Case 4 engl.
|Case 4 engl.|
|Task||Improvement of Overall Equipment Effectiveness (OEE) of fully interlinked Industry 4.0 systems|
|Number of employees||Approx. 120|
|Implementation length||6 weeks|
The company is a supplier of automotive parts; a few years before the project start, it purchased two interlinked systems. The individual processing machines, some of which are one-off productions, were linked with automatic test equipment via a robot. This, in turn, was connected with a robot and the conveyor belt, and in some cases, another system was linked to it. It was thought that the system could be set up with the simple push of a button, and that it would have a very high Overall Equipment Effectiveness (OEE). Neither was true; the expectations were far from realistic.
One of the key problems was that there was no general contractor to service the entire line. The company had to monitor and organise every interface itself. In this case, the analysis had to be done differently, because there were extensive downtimes that the manufacturer was unable to resolve. The first step of the project was therefore to record all disruptions and to assign tasks through stringent project management.
The second problem was that the belts could not produce the planned and desired quantity. When analysing the maintenance and buffering process, it was identified that the number of pallets was so high, that they would block all pallets before the source of the disruption and downtime. As a result, none of the systems had any pallets. After the disruption was resolved, the system was literally flooded with pallets and it took up to 30 minutes to resupply all systems. These stops and blockages also greatly contributed towards the fact that the output quantity was not as planned. The solution: One third of all pallets was removed. As a result, the chain had no more peaks and stoppages and could run continuously and produce components at regular intervals.
A fundamental problem in the project was how KPIs, facts and the resulting decisions were dealt with. It was necessary to revisit the methodical sequence: first the facts had to be collected, the KPIs had to be put in place to check progress, and then a bold decision had to be made. Because in addition to the technical challenges, there were also human issues. The more complex and complicated the systems, the more qualifications the people needed in order to immediately take care of a system problem. Only one employee in the project had this skill. Therefore, additional personnel had to be trained and qualified, but this was not possible in a short project period of 1.5 months.
In the final results it was apparent that the systems’ OEE could be increased significantly, and that the targets could be achieved with the implementations planned for after the project end.