Trends and challenges of forecasting in the airline industry research

Dewi Oki Anita Candra (), Masruroh Nur Aini () and Wibowo Budhi Sholeh ()
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Dewi Oki Anita Candra: Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia
Masruroh Nur Aini: Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia
Wibowo Budhi Sholeh: Mechanical and Industrial Engineering Department, Universitas Gadjah Mada, Grafika Street No. 2, Sinduadi, Mlati District, Sleman Regency, Special Region of Yogyakarta, Indonesia

Engineering Management in Production and Services, 2025, vol. 17, issue 2, 23-36

Abstract: This study aims to comprehensively review aviation forecasting research by identifying its bibliometric trends, evolving research areas, and thematic developments. It focuses on understanding the aviation industry’s research gaps, highlighting emerging trends, and offering insights into future forecasting innovations. A systematic literature review in the Scopus database used Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and bibliometric analysis. It identified key patterns, influential publications, and emerging topics. A science mapping analysis was executed to pinpoint research trends in airline forecasting using Biblioshiny to visualise the network analysis and thematic evolution keywords mapping. The study categorised research trends and identified underexplored areas for future investigation. The findings reveal significant shifts in aviation forecasting research, with three distinct phases of publication growth and a surge in output from 2016 onwards. Passenger demand forecasting remains the most researched topic, though its growth has stabilised. Emerging issues such as customer behaviour, financial forecasting, and dynamic pricing have gained prominence, driven by advancements in machine learning and big data analytics. The study also highlights transitioning from traditional statistical methods to more advanced predictive techniques, emphasising real-time decision-making and operational efficiency. Established research areas, such as air cargo forecasting and fleet scheduling, have become more standardised, reducing the need for further innovation.

Keywords: airline; forecasting; bibliometric analysis; PRISMA (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecoman:v:17:y:2025:i:2:p:23-36:n:1002

DOI: 10.2478/emj-2025-0010

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