INDUSTRY WATCH
Airport management
Natural Language Text Generation would dispatch management summaries about most problematic airfield areas as well as recommendations for improvement to the airport management and their stakeholders to better explain and document how to optimise capital and expenses.
Launching an AI project
Launching an AI and robotisation process is a major transformation and airport management should consider a number of points.
First they should engage field workers early to include their feedback in the development process, evaluate skills gaps and gain their trust to work alongside machines.
Second they should start with a pilot project to align expectations from all stakeholders, learn from the pilot before wider implementation and build business cases across the airport costs and profit pools.
machine trainers to fine tune their outcomes either on location or overseas, translators able to explain how the decisions are made and machine surgeons when the robots are damaged
Field technicians performing repetitive jobs will see their focus shift on most complex cases not solved by machines where generic intelligence to think out of the box solutions might be required.
This is an opportunity to upskill their knowledge and become augmented technicians which starts to be adopted in aviation maintenance.
Machine Learning and Robotics Processing Automation are still in their infancy in the airside maintenance industry.
Use cases success should be judged based on their ability to highly correlate fault prediction to root causes, decrease mean time to repair, accelerate human acceptation and adoption rate and, ultimately, improving overall airfield availability and uptime and better end to end view of the airport arrival, departure throughput.
An AI, ML project will redesign airside operations processes by shifting work from time-based maintenance to predictive issue maintenance whilst implementing automated repair systems workflows.
To drive an AI transformation, new jobs will have to be considered such as robots programmers, expert
Implementing Machine Learning in airfield maintenance is not only an opportunity to decrease maintenance costs and improve airfield availability but also a possible new revenue stream by productising those services and selling them to airports unable or unwilling to develop a machine driven maintenance team. p
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