ebook

Reducing End-of-Line Testing with Predictive Quality

Topics

01

The downsides of end-of-line testing

02

The hidden costs of end-of-line testing

03

Reducing the dependency on end-of-line testing

04

A better solution

05

Multi-variate anomaly detection

06

The impact of a scalable machine learning platform

Abstract

End‑of‑line (EOL) testing is commonplace in automotive manufacturing as an effective method of ensuring quality conformance. However, EOL testing presents significant limitations, including cost, increased cycle time, and an inability to prevent quality issues and defects from occurring in the first place.

This eBook presents an alternative quality‑control methodology that enables automotive manufacturers and suppliers to implement predictive, proactive quality improvements in production. By applying advanced analytics to production data in real time, manufacturers can avoid subjecting every unit to end‑of‑line testing, improve first‑time‑through (FTT) rates and operational efficiency, lower production costs, reduce downstream quality spills, and prevent warranty claims.

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