Covid-19 Pandemisi Havayollarının Filo Kullanımı Nasıl Etkiledi, Etkileyecek?

Abstract

Küresel çapta krize sebep olan COVID-19 virüsü havayolu kullanımını olumsuz şekilde etkilemiştir. Havayolları, sınırların kapatılmasıyla uçuş yapamaz hale gelmiştir. Kriz sebebiyle yolculuk talebi azalmış, uçuş rotaları kısalmış, uçuş sıklıkları azalmış, kullanılan uçakların ortalama ağırlıkları düşmüştür. Havayolları düşen talep karşısında kullanmadıkları uçaklarını Avrupa’nın dört bir yanında farklı havaalanlarına park etmişlerdir. Her havayolu filo yönetimi konusunda bazı kararlar almış, farklı tercihlerde bulunmuşlardır. İçinde bulunduğumuz pandemi koşulları her havayolunu farklı düzeyde etkilediği düşünülmektedir. Eurocontrol verileri doğrultusunda havayollarının filolarının kullanımlarının incelendiği bu çalışmada, Avrupa hava sahasında en yoğun uçuş trafiği yaratan ve en büyük filoya sahip 10 havayolunun filo kullanımları zaman serisi analizi yöntemi kullanarak incelenmektedir. Çalışmada amaç örneklem içerisine alınan havayollarının filo kullanımlarının farklı zamanlarda nasıl etkilendiğini gözlemlemek ve bu gözlem doğrultusunda bahsedilen etkilerin nedenleri doğrultusunda tahmin yapmaktır. Böylece havayollarının filo yapılarını tekrar gözden geçirme olanağı bulabilecekleri düşünülmektedir. Analiz neticesinde filo kullanımında en büyük dalgalanmanın Vueling ve Easyjet’te, diğer havayollarına nispeten en az dalgalanmanın Türk Hava Yolları (THY) ve KLM’de olduğu gözlemlenmiştir. Ortaya çıkan sonuçlar neticesinde havayollarına farklı öneriler sunulmuştur. Havayolları politik bağlarını gözden geçirmeli, hükümetlerle daha yakın ilişkiler kurmalıdırlar. Filo yapılarını gözden geçirecek havayolları yeni uçak siparişlerinde ortaklık yapılarına uygun siparişler vermelidirler. Uçuş ağlarıyla birlikte iş modellerini değiştirecek havayolları filo yapılarını, bu yeni modele göre kurmalıdır.

Keywords

• Havayolu Filo Yönetimi
• Covid-19
• Pandemi
• Zaman Serisi
• Yapısal Kırılma

How Has The Covid-19 Pandemic Affected Airline Fleet Utilization?

Abstract

The COVID-19 virus, which caused a global crisis, has negatively affected the use of airlines since the beginning of the pandemic. Airlines have become unable to fly with the closure of borders. Due to the crisis, travel demand has decreased, flight routes have shortened, flight frequencies have decreased, and the average weight of the aircraft used has decreased. In the face of falling demand, airlines have parked their aircraft, which they do not use for supply, at different airports all over Europe. Each airline has taken some decisions about fleet management and made different choices. It is thought that airlines are affected differently due to quarantines and closures under the current pandemic conditions. In the study, which examines the use of fleets of airlines between January 1, 2020 and June 26, 2021, in line with Eurocontrol data, the fleet usages of 10 airlines that create the heaviest flight traffic in the European Airspace and have the largest fleet are examined using the time series analysis method. The aim of the study is to observe how the fleet usage of the airlines included in the sample is affected at different times and to make predictions in line with the reasons for the mentioned effects in line with this observation. Thus, airlines will have the opportunity to reconsider their fleet structures. As a result of the analysis, it was observed that the highest volatility in fleet usage was in Vueling and Easyjet, and the least volatility in Turkish Airlines and KLM compared to other airlines. As a result of the results, different suggestions were presented to the airlines. Airlines should review their political ties and establish closer relations with governments. Airlines that will review their fleet structures should place orders in accordance with their partnership structure in new aircraft orders. Airlines that will change their business models with their flight networks should establish their fleet structures according to this new model.

Keywords

• Airline Fleet Management
• COVID-19
• Pandemic
• Time Series
• Structrual Break

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