CAMP Systems launches AI tool to modernise aircraft maintenance
Operations leaders want fewer AOG surprises, faster turn times, and clean audits. CAMP Systems has introduced an AI tool aimed at those outcomes by compressing how teams interpret data, build work packages, and keep fleets compliant.
Think of it as a smarter layer on top of maintenance records, manuals, and parts data. It reduces manual lookups, flags issues earlier, and turns fragmented inputs into a clear plan for action.
What this means for Operations
- Fewer unplanned events: Early signals from trends in write-ups, sensors, and cycles help teams act before a delay or deferral.
- Faster planning: Auto-suggested work scopes, parts lists, and task sequencing shorten hangar time.
- Cleaner compliance: Cross-references tasks against manuals, ADs, and intervals to reduce misses.
- Better wrench time: Technicians get step-by-step guidance and context without digging through multiple systems.
- Tighter cost control: Inventory and vendor lead times are factored in earlier, improving buy/repair decisions.
How it fits into the maintenance flow
- Data intake: Flight hours/cycles, work orders, MEL/CDL, repetitive defects, sensor streams, and OEM publications.
- Prioritisation: AI triages discrepancies by safety impact, recurrence, fleet exposure, and parts availability.
- Work package build: Generates draft task lists, labour estimates, tooling, and required documents.
- Compliance check: Maps tasks to ADs, SBs, and program intervals; flags gaps before release.
- Technician assist: Natural language search across manuals and historical fixes, with citations for traceability.
Expected impact (track these KPIs)
- AOG hours per 1,000 flight hours and repeat discrepancy rate
- Mean time to build a work package and hangar TAT
- Schedule reliability and deferred defect backlog
- Technician wrench time ratio and first-time fix rate
- Parts expedite spend and inventory turns
Integration and governance
Expect APIs or native links into maintenance tracking, inventory, and scheduling tools. The value grows when the AI can see work history, open tasks, and stock positions in one view.
Treat the system like any critical maintenance application. Validate data lineage, model explainability, and audit trails. Confirm export controls, supplier access, and data residency policies are in place.
Recommended rollout plan
- 30 days: Pick a pilot fleet, define success metrics, and connect core data sources. Run parallel with current planning.
- 60 days: Expand to routine checks and repetitive defects. Compare AI work packages vs. human-built for quality and time.
- 90 days: Add inventory and vendor data, enable technician assist, and push into heavy checks if results hold.
Questions to ask before go-live
- Which data sources are required on day one, and what improves accuracy over time?
- How are AD/SB updates and OEM revisions ingested and cited?
- What guardrails stop the tool from suggesting non-approved methods?
- How are recommendations explained and logged for audits?
- What are the failure modes, alerts, and human-in-the-loop steps?
Quick checklist for Operations
- Define hard targets: AOG reduction, TAT, backlog, and cost per flight hour.
- Pick clear use cases: repetitive defects, check planning, or parts forecasting.
- Set approval rules: who reviews AI-generated packages and compliance checks.
- Train planners and leads; measure adoption and impact weekly.
- Close the loop: feed outcomes back to improve recommendations.
Upskill your team on AI in operations
If your planners and supervisors need a fast primer on applying AI to scheduling, inventory, and maintenance workflows, review these resources:
Bottom line: If this AI tool shortens decision time from hours to minutes and lifts schedule reliability, it's worth a pilot. Start small, instrument the metrics, and scale where the data proves out.
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