Transparency & control for booster production line with
PIA Industrial App Suite
PIA Fact Check
- Increase in system transparency
- Analysis of error sources
- PIA apps used: piaVisibility, piaOEETracker, piaOptimum, and piaAnalyze
Transparency and control: PIA apps make them possible
An automotive supplier relied on PIA's expertise for its assembly system for boosters. The PIA Industrial App Suite (”IAS” ) was used with the modules: piaVisibility, piaOEETracker, piaOptimum, and piaAnalyze. The large and extensive plant with many manual and complex automated processes needed to be manageable and visible. Although there was already a connection to an MES system, this did not provide the necessary level of data detail to be able to analyze production sufficiently. With the applications from PIA IAS, the customer received a powerful toolset that provides a transparent overview of daily production and allows extensive analyses that would not have been possible before.
Precise analysis of error sources with the PIA IAS
During the final inspection of the components, loose components were found in the housing. The customer approached PIA with this problem and asked for support. The analysis by PIA IAS was helpful, as the existing MES system could not provide any useful information for solving the problem. With the help of IAS, the processing times in all screwdriving stations could then be traced. By analyzing the individual cycles, a message was identified that indicated that no screw had been detected in the feed and another screw was fed in after the message was acknowledged by the operating personnel without checking. The screw fed in first had slipped through the feeder and fallen into the housing. Thanks to the message history and the precise assignment of component processing in PIA IAS, further faulty components could be identified based on the known error pattern. The list of potentially affected components could be passed on to the end customer and additional monitoring of the screw feed was implemented to ensure that the problem was permanently rectified. In addition, the operating personnel were made aware of this problem in the manual screwdriving station. This data analysis and collection would not have been possible without PIA IAS.
