A Data Envelopment Analysis Approach to Measure and Compare Efficiency in Airline Traffic

Details

Supervisors

Catanzaro, Daniele

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

The airline industry has attracted significant attention at Brussels International Airport due to its proliferation, connectivity, and many ranges of airlines. Understanding the behaviour and performance of airlines in this market is important for decision makers and other stakeholders in this industry. Our study aims to assess the efficiency of airlines utilizing Data Envelopment Analysis (DEA) and provide information regarding the best practices and areas for improvement, which will contribute to the overall success of the airline industry at Brussels Airport. Our study employs a DEA model to assess the effectiveness of seven different airlines that are operating at Brussels International Airport. DEA calculates the degree to which airlines can transform inputs into outputs, taking into account the diversity of their operations, it compares apples to pears., our investigation attempts to rank and compare airlines in terms of these variables. The results have significant value in providing guidance that is useful in improving operations, increasing competitiveness, and improving efficiency in the aviation industry. How efficient is an airline company? This natural question hides several non trivial aspects. For example, just to cite a few, what efficiency is in this specific context? How to measure efficiency? How to compare efficiency? Our efficiency scores are derived using linear programming methods. Our investigation employs the Mosel language for optimization and the Xpress software for execution, this software guarantees accurate results. We have assessed seven airlines. Four of them obtained the maximal efficiency score which means they perform at their ideal size and three of them achieved a score with many rooms of improvement especially for one in particular. We have provided recommendations and insights for the decision makers to improve their score to avoid any risk and keep their competitiveness. The DEA model is a beneficial tool for assessing efficiency, but it has many limits. These include the selection of input and output variables that are subject to the analyst's discretion, the potential for inaccuracies in measurement, the data reliability, the static nature of the analysis, the exclusion of external factors, the inability to differentiate scale and allocative efficiency, and the need for integration with other analytical approaches. Future research should concentrate on adding external factors, dynamic components, and comparative analyses to the DEA method in different situations and disciplines.