Workforce Planning in Aviation: The implementation of Artificial Intelligence in Recruitment

Abstract

The International Civil Aviation Organization (ICAO) is responsible for international standards and recommended practices (SARPs) to unify aviation globally through regulations and practices with safety, security, efficiency, and green policy of the highest interest. One component of ICAO’s standards is the documentation required to be on board an aircraft, as stated in Article 29, which mandates crew members to carry their “appropriate licenses,” evidence for their competence, and required hiring documents. However, barriers persist to standardizing electronic personnel licensing. ICAO is developing the required technical specifications to implement and verify personnel licenses globally. To support this transformation, IATA (International Air Transport Association) encourages airlines, companies, and aviation organizations to innovate modern solutions, using artificial intelligence and machine learning (AI/ML) to optimize operational goals. A starting point to support this conversion to electronic personnel licensing is during the employee recruitment phase. This research project focuses on implementing a potential pilot recruitment EPL system on digital platforms using visual and graphic design tools with UI/UX considerations for airlines, companies, and aviation organizations. The project followed a SWOT analysis, Safety-Risk Assessment, Benefit-Cost analysis, Sustainability Assessment, and Management of Change, and presented a Functioning digital prototype. The emerging selected technologies are AI/ML following the EASA AI classification Roadmap 2.0 (2023). The application benefit is the offered organizational culture adaptability and Key Performance Indicators selection following a Lean Six Sigma approach. Cybersecurity is granted following multiple layers design - user approach. Finally, the project takes into consideration accessible design features.

Keywords: Workforce Planning, Human Systems Integration, Artificial Intelligence, Machine Learning, Deep Learning, Lean – 6 Sigma.

DOI: 10.54941/ahfe1004591