Aviation Decision-Making Requires Advanced Technologies

 

For aviation to be successful, many complex airport operational tasks must be accomplished efficiently and on time. Given today’s staff shortages, this is even more critical. Equally important are sustainable airport operations to achieve overall aviation sustainability.

The sheer number of tasks and inherent market volatility make this goal extremely challenging. The right resources must be in the right place at the right time. Real-time operational decision-making is paramount when there are unexpected disruptions. The most advanced technologies are needed to make the right decisions while concurrently addressing “what-if” scenarios.

Today, ubiquitous airport systems, the Internet of Things (IoT) and much-increased computing power enable airport processes to be digitalized in detail. Going forward, even more data will become available for analysis and utilization. According to Moore’s law, computing power will continue to increase exponentially over the coming years, while computing costs will decrease significantly.

From an Operations Research (OR) standpoint, this facilitates the simultaneous use of two algorithmic principles, each highly efficient when applied alone. This powerful parallel approach is called “hybrid AI”. It combines the benefits of “data-driven” (AI) and Machine Learning (ML) with “know-how-driven” algorithms such as Mathematical Optimization and Fuzzy Logic.

Hybrid AI supports airline, airport and ground handling operations across broad areas, above and below the wing, for example, in aircraft maintenance or cargo. Hybrid AI allows for better resource management of staff, ground support equipment, bays, and terminal resources, from strategic planning to the day of operations. Furthermore, Hybrid AI provides powerful decision support for managing disruptions.

Optimizing scenario planning and predictive disruption management

The past few years have been game-changing for aviation. We have seen the accelerated adoption of technology. The pandemic also provided a valuable learning lesson. Planning for the unexpected in aviation is more crucial than ever. Balancing “typical” day of operation needs with the ad-hoc resource demand created by unexpected disruptions requires sophisticated planning and decision-making. Gut feelings and repeating “yesterday’s” decisions are no longer suffice.

By applying data-driven AI and the underlying predictive modelling, planners use real-world data to forecast the right staff and ground support equipment demand. They correctly predict expected volumes, passengers, PRM, baggage, or cargo. Furthermore, disruption probabilities are considered.

This data enables them to effectively prioritize staff and equipment resources and physical assets to mitigate disruption impacts. Long-term, mid-term and short-term resource planning scenarios can be developed to enable more stability regarding potential operational changes using what-if analyses. All airport operations benefit from this optimized planning.

For example, if a weather-related disruption, fog, or ice storm occurs, hybrid AI-driven software will support effective staff scheduling by automatically learning from the past and considering previous, similar scenarios. Additionally, rule-based specifications (i.e., qualifications, preferences, SLAs) are applied. IoT helps by providing the real-time context, for example, in monitoring ground support equipment locations. The result is heightened situational awareness and automatic prioritization of tasks and resources. In this way, sophisticated decision-making support tools help planners minimize the effects of such disruptions and mitigate their impacts on flight schedules, operational costs, and passenger experience.

Optimizing cargo operations

The same predictive modelling is also applicable to cargo airline operations, addressing supply chain disruptions and facilitating the best decisions. With optimization software, integrating data from multiple sources (i.e., flight, truck, cargo, staff, GSE locations) and applying Hybrid AI, cargo airlines can identify the best plans and real-time tactics to maximize efficiency, customers’ SLAs, and sustainability simultaneously.

Optimizing workforce management

Successfully managing the workforce must consider such criteria as demand requirements, workplace regulatory mandates, individuals’ qualifications and preferences, and schedules. Integrating digitalization into workforce management, facilitated by optimization software incorporating sophisticated technologies, enables planners to better align demand fluctuations and operational needs with staff capabilities, scheduling preferences, and increased productivity goals.

Optimizing line maintenance

Leveraging aircraft data, hybrid AI and the IoT is helping airlines achieve enhanced line maintenance operations. Historical data and Machine Learning algorithms enable sound preventive line maintenance decisions, informing a LM Technician or Engineer proactively where to replace parts or make preventive checks to deter malfunctions. Additionally, hybrid AI helps to forecast the duration of various checks and replacements in LM for better resource schedules. In turn, this reduces flight delays, costs associated with unplanned overtime, expedited shipping costs for parts, and potentially stressful, rushed and, subsequently, inferior quality.

Optimizing sustainability

New aviation sustainability goals and requirements regularly occur within the industry and across regional governments. While sustainable airline fuels are not yet available in sufficient quantities, airport operations can significantly reduce an airport’s carbon emissions. For example, today, mixed GSE fleets are already in use. The numbers of electric and hydrogen GSE are rapidly increasing. Using advanced AI and predictive analytics, airlines, airports and ground handlers can optimize their planning of such mixed GS fleets to reduce fuel consumption and related CO₂ emissions, while guaranteeing operational stability simultaneously. Reducing emissions by better planning applies to driver-based vehicles similarly as autonomous GSE.

The questions to ask today

The aviation industry is at a critical crossroads. Business as usual won’t suffice. We all learned this during the crisis. Software solutions must provide advanced technologies and a mature aviation model. They are crucial to supporting optimum aviation operations. The industry must adopt new solutions that help companies become more proactive in addressing the wide range of situations that disrupt static plans, moving away from pure reactive handling of such cases.

It is more urgent than ever that aviation companies find answers to critical questions such as:

• How can we fully optimize, increase productivity and plan resources to meet our operational promises?
• How can we build greater employee satisfaction?
• What will be the keys in 2023 to driving maximum efficiencies and cost reductions?
• What measures should we take to protect our earth now and in the future?

Advanced technologies will be central in answering all these questions and will be the essential building blocks to the aviation industry’s successful go-forward strategy.

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By Ekkehart Vetter, CTO at INFORM Aviation

Image by HT Ganzo via Getty Images