Data Model Overview

Structured, Product-Centric Trace Architecture

Traceability is only as strong as its data model.

x4Trace is built on a structured, product-centric data architecture where every unit is represented as a controlled entity — not as a collection of disconnected log entries.

Product Instance as the Core Entity

At the center of the data model is the Product Instance.

Each instance contains:

  • Unique product identifier
  • Flow definition reference
  • Current execution state
  • Timestamp metadata
  • Lifecycle status

All production data is linked to this core entity.

No orphan records.
No ambiguous ownership.

Operation-Level Trace Records

Every time a product passes through an operation, a structured trace record is created.

Each operation record includes:

  • Station identifier
  • Operation type
  • Execution result (PASS / FAIL / Code)
  • Start and end timestamps
  • Associated hardware context

This ensures full chronological reconstruction of product history.

Parameter & Variable Storage

For each operation, parameter-level data is stored explicitly.

Examples:

  • Measured torque values
  • Sensor readings
  • PLC variable states
  • Operator inputs
  • Validation flags

Parameters are not stored as raw text logs.
They are structured, queryable values linked to the operation instance.

This enables:

  • Targeted analysis
  • Root cause filtering
  • Statistical evaluation
Validation & Routing Metadata

Each state transition includes recorded metadata:

  • Evaluated rule
  • Result of validation
  • Routing decision taken
  • Exception flags (if any)

This ensures that every branch decision can be audited.

Rework Tracking

Rework is stored as structured loop iterations.

The model supports:

  • Iteration counters
  • Linked failure codes
  • Timestamped retry attempts
  • Final disposition tracking

Rework history remains part of the same product identity.

Data Integrity Principles

The x4Trace data model enforces:

  • Referential consistency
  • Explicit entity relationships
  • Structured parameter storage
  • Deterministic state history

Every product maintains a coherent digital footprint from first scan to final completion.

Enterprise & Analytical Readiness

Because the model is structured:

  • ERP synchronization is consistent
  • Reporting queries remain predictable
  • AI analysis receives contextual datasets
  • Historical audits are reproducible

Data is not reconstructed after the fact.
It is structured correctly at creation time.

Summary

The x4Trace data model is designed around controlled production logic.

It does not accumulate activity logs.
It builds structured product histories.

Traceability is not an output feature.
It is the foundation of the architecture.