Increase production yield

Improve First Pass Yield (FPY) | QualityLine
Use Case: First Pass Yield (FPY)

You see your yield dropping.
You can't see what's driving it.

First pass yield doesn't drop randomly. It's driven by variation across machines, materials, processes, and production runs — but that variation is spread across disconnected systems.

Your tools detect defects. They don't explain why they keep happening. QualityLine connects your manufacturing data — so you can see what's actually driving yield loss.

Request a Demo →
Yield Loss Analysis
SMT
AOI
SPI
MES
ICT
ERP
Repair
Reflow
↓
Yield Driver Identified
Placement offset on Line 2 — feeder #7, component C0402
Accounts for 68% of solder defects in last 14 days across 4 production runs
91%
Up to 30% improvement in first pass yield
50% reduction in defect escapes and rework
1,000+ manufacturing lines connected globally
No replacement of existing systems required
The FPY Problem

First pass yield is easy to measure.
Hard to explain.

Most teams follow the same cycle:

You see yield dropping · You find and fix the defect
Yield recovers · Then drops again

Because the real cause was never fully understood. The fix addressed the symptom visible in one system — not the underlying variation spreading across several.

Your tools detect defects.
They don't explain why they keep happening.

A stencil variation affects multiple boards — across shifts and runs
A placement offset appears only on certain production runs
A supplier batch introduces subtle defects that only appear downstream
A changeover creates temporary instability that's never measured
Each system captures part of the story — none of them connect it
So the same issues return — under a different form
Why FPY Improvements Don't Last

Most FPY fixes treat symptoms.
The causes stay hidden.

The Pattern
Fixes are based on isolated data
When an engineer investigates a yield drop, they look at the system that flagged the defect — not at the upstream conditions across multiple systems that created it. The fix is real, but incomplete.
The Pattern
Root causes are assumed, not proven
Without connecting data across machines, materials, and runs, root cause analysis relies on experience and intuition. Sometimes that's right. Often it misses the interaction between variables that actually drives the defect.
The Pattern
Patterns across machines and runs are missed
The same defect type appearing on three different runs over two weeks isn't visible if each run is investigated independently. Cross-run patterns — the clearest signal of a systemic cause — are invisible without connected data.
The Pattern
Teams react to defects instead of controlling variation
Reactive quality control is expensive — in rework, scrap, and investigation time. The shift from reactive to proactive only happens when you can see the variation building before it produces the defect.

Ready to see what's actually
driving your yield loss?

See how QualityLine connects your manufacturing data to identify yield drivers — in a live session using real production data.

Request a Demo →
How QualityLine Helps

See what's actually
driving yield loss

QualityLine connects data across SMT machines, AOI, SPI, ICT, testing systems, repair and rework, MES, and ERP — in a single analytical view structured by production run, machine, and process condition.

No replacement. No disruption. No changes to your existing setup.

Instead of analyzing defects one by one, your team can see:

Which conditions consistently lead to yield drops across runs
Which defects repeat — and what they have in common
How process variation across machines impacts FPY
Where instability enters the line — and when
30%
FPY improvement across connected manufacturing lines
50%
Reduction in defect escapes and rework cost
Zero
Systems replaced — connects to what you have
Days
To first actionable yield insights
Core Capabilities

How QualityLine supports
first pass yield improvement

Three capabilities that work together to move your team from reactive defect-fixing to proactive yield control.

01
Cross-system correlation

Connects data across SMT, test, and repair systems so your team can see how conditions across the full line interact to produce yield loss — not just where the defect was detected.

See SMT Analytics →
02
Root cause visibility

Identifies patterns behind recurring defects across machines, materials, and production runs — distinguishing real causes from coincidence so fixes actually hold.

Find Root Cause Faster →
03
Process monitoring

Tracks variation and process stability across production runs in real time — alerting before a drifting parameter produces a yield event rather than after it already has.

Monitor Process Capability →
Customer Results

The same data.
A completely different level of control.

"

QualityLine gave us visibility we didn't know was possible. We could finally correlate what was happening across our production runs — and the root causes that came out of that changed how we approached yield control entirely.

Sigalit Fountain Director, Engineering QA & EHS — Emerson
30%
FPY improvement across connected manufacturing operations
50%
Reduction in defect escapes — catching yield issues before they become rework or returns
1,000+
Manufacturing sites running QualityLine globally, including Siemens, Emerson, Jabil, Flex, Molex
Common Questions

How do manufacturers improve
first pass yield?

Improving first pass yield requires understanding how variation across machines, materials, and processes impacts defects. Without connecting data across systems, teams fix symptoms instead of controlling the causes — and yield drops recur.
Why does FPY drop even after fixing defects?
Because the underlying cause was not fully identified across systems. When fixes are based on isolated data from a single machine or inspection system, the root cause — which often spans multiple stations, material lots, or process parameters — remains unaddressed. The defect returns in a different form.
Can QualityLine identify recurring yield issues?
Yes. QualityLine detects patterns across production runs and conditions — connecting data from SMT, AOI, ICT, repair, and process systems to identify which variables consistently correlate with yield drops across runs and machines.
Does it replace existing systems?
No. QualityLine works with your existing SMT, test, and MES systems — adding a connected analytical layer without replacing or disrupting any current infrastructure. Data collection is non-invasive and does not affect throughput.
How quickly can we improve FPY?
Most teams begin identifying key drivers of yield loss within days to weeks of deployment. Measurable FPY improvements typically follow within 30–90 days, depending on production volume and defect frequency.
What types of yield loss can QualityLine identify?
QualityLine identifies yield drivers related to machine parameters, material lot variation, process drift, changeover instability, operator or shift patterns, and cross-station interactions — any variation that correlates with defect rates across your connected systems.
How is this different from what our AOI or ICT system already does?
AOI and ICT tell you where defects occur and what type they are. QualityLine tells you why they occur — by correlating those inspection results with upstream process data, material history, and machine parameters across the full production flow.

Want to see what's driving yield loss on your specific line?

We'll walk through your production setup and show you exactly what QualityLine would surface.

Request a Demo →
See What Your Production Data Is Missing

Your yield isn't random.
You're just not seeing what's driving it.

QualityLine helps you connect your manufacturing data — so you can improve first pass yield with clarity and control.

Request a Demo →

Quality Line
QualityLine Production Technologies Ltd

 

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