individually to the company using it. As a rule, this is a long process, as the company first has to develop an understanding of the way of thinking in the categories of added value and waste. Furthermore, depending on the systems or products, the necessary basic data is recorded to determine the key figure and the like. U. as difficult. Many companies therefore rely on special software for data collection, evaluation and analysis. The overall system effectiveness together with the overall service efficiency (OSE or Overall Service Effectiveness) results in the Overall Administration Effectiveness (OAE) of a company.

Overview of GAE

Representation of the OEE calculation

The GAE is a key figure for unplanned losses of a system. Therefore, in the first step, the planned downtimes are deducted from the calendar time (24 hours, 7 days a week). Planned downtimes can be, for example:

  • No occupancy / occupation
  • Planned maintenance
  • Break
  • strike
  • No product manufacturing

The remaining operating time is the basis for the OEE and is thus defined as 100%.

The performance, availability and quality losses are now deducted from this 100%, so that the OEE of the system results.

Availability factor

The availability factor is the relationship between the time until an error occurs and the time the function fails.

It is defined as follows:

Availability factor = average value of time between failures / (average value of time between failures + average value of recovery time)


Availability factor = MTBF (mean time between failures) / (MTBF (mean time between failures) + MTTR (mean time to repair))

With the standard change DIN EN 13306: 2017, the availability can be clearly described. The MOTBF (mean operating time between failures) indicator has now been included in the standard for the duration of the unit. The calculation of the OEE has become clearer as a result.

The availability factor is reduced by the recovery time, such as the following events:

  • Waiting for maintenance
  • Power failure
  • System fault (malfunction)

A convention must be made in the company as to when an unplanned downtime occurs. Detecting a system failure every second and giving reasons for it means too much effort for most companies. In operational practice, a detection limit of 1 minute of system failure has proven to be a pragmatic approach. All downtimes over one minute are included in the performance factor.

Whether setup reduces the OEE is a question of the company-specific definition. If setup activities reduce the OEE, there is a motivation to reduce setup times through SMED (Single Minute Exchange of Die). On the other hand, this also means that an OEE increase can be achieved through fewer modifications, i.e. through larger batch sizes . This goes against the principles of lean manufacturing . If set-up activities do not reduce the OEE, there is a risk that faults will be incorrectly declared as a set-up process.

The best way to deal with setup times is to work with setup time default values. The planned set-up time does not reduce the OEE, however, exceeding set-up times reduces it. For this, however, set-up time default values, differentiated if necessary for different set-up variants, must be available. The effort for this is u. U. however very large.

Of the three OEE factors, the availability factor is often the easiest to grasp. Therefore, GAE initiatives in companies often start with the recording of the availability factor.

Power factor

The performance factor is a measure of losses due to deviations from the planned piece time , minor failures (i.e. the downtimes that do not affect availability) and idling.

Performance factor = actual performance / target performance (for example in pieces / hour )

It should be noted that the power factor may only be calculated in relation to the running time and not to the operating time.

While the actual performance can be measured, it is often difficult in operational practice to arrive at a target performance as a default value. If no information from the machine manufacturer is available or if this is not realistic, the concept of the “best-demonstrated piece time” has proven itself. The production speeds of the products from the past are compared with each other and the highest production speed is defined as the target output in the sense of 100% performance factor. However, the power factor obtained in this way is not suitable for production program planning. The factor 1 represents a peak value that would regularly not be reached.

For systems that manufacture only one or a few products, calculating the power factor is easy. If a large number of different products are run on one system, the effort required to determine a standard time may be high.

Quality factor

The quality factor is a measure of the loss due to defective parts that need to be reworked. It is defined as follows:

Quality factor = (number of parts produced - number of rework parts - number of rejects) / number of parts produced

A pragmatic approach is particularly useful when recording the quality factor: Inadequate quality is often not discovered in the system that caused it. Here it has proven to be useful to use the “discovery principle”, that is, to burden the system with an OEE reduction at which the error was discovered. In this way, the GAE moves away from a pure system and becomes a process indicator. The OEE of a system can of course also be optimized by optimizing other systems. The GAE should also be a key figure that is as timely as possible. In this respect, the OK quantity should be determined at the end of the lot at the latest and the OEE calculated. Subsequent corrections to the GAE z. B. by later blocking is not advisable.

