OEE, in brief, is an industrial parameter indicates the efficiency of your available time. It shows how much of this available time is used without
The simplest method for OEE tracking is to ask the operator fill a simple form that you will provide for downtimes/performance/quality. The data collected by this method is not sufficient for today's fast moving processes.
For this purpose, these data can be easily collected with different digital methods. These are;
1. HMI: It can be thought of as industry standard panels with interfaces / software that enable humans and machines / software to interact, or other solutions such as barcodes that will enable this interaction. The approach here can be thought of as entering the information into the panel manually by the operators.
If you have just started using OEE, it will take you to a different dimension.
Although you cannot get instant data, you can clearly see the OEE account at the end of the batches.
Even in Excel, an interface (not forgetting that operators will enter data with fingers, so use large buttons and visuals) can be prepared and used.
If desired, a software can be easily installed.
It can work integrated with the barcode you use for product tracking.
You can receive data via USB or Ethernet.
You can analyze the data you collect with a Business Intelligence software (BI).
Topladığınız verileri bir İş Zekası yazılımı (BI) ile analiz edebilirsiniz.
This application is a semi-manual application and the accuracy of the data would not be 100%.
A less expensive panel / tablet can be chosen instead of an industry standard panel just to collect data (considering the situations it will face in the shopfloor. heat, vibration etc)
2. Machine data acquisition: Many machines / systems today have controllers that allow you to receive data collected from the sensors installed by the machine manufacturer. (PLC, smart relay, analyzer etc.)
You can monitor the data instantly
In addition to OEE data, you can get all the data (temperature, door open-closed, barcode, HMI etc.) collected by the PLC.
You can save the collected data in a database.
You can analyze the data in the database with a business intelligence software (BI).
In this way, you can ensure that some downtime and lost performance categories are automatically recorded without manual input from the operator.
You can control the processes with Statistical Process Control.
It is possible to receive alarms and warnings in special situations.
Using the data in the database, you can predict problems that may affect OEE from the data with a machine learning software. (predictive maintenance, predictive quality etc.)
Getting the data requires good automation knowledge and research.
The data you receive is limited to the machine's sensors. This could mean that other data important for machine learning cannot be expressed mathematically.
3. Non-machine (Process) data acquisition: Not all the factors affecting OEE can be provided to you by the machines. For example, the performance loss of a bearing cannot be predicted solely from the machine data. In such cases, all data sources (factors) needed to create the correct mathematical model are defined by analyzing the process and integrated into the system. This means the digitalization and integration of the data created in the previous and following processes.
By establishing a complete mathematical model, your predictive estimates for the factors affecting OEE will be more accurate.
You can see the sources of the problems with the process view.
Once machine learning reaches at a sufficient level, you can reduce operator dependency with approaches like Advanced Process Control (APC).
As our knowledge of the process increases, new parameters will need to be added to the model in the future.
Please contact for more information.