Yield analysis
Track first pass yield, final yield, retest yield and product-level yield trends.
Analytics
Manufacturing Test Data Analytics
Turn production test results into insight across yield, failures, station performance, quality trends and recurring defects.
What is manufacturing test data analytics?
Manufacturing test data analytics uses production test results to understand yield, failures, station performance, trends and recurring quality issues. It connects measured values, limits, timestamps, products and retests so teams can move beyond isolated pass/fail records.
What is manufacturing test data analytics?
Manufacturing test data analytics is the process of using test results to understand what is happening across production.
It helps teams move from isolated pass/fail records to useful answers about yield, failures, drift, station performance, retest behaviour and product quality.
Why test analytics is hard
Most teams already collect test data. The problem is that the data is often not structured for analysis.
A production line may generate thousands or millions of measurements, but teams still struggle to answer:
- Which test steps fail most often?
- Which stations produce the highest failure rates?
- Which failures are increasing over time?
- Which products have weak first pass yield?
- Which failures are linked to a fixture, supplier or batch?
- Which units passed only after retest?
- Which quality issues are recurring?
Analytics use cases
Failure analysis
Identify recurring failures, weak test steps, marginal measurements and failure clusters.
Station performance
Compare failure rates, measurement distributions and retest behaviour across stations.
Limit monitoring
Understand how limit changes affect pass/fail outcomes and product quality.
Quality investigation
Use test data to support non-conformance reviews, customer complaints and internal investigations.
Supplier or batch analysis
Link failures to suppliers, batches, lots or incoming material quality where that context is available.
What makes test data analytics reliable?
Reliable analytics depends on consistent context. Raw measurements are not enough.
Teams need:
- Consistent test step names
- Product and serial number context
- Test limits and limit versions
- Station and fixture context
- Timestamp and operator context
- Retest and rework history
- Clear pass/fail definitions
- Links to quality workflows
- A single view across test systems
Example analytics questions
A useful test data layer should help answer questions such as:
- Show all failures for this product over the last 30 days.
- Which station has the highest retest rate?
- Which failure modes are increasing this week?
- Which units failed before passing?
- Which measurements are trending toward limits?
- Which product variants have the lowest first pass yield?
- Which batches are linked to repeated failures?
How Arc helps
Arc helps teams structure manufacturing test data so it can support analysis across production, quality and engineering workflows.
Instead of leaving data trapped in files, station PCs or isolated systems, Arc helps create a more connected view of test performance.
How test data analytics differs from raw reports
Raw reports show what happened in one test run or one station. Manufacturing test data analytics connects many records over time so teams can compare products, stations, batches, limits and recurring failure patterns.
FAQ
What is manufacturing test data analytics?
It is the analysis of production test results to understand yield, failures, trends, station performance and quality issues.
What data is needed for manufacturing test analytics?
Teams need measured values, limits, pass/fail results, product IDs, timestamps, station IDs, test step names and retest history.
How does test data analytics help quality teams?
It helps quality teams investigate failures, prepare evidence, identify recurring problems and support corrective action workflows.
Can test data analytics identify station problems?
Yes. Comparing results across stations can reveal fixture problems, calibration issues, operator variation or local process issues.
Is test data analytics only useful at high volume?
No. It is useful whenever teams need to compare results across products, stations, time periods or quality events.
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Bring an example of your current test data workflow and we’ll map where results, failures, limits, traceability and quality evidence are getting lost.
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