eBook: End-of-line testing is good... but predictive quality analytics is better
This eBook walks you through:
- The reasons why end-of-line testing is so widely used in automotive manufacturing
- The limitations of end-of-line testing
- The hidden costs of end-of-line testing
- Current alternatives to end-of-line testing
- How a predictive quality solution driven by machine learning and AI will reduce the need for end-of-line testing
End-of-line (EOL) testing is commonplace in automotive manufacturing as an effective method of ensuring quality conformance. However, EOL testing presents some significant limitations including cost, increased cycle time, and a lack of ability to prevent quality issues and defects from occurring in the first place.
This eBook offers an alternative quality control methodology for automotive manufacturers and suppliers to enable predictive and proactive quality improvements in production. By performing advanced analytics on production data in real time, automotive manufacturers can avoid subjecting every unit to end-of- line testing and simultaneously reduce warranty claims, lower costs, improve first time through (FTT) and overall throughput.