Speaking at MRO Management's PAM Europe Conference this week in Dublin, Doug Goldfinch, senior solution consultant at Boeing Digital Services, emphasised that collaboration between airlines, OEMs, suppliers and engineers will be fundamental to translating increasingly sophisticated predictive technologies into trusted operational decisions.
“Predictive maintenance has evolved. Ten years ago, many conversations focused on what might be possible. Today, predictive maintenance is delivering measurable operational value across airlines around the world,” Goldfinch told delegates.
He pointed to the growing availability of connected aircraft data, advances in analytics and the emergence of artificial intelligence (AI) as factors that have accelerated predictive maintenance capabilities.
However, while the technology has matured considerably, integrating those capabilities into established maintenance processes remains a significant challenge.
“Predictive maintenance works and the industry has proven it. The challenge now is making it a trusted part of everyday operations,” he said. “But how do we make predictive maintenance a trusted part of everyday maintenance operations? How do we move from developing predictive capabilities to embedding them into everyday workflows?”
Goldfinch argued that the answer lies not simply in developing more sophisticated algorithms, but in bringing together the different areas of expertise required to interpret aircraft data and translate predictive insights into meaningful maintenance actions.
“Operationalising predictive maintenance begins with collaboration,” he explained. “Airlines; Operational experience from daily flight and maintenance activities. Suppliers; Deep component expertise and failure mode knowledge. OEMs; Aircraft design knowledge and systems understanding. Engineers; Connect all of those perspectives together into actionable insights.”
This collaborative approach, he suggested, becomes increasingly important as predictive applications move beyond identifying relatively straightforward operational trends towards more complex aircraft system and component-level diagnostics.
For airlines, the critical measure of success is whether predictive alerts are trusted by engineers, consistently acted upon and ultimately contribute to reducing unnecessary maintenance, troubleshooting time and operational disruption.
“Technology is an important enabler. But technology alone does not operationalise predictive maintenance. Collaboration does.”
Goldfinch also highlighted the importance of continuous learning, with maintenance findings and operational experience feeding back into the development and refinement of predictive capabilities.
Boeing's Prognostic Research Collaboration Program was cited as an example of how industry-wide cooperation can help share knowledge and improve predictive performance.
The presentation also provided an overview of Boeing's expanding Digital Services portfolio, including its Condition-Based Scheduled Maintenance solution, which uses aircraft health and performance data to support a transition away from fixed maintenance intervals towards condition-based interventions.
Meanwhile, Boeing's AHM Predictive Alerts Builder, formerly Insight Accelerator, uses AI-driven analytics to identify patterns in flight and maintenance data, allowing engineers to develop, test and refine fleet-specific predictive alerts without requiring coding or specialist data science expertise.
Goldfinch also outlined the capabilities of Boeing ReliablePlane, which combines wireless sensor data with Boeing-authored diagnostic procedures to identify early component degradation, support repeatable maintenance testing and reduce unnecessary component removals.
A further development highlighted was Dataliner, Boeing's new AI-powered data intelligence platform, designed to bring fragmented aviation data together into a common intelligence environment.
Developed using technologies from Microsoft, Databricks and others, the platform is intended to support fleet-wide analytics, predictive maintenance and AI-assisted engineering workflows while prioritising data security, sovereignty and customer choice.
Boeing's AHM Predictive Alerts solution is already hosted on Dataliner, with Airplane Health Management scheduled to migrate to the platform. New applications include TechOps AI Agent, designed to support engineering workflows from initial triage through to execution, and Asset Lifecycle Planning, which applies AI to engine maintenance planning.
"Together, these developments reflect Boeing's broader strategy of connecting aircraft intelligence with the operational processes required to act on it, rather than treating predictive analytics as a standalone capability," Goldfinch said.
For Goldfinch, the direction of travel is clear. With predictive maintenance increasingly established across the industry, the next phase of its development will be defined less by the ability to generate alerts and more by the ability to turn those alerts into trusted, repeatable maintenance decisions that improve fleet availability and reduce the overall maintenance burden.
