Speaking at MRO Management's PAM Europe Conference in Dublin this week, Carles Cánovas, predictive maintenance engineer at Volotea, outlined the airline's journey from initial implementation to fleet-wide deployment, highlighting the importance of converting predictive alerts into actionable maintenance decisions.
“Every operator that implements predictive maintenance has the best intentions and that's to harness everything that it offers. All operators want the same benefits,” Cánovas told delegates.
For Volotea, the business case has been shaped by the particular demands of its operating model. The airline operates 41 A320 and A319 aircraft, with an average fleet age of 19.3 years, across 21 operational bases. During the peak summer season, it operates approximately 320 flights per day, with aircraft averaging seven cycles daily.
“Limited ground time, high aircraft utilisation and a distributed network made predictive maintenance a necessity rather than an option,” Cánovas explained.
The airline began its RMAX journey in 2024 with a one-year trial covering 25 aircraft, focusing initially on developing internal processes, defining KPIs and optimising alert thresholds. Full fleet deployment followed in 2025, alongside the standardisation of procedures and the introduction of automated reporting dashboards.
By 2026, the emphasis had shifted towards scaling predictive capabilities across operational teams and embedding the technology into maintenance planning, spare parts positioning and resource allocation.
“Implementing RMAX gave us predictive insights. Turning those insights into operational value required a structured journey,” said Cánovas.
Cánovas explained that central to this approach is a defined process linking engineering assessments with maintenance control, planning and logistics. Predictive alerts undergo validation and risk assessment before being translated into work orders, spare parts allocations and planned maintenance interventions. Subsequent engineering reviews assess corrective actions and monitor data trends.
“A predictive alert is only the starting point; operational value is created through structured decision-making,” he emphasised.
The results presented in Dublin provide a tangible indication of the programme's effectiveness. During 2026, Volotea reviewed 485 predictive alerts, identifying 320 confirmed faults and generating 246 planned work orders. "These interventions resulted in 141 component replacements and an estimated 112 avoided operational events," Cánovas said.
Of those avoided events, Cánovas explained that 106 were potential delays and six were diversions. The airline also calculated savings of 195 troubleshooting man-hours and 10.4 tonnes of fuel.
Excluding 54 correlated alerts associated with the same faults, Cánovas explained that Volotea achieved an operational value rate of 25.9%, while recording a no-fault-found (NFF) rate of just 4.3% across 141 component removals.
One particularly significant example concerned the aircraft bleed air system, historically a major contributor to technical delays across Volotea's fleet.
“Predictive maintenance helped transform the bleed system from Volotea's leading source of delays in 2024 to the 7th-ranked contributor in 2026,” Cánovas revealed.
Bleed system-related technical delays fell from 36 in 2024 to 18 in 2025 and 13 in 2026. Cánovas explained that the airline attributed the improvement to predictive insights combined with OEM collaboration, particularly in identifying faults associated with high-pressure valves, pressure regulating valves and bleed temperature.
Across 107 bleed system predictive alerts, Volotea reported 29 avoided fault events, representing an operational value rate of 27.1%. Just three of 28 predictive component removals resulted in NFF findings.
The figures underline a broader message from Cánovas' presentation: while predictive technology can identify emerging faults, its effectiveness ultimately depends on the processes and coordination required to turn those insights into timely maintenance action.
For an airline operating a highly utilised fleet across a geographically dispersed network, Volotea's experience demonstrates how integrating predictive maintenance into everyday engineering and operational decision-making can deliver improvements in reliability, resource utilisation and disruption avoidance.
