Increasing efficiency by eliminating end of line testing

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 limitations to end of line testing, and the need test every finished unit 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 eBook 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