A predictive model to improve the punctuality of wide-body aircraft's maintenance and fleet reliability

Abstract

This research was carried with the aim of analyzing Martinair M&E impact on the network performance and providing an efficient tool to predict delays and the On-Time Performance (OTP). The methodology undertaken in this research is based on conventional analytical methods to identify the ‘real’ delay root causes and their relative importance. The growth of delays and unplanned ground times are analysed in different ways, not only using rates and duration but also per month, per quarter, per year, per station and per Maintenance Delay Categorization Groups (MDCG). Fishbone diagrams are drawn to completely identify and understand the root causes. Statistical analysis (Binomial Logistic Regression and GLM-ANOVA) is applied to investigate the significance of the maintenance factors and their interactions and to build a regression equation which allows the development of the predictive model. The predictive model gives valid OTP results and enables managers and engineers to take preventive measures in order to enhance punctuality.