Overall plant efficiency

The term overall system efficiency is often used synonymously in German with overall system effectiveness , even if this is incorrect (see effectiveness ). Measures that increase production output but cause disproportionately high costs can be effective, but at the same time inefficient and therefore not economically viable.

Overall equipment effectiveness

The OEE is defined as the product of the availability factor, performance factor and quality factor:

GAE = availability factor × performance factor × quality factor

The result is a percentage that indicates the proportion of the planned machine running time that was actually produced in accordance with the quality criteria .

This value is usually well below 100%, since the influencing factors can never fully reach 100%. This highlights the limiting factors that need to be worked on.

Benefit of the GAE

The benefit of the GAE lies in the transparency of the value-added share of the system and allows management to look at the systems from a different perspective. The key figure is ideally suited to e.g. B. to link a target agreement process to an OEE increase, because the key figure can be made robust against structural changes in production due to its scope.

Increase in OEE

For a targeted increase in OEE, the reasons for deviations in OEE factors from 100% must be recorded. So the question is why wasn't

  • 100% of the operating time produced?
  • Did you drive 100% of the planned speed?
  • 100% of the products produced in the defined quality?

A manual or system-supported acquisition (e.g. with the help of software for operating data acquisition or machine data acquisition ) of the deviations from the defined ideal state can generate an analysis of the Pareto distribution of the largest sources of loss for a plant. From this, measures for the targeted improvement of the OEE can be initiated, which can then also be measured. The optimization is started with the source of the greatest OEE loss.

Practical experience

  • An OEE of 85% achieved in practice can be classified as “very good”. It is important to differentiate here which type of plant / production mechanism is involved. An OEE of 90% can also serve as a lower limit in a full-account system, whereas an OEE of 60% can represent an upper limit in a production process that is difficult to manage.
  • Especially in the case of several systems linked without buffering, an OEE of the overall system of z. B. 85% difficult to achieve, since the calculation is done by multiplying the individual OEE (e.g. 3 linked systems with an OEE each of 90% lead to an OEE of the overall system of 0.9 × 0.9 × 0, 9 = 0.73)
  • In the case of several systems linked with buffering, correct OEE recording can only be carried out with several measuring points for IT purposes. In addition to the standard reasons for disturbance, each individual measuring point also needs the disturbance categories "traffic jam" and "demolition" in order to be able to identify upstream and downstream disturbances in production systems.
  • The determination of the OEE causes a lot of effort, especially in the beginning, due to the training of employees on the one hand and the acquisition of unadulterated data on the other.
  • The GAE cannot be used to measure the performance of employees. It is used to determine and measure system losses, which can then be eliminated through appropriate cause analyzes and measures.


The GAE is not to be equated with other key figures that are usually recorded in production or maintenance such as B.

The reason for this is that the setup time cannot be taken into account for an OEE key figure (only the planned occupancy time). With this approach, the necessary optimizations in the setup process are missing.


Independent OEE literature:

  • Koch, Arno: OEE for the production team. The Complete OEE User Guide - Or How To Discover The Hidden Machine. Ansbach: CETPM Publishing, 2008, ISBN 978-3940775047 .
  • May, Constantin; Koch, Arno: Overall Equipment Effectiveness (OEE) - a tool for increasing productivity, published in: Zeitschrift der Unternehmensberatung (ZUB), issue 6/2008, pp. 245–250. for download from the Center of Excellence for TPM (CETPM) (PDF file; 190 kB)
  • Oee for Operators: Overall Equipment Effectiveness, Productivity Development Team, Productivity Press, 1999

In TPM literature:

  • TPM - Efficient Maintenance and Machine Management, Edward Hartmann, Landsberg, Verlag Moderne Industrie, 2000 [p. 77ff.]
  • Introduction to TPM, Seiichi Nakajima, Productivity Press Cambridge Massachusetts, 1988 [p. 27ff.]
  • Total Productive Maintenance, Al-Radhi, Carls-Hanser-Verlag Munich, 1995 [p. 30ff.]
  • Total Productive Maintenance - The Western Way, Peter Willmott, Butterworth-Heinemann Ltd., 1994 [p. 25ff.]

Web links

Individual evidence

  1. Jürgen Kletti, Jochen Schumacher: The perfect production - Manufacturing Excellence through Short Interval Technology (SIT). 2010, Springer, ISBN 978-3-642-13845-4 , pp. 80ff