“With advanced analytics and machine learning, automakers can mitigate the need to subject every unit to end of line testing while reducing warranty claims, lowering costs, and improving throughput.”
End of Line (EoL) testing is a crucial part of the automotive manufacturing process, acting as a final check on complex assemblies to minimize the risks of shipping defective units to customers. However, despite the obvious benefits, there are significant limitations to end of line testing, and the need to subject every finished unit to end of line tests reduces throughput and diverts resources from more valuable activities.
This white paper lays out the case for and against comprehensive end of line testing, and offers an alternative quality methodology. By integrating advanced analytics and machine learning into production lines, automakers can mitigate the need to test every unit at the end of the line, reducing warranty claims, lowering costs, and improving throughput.
Readers of this white paper will learn:
- Pros and Cons of comprehensive end of line testing
- An alternative quality methodology utilizing machine learning
- How to evaluate machine learning models using ROC Curves