Modern ERP systems are extremely powerful and can support your company in many areas. In the area of production, the maintenance of equipment and
1. data collection directly in the ERP system
Thanks to the many interfaces in the IoT sector, most production equipment can now be networked very easily. Their data can be merged in the ERP system and evaluated accordingly. This creates practical ERP data that can be used to
2. implement further factors for detecting anomalies
For an ERP system to be able to detect the smallest errors and draw conclusions from them for maintenance work, the system must be able to learn.
The data from the devices alone are not sufficient for this, as they do not allow any derivations. Therefore a variety of data from which the system can learn in the long term. Important ERP data includes, for example, an implemented ticket system that records repairs and breakdowns and thus sets these in relation to the machine data.
3. artificial intelligence and predictive maintenance
For such a system to work effectively, it must have a basic form of machine learning or artificial intelligence. This is the only way the system can not only react to known errors, but also draw the appropriate conclusions based on the experience gained and the new ERP data.
Predictive Maintenance depends on a system of this kind dealing with a wide variety of data and interpreting it in such a way that potential faults and malfunctions are detected at an early stage. Only in this way can the system be able to plan maintenance precisely, minimize downtime and also prevent damage to equipment and production resources.
4. algorithms learn to recognize errors
To do this, the system’s algorithm must be able to recognize errors. This can be done in several ways. Firstly, through the available data from the individual production devices and other systems such as ticketing or maintenance logs.
In addition, programmers can actively contribute to increasing the learning speed, for example by using visible referencing anomalies and damage or maintenance are already implemented in the algorithm. are already implemented in the algorithm. This not only makes the system faster, but also helps to avoid at least these errors in the future.
5. also enter repairs and maintenance in the ERP data
The higher the level of detail in the ERP data, the more the system can benefit from it and the more accurately Predictive Maintenance implemented become. Therefore should a as high data density should be available.
Maintain all relevant data in the ERP system to obtain the best recommendations for predictive maintenance
Data on maintenance and completed repairs can also be entered into the system so that further correlations and causal relationships can be identified. It may initially take some time to transfer this data into the system, but the results are impressive.
6. monitor the correlation of all factors
As good and powerful as an ERP system with artificial intelligence may be, it cannot replace the human factor. This means that interpretation errors can occur in the program, especially at the beginning, as the individual data records are placed in the wrong correlation, for example.
It is therefore particularly important at the beginning to carry out any maintenance to check and evaluate. Was maintenance necessary and are there already signs of damage or wear and tear on the production equipment? The better these factors are checked and re-entered into the system as part of the ERP data, the better and more effectively Predictive Maintenance in your company.
7. regularly test the ERP system and thus continue to train it
The better and more efficiently predictive maintenance works in a company and the less downtime there is, the less attention is paid to ERP data and therefore the basis for success. This is a problem that occurs in many industries.
It is therefore important that companies continue to feed the ERP system with data and check its work regularly. Often only spot checks are sufficient to confirm that the system is working properly and thus have a positive influence on the process. This is important so that errors in the system, for example based on incorrect data records from a machine, can be detected and rectified at an early stage. Here, every company is required to constantly strive for improvement and to continue to actively use the ERP data to improve the Predictive maintenance.
