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.
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