Predict Parts Failure to Prevent Flight Delays

Airlines Operations Decrease Costs Improve Customer Experience Executive Summary Predictive Maintenance
Predict the likelihood an aircraft part will fail in order to intervene and prevent flight delays or complications from occurring.
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Business Problem

In 2016, the cost of maintenance-related delays for airlines in the US was well over $0.5 billion. Moreover, almost a third of total delay time is due to unplanned maintenance. For aviation companies, delays and cancellations are a huge and expensive problem. This has drastic effects not only on the airlines, but also on passengers who suffer inconveniences due to delays. Currently there is no specialized process that can rationalize, predict, and streamline maintenance for aircrafts. It is pertinent that advanced analytics be utilized in order to help airlines maintain efficiency, improve the quality of their fleet, and reduce delays and cancellations which would inevitably impact their reputation and consumers.

Intelligent Solution

AI will allow your organization to predict when maintenance will be required. AI’s time series forecasting capabilities can leverage historical maintenance data to predict future maintenance needs. AI can also leverage binary classification with fault messages as Minimum Equipment List (MELd) or “Fix it” which allows your maintenance crew to determine the next best action on a part level. By empowering your maintenance team on the likelihood of maintenance required for a specific aircraft, you will be able to remove an aircraft from service proactively and prevent any flight delays. This will also empower your staff to determine downtime for a particular aircraft. Your organization will improve its relationship with its customers, increase on time performance, and reduce any delay-related costs.

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Airlines harness AI to optimize flight routes, enhance passenger experiences, and streamline operations. From predictive maintenance of aircraft to dynamic pricing strategies, AI empowers airlines to operate more efficiently and safely.