CASE 10 · CASE ATLAS · LEVEL B WORKSHOP SCENARIO

Cross-document risk intelligence

SITUATION

A single critical characteristic needs to be traced across the requirements specification, risk analysis, control plan, inspection instructions and inspection records to confirm nothing was lost or altered as the characteristic moved through revisions and organizational handoffs.

EVIDENCE

All five document types, each carrying the characteristic identifier, revision, and where available a cross-reference to the adjacent document in the chain.

AI APPROACH

The Quality Evidence Graph builds the trace across all five artefacts and highlights any point where the characteristic's stated value, tolerance or method is inconsistent between adjacent links, or where a link is entirely absent.

KEY CONTROLS

Every inconsistency is reported with the exact two documents and revisions compared; the trace states its own coverage rather than implying completeness it did not achieve.

VALIDATION MEASURES

Seed a known set of documents with deliberate cross-document inconsistencies and confirm the tool detects the seeded breaks before trusting it on a live characteristic.

HUMAN DECISION

The accountable quality engineer investigates every flagged inconsistency and decides whether it reflects a real drift requiring correction or a benign difference in representation.

DISCUSSION QUESTIONS

  1. What is the actual cost, historically, of an inconsistency like this surviving to serial production undetected?
  2. How do cross-reference metadata gaps limit what this trace can honestly claim to cover?
  3. What would make you trust this tool's coverage statement enough to stop doing a parallel manual trace?

TRY IT LIVE

Run this scenario's approach through Quality Evidence Graph — What connects this decision to its evidence, controls, and lessons learned?

Open Quality Evidence Graph →