how to link workshop test results to AI rotable spare planning?

You can reduce risk by linking workshop test evidence to rotable spare status so AI stock planning only treats returned rotables as serviceable when workshop acceptance test criteria are met, using AI repaired spare workshop test evidence as the decision input for planning.

How rotable spare planning is Applied

To connect workshop results to AI planning, you need a controlled data path from the workshop job record to the rotable item condition model used by planning. Practically, this means you define what “serviceable” means in measurable terms, capture the results in a structured way, and then map those results to a condition outcome that the AI can consume reliably.

• Define workshop acceptance test thresholds per component type, including pass or fail rules and any required tolerances, so workshop acceptance test outcomes become deterministic inputs rather than narrative notes
• Store rotable component condition as a structured field on the returned unit (for example, condition grade, test date, test method, measured values, and pass/fail), so the AI has consistent evidence to evaluate
• Attach ship spare reliability evidence to the same unit history by linking the repaired unit to its prior failure mode, removal reason, and operating hours since last overhaul, so planning accounts for repeat failure risk
• Implement a validation gate that blocks “serviceable” tagging for AI planning until the test record is complete and signed off by the responsible workshop role (you can enforce this with master data validation rules and workflow status checks)

Operational Impact

1. Technical Managers reduce downtime risk by ensuring AI planning does not allocate inventory based on incomplete or non-conforming repairs, improving confidence in rotable readiness and turnaround schedules.
2. Procurement Managers gain tighter budget control by preventing overstocking caused by conservative assumptions when workshop data is missing, while also avoiding understocking when repairs truly meet criteria.
3. Data governance improves audit readiness because every AI planning decision can be traced back to workshop acceptance test evidence, including who approved the result and which thresholds were applied.

Important to know: Treat workshop test evidence as a gating attribute, not as free text. If the workshop system records measured values and pass or fail outcomes in a consistent structure, you can map them to a single rotable condition outcome that AI planning can trust, which lowers the risk of counting returned rotables as serviceable when they are not.