ARIA/ARIA Track
Production traceability and genealogy
ARIA Track · Use cases

A day with ARIA Track.

Concrete scenarios with real personas. No generic storytelling — these are the steps you see on the line when ARIA Track is in production.

Scenarios2 scenarios
Persona

Chiara

Quality manager · automotive components line

A customer complaint comes in about a batch shipped weeks ago. Chiara has to reconstruct what happened to that batch — stations, parameters, timing — before she replies. With ARIA Track she opens the genealogy and within minutes has the full history, without digging through Excel sheets.

  • 09:00

    Searches the disputed batch by code

    → She types the batch number: ARIA Track opens the genealogy with every part in it and the stations traversed.

  • 09:04

    Opens the drill-down on the flagged part

    → She goes from the batch to the single product: creation timestamp, per-station process start and end, captured parameters. She immediately sees where the part deviates.

  • 09:10

    Compares against station history

    → She checks whether the anomaly is isolated or recurring on that station within the batch window. The event history is all there, no sheets to cross-reference by hand.

  • 09:15

    Exports the genealogy to CSV for the report

    → She attaches the full timeline to the customer reply and to quality. No command ever left toward the line: Track only observed and recorded.

Persona

Davide

Process engineer · e-mobility stator line

On the stator line built with CM Srl, each piece crosses more than 15 stations, every one with its own PLC. End-of-line electrical testing rejects three pieces from the same batch: Davide has to find which station introduced the defect before the line produces another hundred.

  • 09:00

    Electrical testing rejects three pieces

    → A reject tells you the piece is out of spec, not where it was spoiled. On a 15-station line, the difference between those two things is a production shift.

  • 09:10

    Opens the genealogy of the three rejected pieces

    → For each piece he sees the sequence of stations crossed, with the parameters recorded at each. All three share the same time window on one station only.

  • 09:25

    Compares that station's parameters against a good batch

    → The comparison isolates the drift: same recipe, same operator, one process parameter shifted. Not a hypothesis — the recorded data.

  • 09:40

    Holds the pieces that passed in the same window

    → The genealogy says exactly which pieces crossed that station in those hours. Those get held, not the whole batch.

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