How to optimize Aircraft Turnaround Time with the help of Data Analytics?

Efficiency is the cornerstone of successful airline operations, and one crucial aspect that directly impacts overall performance is Aircraft Turnaround Time (TAT). TAT is the time it takes to prepare an aircraft for its next flight after landing. It involves a series of complex processes, including maintenance, refueling, cleaning, and boarding. Reduced TAT not only enables airlines to operate more flights in a given time frame but also ensures better resource utilization, leading to increased revenue. Additionally, faster turnarounds contribute to improving on-time performance, enhancing the overall passenger experience, and strengthening the airline's competitive edge.

Maintenance and Engineering departments often face hurdles such as unpredictable technical issues. This can lead to delays, affecting the entire operational schedule.

Let’s quickly review what is data analytics.

Data analytics is the process of examining data sets to find trends and draw conclusions about the information they contain. It's a diverse field that uses various analysis methods, such as math, statistics, and computer science, to gain insights from data. Data analytics covers everything from basic data analysis to thinking about how to collect data and creating the structures to store it. For example, predictive maintenance is a specific application of data analytics within a broader field, using analytical techniques to forecast equipment failures and proactively manage maintenance activities.

How can data analytics help in the process of TAT?

As mentioned, one of the primary applications of data analytics in aviation is predictive maintenance. Predictive maintenance relies on the analysis of historical and real-time data from various sensors and components of the aircraft. By continuously monitoring the health and performance of critical systems, predictive maintenance algorithms can identify potential issues before they escalate. This early detection allows maintenance teams to address problems during scheduled turnarounds, minimizing the likelihood of unexpected issues causing delays. This results in shorter TAT, as the aircraft is more likely to be ready for its next flight without unexpected delays due to unforeseen maintenance requirements.

Furthermore, maintenance & engineering teams can plan and schedule maintenance activities more proactively. Instead of reacting to failures, airlines can adopt a preventive approach, addressing potential issues during routine maintenance checks. This proactive planning ensures that necessary repairs or replacements are conducted within the allotted turnaround time, preventing the need for additional, unscheduled maintenance that could extend TAT.

Another advantage of knowing in advance which components may require attention allows for better resource allocation. Maintenance & engineering teams can ensure that the right personnel, tools, and spare parts are available during turnarounds. This optimization of resources contributes to faster and more efficient maintenance processes, directly impacting TAT.

Overall, Predictive maintenance provides maintenance & engineering teams with data-driven insights. This information allows for more informed decision-making during turnarounds. Teams can prioritize tasks based on the predicted health of various components, ensuring that critical issues are addressed promptly, and non-urgent tasks are scheduled efficiently.

How can we achieve this?

Implementing predictive maintenance to improve TAT requires a systematic approach by maintenance and engineering teams. As always it involves several steps.

We need to start by clearly defining the objectives of implementing predictive maintenance. Determine specific goals related to TAT improvement, such as reducing unplanned maintenance delays, optimizing resource utilization, and enhancing overall operational efficiency.

This is followed by gathering relevant data that is needed to achieve the objective from various sources, including historical maintenance records, sensor data from aircraft components, and other relevant operational information. Establish data integration processes to bring together diverse datasets for comprehensive analysis.

IMPORTANT: Before you can benefit from data analytics, we need to focus on data quality and data standardization. We cannot repeat it enough garbage means garbage out – you cannot drive value from data and make the right decisions on flawed data. Hence, you need to work on your data. Cleanse and preprocess the data to handle missing values, outliers, and inconsistencies. Ensure data quality and integrity for accurate analysis. (Read also our blog about data quality).

Next, you need to select appropriate analytics tools and technologies based on the nature of the data and project requirements. Develop statistical or machine learning models tailored to predict maintenance needs, identify trends, or optimize processes. Test and refine models to ensure accuracy and reliability. You also need to choose a visualization tool that is suitable for presenting data in a clear and meaningful way. Create user-friendly dashboards that provide a comprehensive overview based on your objectives and models. Ensure that the design facilitates easy interpretation of trends, patterns, and critical information.

REMARK: Do not forget to integrate the predictive maintenance systems with your MRO/M&E software system. This ensures a seamless flow of information between predictive models and the tools used by maintenance and engineering teams for planning and execution.

It is also recommended to define thresholds for various parameters to trigger alerts when potential issues are detected. These thresholds should be set based on the analysis of historical data and the specific requirements of the aircraft and components.

Provide training to the maintenance and engineering team on the use of predictive maintenance tools and the interpretation of alerts. Ensure that there is a clear understanding of how these tools will integrate into existing workflows.

We also recommend conducting a pilot implementation of the predictive maintenance system on a subset of the fleet. This allows you to evaluate the effectiveness of the system in a controlled environment before full-scale deployment.

If all goes well, implement a continuous monitoring process to assess the performance of the predictive maintenance system. Gather feedback from maintenance teams and use it to refine and improve the models over time.

How EXSYN can help

In the context of optimizing TAT through data analytics, tools like EXSYN's AVILYTICS play a significant role. AVILYTICS stands out with its comprehensive approach, integrating data from MRO/M&E Systems, Flight Operations, Finance Systems, and onboard aircraft data. This integration facilitates a complete view of fleet management and technical reliability and predicts component failures to see AOG risks on your aircraft aiding in decision-making to act before it impacts operations and plant potential issues during routine maintenance checks resulting in reducing operational costs.

What sets AVILYTICS apart is its ability to provide a centralized data warehouse, which is crucial for advanced decision-making, and the creation of customized dashboards by users. It supports data synchronization and offers user-friendly dashboards for monitoring key metrics like dispatch reliability. These features enable maintenance teams to react swiftly to changing scenarios, enhancing aircraft availability.