Phygital: How Cognitive Technology Drives Smart Airline Operations
Global air traffic is set to grow to nearly 10 billion travelers per year by 2050, according to the Waypoint 2050 report of the Air Transport Action Group. While the forecast from the current count of nearly four billion travelers is extremely encouraging as we recover from the pandemic, it is a troubling foreshadowing of likely congestion at airports, leading to delay or cancellation of flights. The airline industry should take the opportunity now to augment physical facilities with digital technology to chart a sustainable growth trajectory, in an effort to mitigate growing pains of the future.
Artificial Intelligence (AI) and cognitive technologies provide tailwinds to flight operations and workflow management by extracting value from unstructured data; detecting motion, anomalies, and patterns in video images; and enabling autonomous capabilities. While conversational assistance in natural language is a widely adopted AI use case in travel and hospitality, it now embodies technical and business processes.
Cameras equipped with computer vision, IoT sensors, biometrics technology, and self-service applications provide a rich repository of visual, textual, and contextual data. These datasets provide insights into passenger demographics, behavior, intent, and purchase patterns, as well as diverse operational activities. Airlines can now utilize cloud-hosted, AI-driven analytical solutions that leverage data for seamless convergence of physical and digital systems. A converged ecosystem can improve landside and airside operations, covering both above and below the wing services, as well as ancillary and ground support services.
Automates landside operations
Since June 2018, IATA Resolution 753 has mandated tracking of each baggage item at four critical points during the customer journey: (1) passenger handover to airline, (2) loading to the aircraft, (3) delivery to the transfer area, and (4) return to the passenger – and such tracking data should be shared with interline partners as well. AI-powered luggage handling systems automate tracking and communication. These systems share real-time baggage status with stakeholders, including passengers. In addition, computer vision-powered smart cameras detect unsafe and prohibited baggage items accurately, which enhances the efficiency of baggage inspection.
Smooth flow of baggage and passengers is the barometer of terminal operations. Face- and iris recognition technology allows airlines and ground handling agents to deploy self-boarding gates. Biometrics enable contactless identification of passengers at airport touchpoints and automate scrutiny at security checkpoints. The immutable identity authentication accelerates passenger screening, passport verification and immigration clearance. In 2018, Miami International Airport implemented facial recognition screening for inbound travelers, which facilitated screening of ~10 passengers per minute, and significantly decongesting overcrowded arrivals facilities.
AI systems with visual sensors are the ‘eyes on the ground’ – monitoring everything from passengers and employees to cargo and concourses. Smart surveillance from the drop-off curb to the aircraft provides critical operational inputs, such as the volume of originating and terminating travelers, and dwell time at screening stations. Real-time data empowers managers to take timely decisions related to addressing curbside requirements, managing passenger throughput, and transforming the experience for passengers as well as airline crew and airport staff. This also enables airport operators to identify chokepoints in the passenger terminal flow and quickly work to remove them.
Tracking of the volume and movement of travelers optimizes queue management and boosts productivity of resources. However, AI-driven efficiency transcends seamless flow during peak travel season. Airlines using self-service solutions and automated kiosks to streamline traveler facilitation services and baggage handling can integrate the data with core service databases and airport management systems to reduce overheads and optimize arrival / departure operations. Further, machine learning models and analytical solutions draw on IoT sensor data and video footage to predict peak footfall and issues during the period, which can be used to enhance self-service processes, contactless mechanisms and in-flight interfaces.
Streamlines airside services
AI platforms enhance the in-flight experience by mitigating technical and logistical issues that disrupt travel. Algorithms synthesize real-time data for clockwork accuracy in coordination of services, such as in-flight catering, ground support equipment handling, handling passengers with disabilities, water supply, and air conditioning. Cloud portals assimilate sensor data spanning diverse parameters – from air quality in the cabin to food supplies, which helps accelerate aircraft turnaround times and improve safety.
Analytical solutions correlate real-time data feeds with aircraft-specific standardized metrics and historical data to detect issues and notify anomalies, including safety issues and delay in ground servicing activities. Further, AI systems augment technical support by providing recommendations that enable maintenance and engineering teams to troubleshoot and diagnose events for managing incidents proactively and refining emergency planning.
Unplanned maintenance causes flight delays and cancellations, which increases overheads, including compensation to travelers. Carriers deploy predictive maintenance applications to significantly reduce equipment failure. Real-time data from IoT-enabled aircraft machinery and onboard health monitoring sensors offer insights into the current technical condition, pinpoint malfunction, and flag potential failure. It empowers maintenance crew and field technicians to undertake physical inspections faster and more effectively. Notably, predictive maintenance improves aircraft reliability. Delta Air Lines, for example, previously partnered with Airbus to implement a predictive fleet maintenance program that reduced maintenance-related flight cancelations from ~ 5,600 to only 55 within an eight-year period.
The scheduling teams within an airline are responsible for seamless operations of thousands of daily domestic and international flights, and should factor in independent, dependent, and mutually exclusive variables for routing and scheduling purposes. For instance, the experience of pilots and flight attendants could be mapped with the flight route and aircraft model – as some airports in Central America require additional airport-specific training qualifications in order for pilots to perform landings. As expected, all crew schedules must adhere to complex labor (union) agreements and government regulations – which vary between workgroups.
This summer has been a very challenging one for airlines everywhere, as they struggle to operate with limited staff of their own and operate at major airports where local staff are also severely limited which cause further costly disruptions to airlines.
AI models optimize crew and schedule management by taking into account operational constraints, regulations, resource availability, maintenance schedules, and costs. Significantly, machine intelligence addresses qualitative issues such as jetlag and fatigue. Smart models help mitigate health risks due to long-haul flights or change in time zones and integrates datapoints into the rostering system. Most important, AI systems optimize aviation fuel consumption for route planning. Maximizing fuel efficiency is a business imperative as well as an ethical practice.
Cognitive systems provide a smart interface between the aircraft, airfield and landside operations. Advanced data science enhances operations, while providing a superior experience for travelers and operators.
Article written by Infosys
Infosys is an associate sponsor at the World Aviation Festival 2022 on 5-6 Oct at RAI Amsterdam, where we will have our booth #12.562 showcasing innovative solutions that solves today’s business problems powering technology and moderate a rich roundtable with top CXOs on ‘‘The convergence of Phygital mechanism to optimize operations above and below the wings, landside and airside. – via usage of camera, sensors , AI etc.’