Why Root Cause Analysis Takes Too Long on SMT Lines

Most SMT lines today generate large volumes of inspection and test data. AOI systems capture defect locations and images.
SPI measures solder paste volume and alignment. ICT and functional test systems log electrical failures.

Yet when a yield issue appears, root cause analysis still takes hours.

Engineers export CSV files. They align timestamps manually. They compare defect codes across systems.
Often, the majority of investigation time is spent preparing data rather than analyzing it.

The issue is rarely inspection coverage. It is data correlation.

Detection Does Not Equal Correlation

Adding AOI or SPI increases defect visibility. It does not automatically improve investigation speed.

When inspection and test systems are disconnected:

• Defect codes differ between machines
• Unit-level traceability is incomplete
• Data must be manually aligned
• Engineers rely on samples instead of full production history

This fragmentation slows decision-making and delays corrective action. FPY plateaus not because defects are invisible,
but because their dominant drivers are difficult to connect quickly.

Why RCA Workflows Break Down

In many SMT environments, root cause analysis is still ad hoc. Each investigation starts from scratch.
Engineers rebuild pivot tables, recreate pareto charts, and re-clean data repeatedly.

Without standardized correlation workflows, even experienced teams lose time between systems rather than focusing on process behavior.

What Actually Helps

Improving RCA speed does not require more checkpoints. It requires structure:

• Unit-level correlation across AOI, SPI and test systems
• Standardized defect classification across equipment
• Reusable RCA dashboards instead of repeated spreadsheets
• Focus on dominant defect mechanisms, not isolated symptoms

When inspection and test data are connected properly, investigation time drops significantly
and corrective actions become more targeted and repeatable.