One of the most impacted industries during Covid, was the Airline industry.

Bringing multiple data sources into one single view and pane of glass to make informed and smarter decisions, was vital to change the maintenance program – and bring aircraft back online after long term groundings and storage. Airlines needed to look at over a year's worth of data to ensure that the aircraft were safe while minimising any potential surprises.

The was also a need to improve work scope to maximize capability, helping to cope with losing skills and an aging workforce, post-COVID, that is almost impossible to replace at the same rate.  New skillsets were created in the team to extract value from this data, combining data analytics and practical knowledge of the systems. Bringing this all together brought success to predictive maintenance and defect management teams.

Combining data sources of sensors, maintenance information data, in-flight alerting, spare parts type provisioning, and other general IP into an accumulated data centralised capability speeds up the research activity. This effectively aggregates the data, bringing it into a common format or common location.

Hear how these systems (often SAAS) result in much more varied data sources, reducing the specialist capabilities and coding capabilities required.  They also don’t need to be implemented across all the infrastructure across the business.

Benefits are being better at maintenance activity, continually improving our maintenance program to lower the cost of maintaining the asset, maximizing yield, improving safety and minimizing disruption.

• There is no perfect tool – just start the journey and your use of the tools will evolve.
• Extracting the data away from the original source allows great manipulation.
• Understand that technology is accelerating at a rate faster than most of our assets
• You have to develop the skills within your team to make smarter decisions.

Maintenance and reliability professionals are increasingly turning to data-driven approaches to enhance operational efficiency and asset longevity. This panel discussion delves into the two paramount challenges we're facing as we navigate the complexities of data utilization: data quality and integration, and the skill gap in data analytics.

Ensuring data quality and seamless integration still poses a significant hurdle. Maintenance and reliability operations generate vast amounts of data from diverse sources, including sensors, equipment logs, and maintenance records. However, inconsistencies, inaccuracies, and fragmentation in data can undermine decision-making processes. The panel will explore strategies for improving data quality, standardizing data formats, and integrating disparate data sources to create a cohesive and reliable data ecosystem.

The skills gap in data analytics also remains a critical issue. While advanced data analytics tools and techniques offer tremendous potential, the lack of personnel with the requisite expertise to leverage these tools effectively hinders progress. This discussion will address the need for upskilling existing staff, attracting new talent with data science proficiency, and fostering a culture of continuous learning within maintenance and reliability teams.

Join us as industry leaders share insights, experiences, and practical solutions to these pressing challenges, paving the way for a more data-savvy and resilient future in maintenance and reliability.