Fremont, CA: In recent years, airline firms have faced greater uncertainty, public scrutiny, and increased rivalry and commoditization. Despite these hurdles, many believe the aviation sector is on the verge of embracing the fourth industrial revolution. Emerging technologies like artificial intelligence (AI), machine learning, and the Internet of Things (IoT) are transforming the aviation industry from the inside out.
Use Cases for the Aviation Industry
- Ticket pricing optimization
Travel ticket costs get determined by a variety of factors, including oil prices, flight distance, time of purchase, competition, seasonality, and others. Some of these criteria vary daily, implying that corporations must constantly adjust ticket rates to reflect these changes. Companies may use AI and machine learning to examine historical data and estimate demand based on a variety of factors. Furthermore, by implementing a more balanced airline booking system, companies can boost long-term sales income.
- Crew management
Airline crew managers must manage complicated staff networks that include pilots, flight attendants, engineers, and other personnel. Day-to-day crew management gets influenced by various criteria, including availability, credibility, certifications, and credentials. It might be difficult to reschedule any of the crew members. On the other hand, Airlines can streamline and partially automate the process by using an AI-based staff rostering system, lowering costs and mistakes while maximizing crew members' full potential.
- Customer service
Companies may use AI to optimize their operational and labor expenses at the same time. AI-powered systems can give information about upcoming flights, aid with check-in requests, and answer basic consumer questions.
- AI-based predictive maintenance
Aircraft maintenance is a difficult undertaking that may cost a lot if done poorly. Today's airline maintenance teams must cope with massive volumes of data generated by modern aircraft and the need to provide timely insights and deploy reliable prediction models. Companies can now identify probable maintenance faults on aircraft with more accuracy thanks to AI and machine learning. Combining AI with predictive maintenance analytics can result in a systematic approach to how and when aircraft maintenance should get performed. Centralized data platforms might provide better and more efficient data governance and more effective model lifecycle management.
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