Business intelligence tools can help airlines improve predictive maintenance. Airlines collect maintenance data and use BI tools to develop machine learning models that predict component failures. These predictive models analyze ongoing flight data and provide insights to schedule maintenance. Predictive maintenance improves safety, reduces costs, and increases efficiency by fixing potential issues before failures occur. BI capabilities like data warehousing, analytics, visualization, and collaboration help airlines optimize predictive maintenance programs.

1. PREDICTIVE MAINTENANCE IN
AVIATION INDUSTRY
Done by:
BANU PRAKASH
MAHANTHESH K N
NITHISH B C

2. INTRODUCTION
• In the aviation industry, predictive maintenance is becoming increasingly
important as a way to reduce costs, improve operational efficiency, and
ensure flight safety.
• This Business Intelligence case study will examine the impact of BI on
predictive maintenance, exploring topics such as the use of data and
analytics, the development of machine learning models, the benefits and
challenges of predictive maintenance, and the impact of BI on the
aviation industry.
• The study will provide insights into how BI can be leveraged to drive
improvements in predictive maintenance and help airlines meet their goals
of reducing costs, improving efficiency, and ensuring flight safety.

3. METHODOLOGY
• Data Collection: The first step in the methodology is to collect
relevant data from various sources, including airline maintenance
databases, flight logs, and industry reports. This data will be used to
develop machine learning models and perform predictive
maintenance analysis.
• Data Preparation: The collected data will be cleaned, transformed,
and normalized to prepare it for analysis. This will involve removing
any missing, duplicate, or irrelevant data, as well as transforming
the data into a format suitable for analysis.

4. • Model Development:
Using BI tools, machine learning models will be developed to
predict component failures. These models will be trained using
historical data and validated using a test set to assess their
accuracy.
• Predictive Maintenance Analysis:
The machine learning models will be used to perform predictive
maintenance analysis, using data from ongoing flight operations.
This analysis will provide insight into the likelihood of component
failures and support decision making for scheduling maintenance.

5. • Evaluation of Results:
The results of the predictive maintenance analysis will be evaluated
to determine the effectiveness of the machine learning models and
the impact of BI on predictive maintenance in the aviation industry.
• Conclusion and Recommendations:
Based on the evaluation of results, a conclusion will be drawn,
highlighting the key findings and recommendations for improving
predictive maintenance in the aviation industry that will provide
benefits and limitations of BI in predictive maintenance .

6. BI capabilities and solutions in Predictive
Maintenance in Aviation Industry
• Data Warehousing: BI applications provide data warehousing
solutions that enable airlines to store, manage, and analyze large
amounts of data from multiple sources.
• Predictive Analytics: BI applications provide powerful predictive
analytics tools that help airlines to identify patterns and trends in
data, predict component failures, and optimize maintenance
schedules.

7. • Visualization:
BI applications provide interactive visualizations that help present
complex data in a clear and understandable manner. This helps
decision makers to understand the results of predictive maintenance
analysis and make informed decisions.
• Collaboration and Sharing:
BI applications provide collaboration and sharing features that
allow multiple stakeholders to work together and share insights.
This helps airlines to effectively coordinate maintenance schedules
and resources, improving overall maintenance efficiency and
reducing costs.

8. • Mobile BI:
BI applications provide mobile capabilities that enable airline
maintenance personnel to access maintenance information from their
mobile devices. This helps to ensure that maintenance personnel
have access to the information they need, when they need it,
regardless of their location.
• Real-time Monitoring:
BI applications provide real-time monitoring capabilities that enable
airlines to monitor the status of their fleet in real-time, helping to
identify potential maintenance issues before they become problems.

9. • Predictive Modeling:
BI applications provide predictive modeling capabilities that
enable airlines to develop and implement predictive models that
can be used to predict component failures and optimize
maintenance schedules.

10. Business Intelligence (BI) tools used for Predictive Maintenance in
the Aviation Industry include:
• Data Warehouses: For example, SQL Server, Oracle, and IBM DB2
are popular data warehouse solutions.
• Data Mining and Predictive Analytics tools: For example,
RapidMiner, KNIME, and SAS Enterprise Miner.
• Dashboards and Visualization Tools: For example, Tableau, Power
BI.
• Collaboration and Communication Tools: For example, Microsoft
Teams, Slack.

11. RESULTS AND FINDINGS
• Improved maintenance planning, increased equipment reliability,
improved safety, reduced maintenance costs, and increased
customer satisfaction.
• Predictive maintenance allows airlines to plan maintenance
activities more efficiently, fixing potential equipment failures
before they occur, reducing the risk of accidents.
• The implementation of predictive maintenance has been shown to
be a cost-effective way to improve the overall efficiency and safety
of the aviation industry.

12. CONCLUSION
• The implementation of predictive maintenance in the aviation
industry has been a game-changer, providing numerous benefits
that have improved the overall efficiency, safety, and
reliability of the industry.
• With the use of advanced BI tools and techniques, airlines can
predict and prevent equipment failures, leading to improved
maintenance planning, increased equipment reliability, reduced
maintenance costs, and improved customer satisfaction.