Infrastructure
To connect production test data across stations and sites, teams need a shared data layer that links serial numbers, product IDs, station IDs, measurements, limits, timestamps, retest history and repair outcomes. This allows engineering and quality teams to analyse production performance across the full manufacturing workflow.
A single station result can tell you whether one unit passed or failed. Connected test data can show whether a failure pattern is recurring, whether one fixture is drifting, whether retests are concentrated on a line, or whether a quality issue is appearing across multiple sites.
The challenge is that production test data is rarely created in one standard format.
Manufacturing test environments often evolve over time.
One product line may use LabVIEW. Another may use TestStand. A newer line may export directly to SQL. A contract manufacturer may send CSV files. Repair teams may use spreadsheets. Quality teams may manage customer issues in another system.
Each workflow solves a local problem, but the wider data picture becomes fragmented.
To connect test data across stations and sites, teams need a shared view of the key identifiers and events.
Important fields usually include:
These fields make it possible to compare performance across the production workflow.
Many teams assume they need to rebuild their test architecture before they can get better visibility.
That is usually not the best first step.
A more practical approach is to create a connected data layer above existing systems. This allows teams to preserve working test processes while making the resulting data easier to analyse.
The goal is to bring together the information needed for investigation, traceability and trend analysis without forcing every station to use the same tool from day one.
Once production test data is connected, teams can answer higher-value questions:
These questions are difficult to answer when data is scattered across local exports and manual reports.
AI can make connected test data more usable by helping teams query, compare and summarise production evidence.
Instead of manually combining files and writing one-off scripts, engineers can investigate patterns across the connected data layer.
AI should not make final engineering or quality decisions. Its value is in helping teams find relevant evidence faster.
Arc helps teams connect production test data across stations, products and sites. It sits above existing test systems and helps teams move from scattered results to connected insight across yield, failures, traceability and quality.
Arc is designed for teams that want better visibility without replacing every existing test, MES or quality workflow.
Teams connect production test data by creating a shared layer that links station outputs to common identifiers such as serial number, product ID, station ID, timestamp, measurement name, limits and result status.
Important fields include serial number, product ID, product revision, station ID, site ID, fixture ID, measurement name, limits, pass/fail result, timestamp, retest status, repair action and final disposition.
No. A connected test data layer can sit above existing systems, preserving working station processes while making the resulting data easier to analyse across products, lines and sites.
Production test data fragments when different lines, stations, products, sites and teams use different tools, file formats, naming conventions, databases, spreadsheets and reporting workflows.
Connected data lets teams compare failures across stations, fixtures, product revisions, sites, retests and repair outcomes, making recurring production issues easier to investigate.
If your production test data is spread across LabVIEW, TestStand, CSVs, SQL databases, spreadsheets, MES exports or repair records, Arc can help you map where the data sits and where visibility is breaking down.
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