Results are stored locally
Test results may sit on station PCs, shared drives or local databases, making it hard to build a full production view.
Production Test
Production Test Data Management
Centralise production test results across stations, products and lines so manufacturing, quality and engineering teams can act on them.
What is production test data management?
Production test data management is the process of collecting, structuring and analysing the results generated by manufacturing test systems. It helps teams understand pass/fail outcomes, measured values, test limits, retests, station performance and traceability across products and production lines.
What is production test data management?
Production test data management is the process of collecting, structuring and analysing the results generated by production test systems.
It covers the records produced when units are tested during manufacturing, including pass/fail results, measured values, test limits, timestamps, serial numbers, station IDs, operators, retests and failure codes.
The goal is simple: make production test data usable beyond the test station.
Why production test data gets scattered
Production test systems often grow incrementally. A team may start with one tester, one product and one report format. Over time, the environment becomes more complex:
- More products and variants
- More test stations
- More fixtures and operators
- More software versions
- More customer-specific limits
- More retest and rework flows
- More reporting requirements
Without a central structure, production test data becomes difficult to compare, search and trust.
Common problems
Retests are hard to interpret
A unit may fail, be reworked, pass later and then ship. Without clean retest history, teams struggle to understand what actually happened.
Failures are difficult to compare
Failure names, test step names and limits may vary across stations or product versions.
Quality reporting is manual
Teams often rely on spreadsheets, screenshots or manually prepared customer reports.
Test data lacks context
A result without station, fixture, product, serial number, limit version and software version is hard to use for investigation.
What good production test data management enables
Faster failure investigation
Teams can identify which test steps are failing, where failures are concentrated and whether problems are linked to a station, product variant or batch.
Better yield visibility
Production and quality teams can monitor first pass yield, retest rates, failure rates and performance over time.
Stronger traceability
Each unit can be linked to its test history, measured values, limits, station and result status.
Improved quality workflows
Test evidence can support non-conformance reviews, customer reporting, supplier investigations and audit preparation.
What data should be captured?
A useful production test data model should capture:
- Product family
- Product variant
- Serial number or unit ID
- Batch or work order
- Test station
- Fixture
- Operator
- Timestamp
- Test sequence or software version
- Test step name
- Measured value
- Test limits
- Pass/fail result
- Failure code
- Retest history
- Rework status
- Report output
- Customer or quality reference
How Arc helps
Arc helps teams create a structured layer around production test data so results can be searched, analysed and connected to quality workflows.
Arc is designed for teams that need to move beyond raw files and manual reports toward a clearer view of manufacturing test performance.
How production test data management differs from dashboards
Dashboards help visualise selected production metrics. Production test data management focuses on the underlying test records, limits, retests, station context and quality evidence that make those metrics trustworthy and useful for investigation.
FAQ
What is production test data?
Production test data is the information generated when manufactured units are tested, including measured values, pass/fail results, limits, timestamps, station IDs and serial numbers.
Why is production test data important?
It helps teams understand yield, failures, rework, traceability, product quality and manufacturing performance.
What is the difference between production test data and engineering test data?
Production test data comes from repeatable manufacturing test workflows. Engineering test data is often more exploratory and used for design, validation or development.
Can production test data help improve first pass yield?
Yes. Better production test data makes it easier to identify recurring failures, station issues, unstable limits and process drift.
Does production test data need to connect to quality systems?
Usually, yes. Quality teams often need test records for investigations, customer evidence, non-conformance workflows and audits.
Request Access
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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