Leadership wanted to move from time-based and reactive maintenance to data-driven predictability: prioritize maintenance, reduce unplanned findings, and maintain full compliance while using sensor and historical data effectively.

Key challenges included:

• Unplanned findings and extended AOG
• Limited use of operational and sensor data for prioritization
• Need for RUL-style prognostics and compliance
• Lack of structured continuous improvement

Datawise integrates predictive maintenance and RUL-style prognostics with inspection and MRO workflows: deep learning and similarity-based models for degradation estimation, combined with historical and real-time sensor data.

The solution enabled:

• Data-driven prioritization of maintenance (CBM-aligned)
• Historical and real-time sensor data for continuous improvement
• Integration with inspection and MRO workflows
• Fewer surprise findings and reduced extended AOG

Teams gain research-backed, data-driven prognostics and operational data for continuous improvement and audit trail.

1. 1Predictive and RUL-style models use historical and real-time sensor data
2. 2Degradation patterns and fault detection prioritize maintenance and reduce surprise findings
3. 3Data is integrated with inspection and MRO workflows
4. 4Continuous improvement is structured and measurable

• Operational and sensor data leveraged for continuous improvement
• Fewer surprise findings and reduced extended AOG
• Data-driven prioritization of maintenance
• Structured continuous improvement; research-aligned prognostics

• Better predictability and fewer disruptions
• Lower AOG and maintenance costs
• Stronger compliance and audit trail
• Scalable for large and small–medium airlines (ATSaaS-style)