Live LinkedIn Webinar · 25 min + Q&A
Why AOI, SPI and Test Data Fail to Improve SMT First Pass Yield (FPY)
In short: AOI, SPI and test data fail to improve SMT first pass yield when data remains siloed.
Without correlation across SMT processes, teams see defects — not root causes.
Practical lessons from real SMT production environments — and what teams do differently when they want FPY to move.
What you’ll take away
- Why AOI, SPI and test data fail to drive FPY improvement
- Where data disconnects occur across SMT, inspection and test
- How teams connect data for faster root cause analysis
- A practical checklist you can apply immediately
FPY is the percentage of assemblies that pass all required steps the first time without rework or repair.
Related:
AI Field Performance Analytics ·
AI Root Cause Analysis
- 25-minute technical walkthrough
- Live Q&A
- Recording shared after the session
Best for: Quality, Process, SMT & Test teams, Manufacturing Engineering, Operations leadership.
FAQ
Why doesn’t AOI data alone improve First Pass Yield?
AOI identifies defects after they occur but does not explain why they happen across machines, time, and process settings.
Can SPI data predict FPY issues?
SPI highlights printing risks, but yield depends on downstream placement, reflow, and test performance.
What data is required to improve SMT FPY?
Unified AOI, SPI, test, and process data analyzed together to identify true root causes.
Join live on LinkedIn and bring your questions — we’ll leave time for practical Q&A.
