Predictive Readiness Scorecard

EXSYN Aviation Solutions

0 / 15 Answered

Predictive maintenance isn’t a tool you buy.
It’s a capability you earn.

Across CAMO and M&E teams, we see the same pattern: advanced analytics promised, but blocked by inconsistent data, manual fixes, and last-minute compliance stress. Before an airline can be predictive, it must be predictive-ready.

Purpose

This diagnostic scorecard helps airlines and CAMO organisations:

  • Assess whether data foundations support predictive use cases.
  • Identify where compliance or documentation discipline breaks down.
  • Align CAMO, Engineering, and IT on what “ready” actually means.
"Predictive success starts long before algorithms. It starts with data discipline."
EXSYN Aviation Solutions

Diagnostic Questions

1. Maintenance History Integrity

1. Is your maintenance history complete, validated, up-to-date and available in your main ERP?

2. Do maintenance events and their corresponding evidence (work cards, CRS, tech logs) match consistently?

3. Do you encounter repeated manual checks because data from maintenance events cannot be fully trusted?

2. Counter & Timestamp Stability

4. Do counters (FH, FC, APU cycles, LDGs) remain stable across aircraft, components, and events?

5. Are discrepancies in FH/FC between flight logs, maintenance logs, and system counters rare?

6. When counter drift occurs, is the root cause easy to identify and correct?

3. Effectivity & Applicability

7. Are SB/AD effectivity tables aligned with actual document effectivity and configuration data?

8. When engineering releases new tasks, do applicability rules map correctly to the physical configuration?

9. How often do tasks or SB/AD requirements need to be manually cross-checked due to unclear applicability?

4. Configuration & Compliance

10. Can you trust that an aircraft’s system compliance reflects its true physical status?

11. Are modifications, replacements, and removals reflected consistently in your systems?

12. Do audits reveal discrepancies between configuration, evidence, and recorded status?

5. Reliability Signal Clarity

13. Are your reliability KPIs stable month-over-month (no unexplained fluctuations)?

14. Can you easily trace an anomalous reliability signal back to related maintenance events?

15. Do engineers spend time rebuilding data manually because system KPIs lack trust?

Ready for your Predictive Stability Score?

Please answer all 15 questions above to calculate your personalized diagnostic.