Electronics & EMS
One defect model across every SMT line
AOI, SPI and test each hold part of the truth about a board. When those datasets stay separate, the same defect is investigated repeatedly on different lines. The platform unifies them into one board-level model, so a shared root cause is visible the first time it appears.
- rework hours
- -34%
- SMT lines connected
- 22
- false-call handling time
- -46%
- sites on one data model
- 3
Standards supported
- IPC-A-610
- ISO 9001
- IATF 16949 (automotive EMS)
Typical monitored characteristics
- Solder paste volume
- Placement offset
- Reflow profile
- Solder joint quality
- ICT/FCT results
Where the cost sits
The problems we start from
These are the recurring findings from discovery workshops in this sector. If none of them describe your operation, a pilot is probably premature — and we will say so.
Operational problems
Repeat defects across lines go unnoticed
Each line investigates its own AOI data, so a stencil or reflow issue affecting six lines is treated as six separate problems.
False calls consume operator time
High AOI false-call rates train operators to override, which erodes confidence in the inspection system itself.
High mix makes trends invisible
Hundreds of assemblies per month mean per-product volumes are too small to see a trend without normalising across products.
Customer-specific reporting overhead
EMS customers each want their own quality reporting format, produced manually every month.
Engineering constraints
Very high mix, low volume
Analysis has to normalise across assemblies and components rather than assume long production runs.
Multiple inspection vendors
AOI, SPI and X-ray systems from different vendors expose incompatible data structures and defect taxonomies.
Component-level traceability
Tracing a failure to a reel, feeder and placement head requires machine-level data most reporting tools discard.
Fast changeovers
Several changeovers per shift mean setup-related defects dominate, and they only show up if changeover events are captured.
Platform capabilities
How the platform is configured for electronics & ems
Same architecture, same modules — configured against the characteristics, sampling logic and evidence expectations of this sector.
Unified defect taxonomy
Normalises AOI, SPI, X-ray and test outcomes into one board-level defect model so cross-line patterns become visible.
False-call reduction
Learns from operator verdicts to separate genuine defects from nuisance calls, and reports the false-call rate per station.
Process-window monitoring
Paste volume, placement offset and reflow profile monitored as a window rather than as isolated pass/fail checks.
Component and reel traceability
Links defects to reel, feeder, nozzle and placement head to expose equipment-specific causes.
Customer reporting packs
Scheduled per-customer quality reports generated from live data instead of assembled by hand each month.
Business outcomes
What changed for operations like yours
Customer-reported figures measured against documented pre-deployment baselines. Ranges, not single numbers, because process maturity dominates the result.
- Rework hours
- -34%
- False-call handling
- -46%
- Lines with one shared cause
- 6
- Monthly reporting effort
- -2 days
Rework hours
After shared root causes were addressed
False-call handling
Operator time on nuisance calls
Lines with one shared cause
Single stencil issue identified
Monthly reporting effort
Per customer account
| KPI | Typical movement | Measurement note |
|---|---|---|
| Rework hours | -25% to -40% | Driven by eliminating repeat defects |
| False-call rate | -40% to -60% | With operator feedback loop enabled |
| First pass yield | +1 to +3 pt | Varies strongly with assembly mix |
| Defect escape to test | -30% | Earlier detection at AOI/SPI |
| Reporting effort | -80% | Scheduled generation replaces manual packs |
Expected ROI
A value case your controller can interrogate
Every line below is an assumption, not a promise. During a pilot each one is replaced with a measured figure from your own baseline, which is what makes the business case defensible in a capital review.
| Value driver | Assumption | Annual |
|---|---|---|
| Rework reduction | 34% fewer rework hours | €420k |
| Operator time recovered | False-call handling halved across 22 lines | €180k |
| Scrap avoidance | Fewer boards scrapped at test | €150k |
| Reporting effort | 12 customer accounts, 2 days/month each | €110k |
| Indicative total | Before platform and integration cost | €860k |
Payback
6–10 months
From first connector to cumulative break-even
How we validate it
- Baseline recorded before any change
- Success criteria written into the pilot scope
- Measured comparison in the pilot report
- Exit conditions agreed up front
Illustrative model based on customer-reported ranges for high-mix EMS operations.
A unified board-level defect model surfaced a shared stencil issue affecting six lines that had been investigated independently for months; rework hours fell 34%.
Related sectors: Automotive, Battery manufacturing, Semiconductor
Start the evaluation
Find out where quality drift is hiding in your plant.
Bring one line, one defect family or one audit workflow. We will map the available data sources, quantify the cost of the current detection delay, and show the fastest route to measurable control.
45-minute technical walkthrough
With a solution architect who knows manufacturing data, not a scripted demo.
NDA before any data review
We can assess feasibility from sample exports without production access.
Written pilot scope
Baseline metrics, success criteria and exit conditions agreed up front.