From Descriptive to Prescriptive Analytics: Turkish Airlines Case Study

Year 2023, Volume: 13 Issue: 1, 99 - 125, 30.06.2023

Musab Talha Akpınar , Kadir Hızıroğlu , Keziban Seçkin Codal

https://doi.org/10.31679/adamakademi.1232332

Abstract

Recent years, evolving technologies have increased importance of data analytics and have extended the potential of using data-driven for decision-making process in different sectors as it has also been shown in civil aviation. The aviation industry supports $2.7 trillion (3.5%) of the world’s GDP thus, it has always been seen to have an inherently strategic role. Propose of this study is an integrated model that combines descriptive analytics (multidimensional analytics) predictive analytics (data mining and more) and prescriptive analytics (MCDM and DEMATEL) in order to extract the critical factors for the improvement of airline baggage optimizations. The data has taken from Turkish Airlines which is one of the biggest 10 airlines in terms of the passenger number. Descriptive analytics results have set a precedent implication of multidimensional reports for service sector. In addition, rules that arise as outcomes of predictive analytics have really significant knowledge for marketing and planning department in civil aviation. Furthermore, they will help to solve some optimization problem in air transportation sector. Owing to prescriptive analytics, displayed results supported by the MCDM and DEMATEL methods. Therefore, all stages of the analytics have been shown step by step on the real-world data implementation.

Keywords

Decision Support Systems , Air Transportation , Civil Aviation , Analytics , Descriptive , Predictive , Prescriptive

Tanımlayıcı Analizden Öngörüsel Analize: THY Vaka Çalışması

Abstract

Son yıllarda gelişen teknolojiler, veri analitiğinin önemini artırmış ve sivil havacılıkta da görüldüğü gibi farklı sektörlerde karar verme süreçlerinde veri odaklı kullanım potansiyelini genişletmiştir. Havacılık endüstrisi, dünya GSYİH'sının 2,7 trilyon dolarını (%3,5) desteklemektedir, dolayısıyla her zaman doğası gereği stratejik bir role sahip olduğu görülmüştür. Bu çalışmanın önerisi, havayolu bagaj optimizasyonlarının iyileştirilmesi için kritik faktörleri çıkarmak amacıyla tanımlayıcı analitiği (çok boyutlu analitik), tahmine dayalı analitiği (veri madenciliği ve daha fazlası) ve normatif analitiği (MCDM ve DEMATEL) birleştiren entegre bir modeldir. Veriler, yolcu sayısı bakımından en büyük 10 havayolundan biri olan Türk Hava Yolları'ndan alınmıştır. Tanımlayıcı analitik sonuçları, hizmet sektörü için çok boyutlu raporların emsal teşkil etmesini sağlamıştır. Ayrıca öngörü analitiği sonucunda ortaya çıkan kurallar, sivil havacılıkta pazarlama ve planlama departmanı için gerçekten önemli bir bilgi birikimine sahiptir. Ayrıca, hava taşımacılığı sektöründeki bazı optimizasyon problemlerinin çözülmesine yardımcı olacaklardır. Kuralcı analitik sayesinde, MCDM ve DEMATEL yöntemleri tarafından desteklenen sonuçlar görüntülenir. Bu nedenle, analitiğin tüm aşamaları gerçek dünya veri uygulaması üzerinde adım adım gösterilmiştir.

Keywords

Analytics , Decision Support Systems , Air Transportation , Civil Aviation , Descriptive , Predictive , Prescriptive

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