Overall Equipment Effectiveness (OEE) is a measure of productivity measurement of equipment. The KPI, and in particular the necessary accompanying information, are not easy to capture, but they have an invaluable asset to asset productivity management. With oee.ai the hurdle to analyze and optimize the OEE is minimal, so that an improvement process based on numbers, data and facts is much easier to install than in the past.
The OEE is a key figure that consists of the three factors availability, performance and quality. By multiplying the factors, the OEE is calculated. The OEE is thematically assigned to Total Productive Maintenance (TPM) and Lean Production. Both concepts have in common that they want to identify and eliminate or reduce waste. In the course of the analysis, a process is subdivided into added value and waste, and the respective shares are made measurable. On the basis, the process can then be optimized.
The OEE Availability Factor and its Optimization
Losses in availability are stoppages above a limit, which must be determined depending on the case. In oee.ai you can set a limit > 1 minute arbitrarily. All shutdowns above this limit reduce availability.
Typical availability losses are major defects, set-up procedures, missing material or personnel, maintenance – everything that leads to a longer downtime of the system. Each time such a stoppage occurs, the prompt for a sturgeon appears on the tablet. These availability issues are often the starting point for OEE optimizations because the causes are usually very transparent and are therefore considered low-hanging fruits.
For the loss reason “set-up”, SMED (Single Minute Exchange of Dies) has even developed its own method within lean production in order to reduce these losses.
The OEE Performance Factor and its Optimization
The performance factor reduces short-stops or even micro-stops and a driving speed of the system, which is less than the default speed. These causes of loss are often difficult to grasp, because the plant, although apparently running, but more often is short or runs too slow. Both things that are not noticeable in a brief presence at a plant regularly.
Within oee.ai, these causes of loss are assigned to a separate fault catalog. A prompt on the tablet occurs when an OEE limit is not reached for a defined amount of time. For example, you can configure oee.ai to query an issue if the system is operating for longer than 15 minutes with an OEE < 60%. This procedure also makes it possible to assess the losses that otherwise “trickle through” one’s fingers.
The OEE Quality Factor and its Optimization
The quality factor indicates the ratio of good parts to reject parts. For this purpose, a separate sensor is attached to a reject route, which counts the bad parts there. Again, the logic is identical again. With an independent Störgrundkatalog the rejects are assigned to the tablet on the tablet.
The Quality Score is a lower priority in OEE Optimization, as its own departments are concerned with quality. Nevertheless, oee.ai is prepared for this application.
Configuration of oee.ai
All oee.ai configurations are made through a user interface in an Internet browser. There is no need to install software on the corresponding computer.
oee.ai is designed in such a way that the sensors can be moved at any time and the data does not get confused. Sensors can therefore be disconnected from one location and logged on to another location again. If you return to a location, the previously collected data is available again as comparison values. In this way, a plausible before-and-after comparison within the oee.ai reporting system is possible at any time.
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