An analysis of African airlines efficiency with two-stage TOPSIS and neural networks

• This paper uses TOPSIS to assess the efficiency of African airlines.
• TOPSIS is used in a two-stage approach with neural networks to predict performance.
• Results reveal that network size is the most important efficiency driver.
• Findings also indicate the existence of a learning curve to some extent.

This paper presents an efficiency assessment of African airlines, using the TOPSIS – Technique for Order Preference by Similarity to the Ideal Solution. TOPSIS is a multi-criteria decision making technique, which similar to DEA (Data Envelopment Analysis), ranks a finite set of units based on the minimisation of distance from an ideal point, and the maximisation of distance from an anti-ideal point. In this research, TOPSIS is used first in a two-stage approach, in order to assess the relative efficiency of African airlines using the most frequent indicators adopted by the literature on airlines. During the second stage, neural networks are combined with TOPSIS results, as part of an attempt to produce a model for airline performance which has effective predictive ability. The results reveal that network size-related variables – economies of scope, are the most important variables for explaining levels of efficiency in the African airline industry, although the impact of fleet mix and public ownership cannot be neglected.