Abstract:
Turnaround Time (TAT) is by far one of the most important activities that take place at the Airports, because of its impact on costs and revenues. Previous studies on Turnaround time have examined the design of Aircrafts with emphasis on reducing the time spent on enplaning and deplaning, defrosting or deicing the aircraft after landing and before takeoff again; simulation of turnaround process all in a bid to reduce the time spent in the process and increase efficiency. In spite of the significance of these previous studies, the uncertainty of the average turnaround time of an aircraft still remains a challenge to stakeholders who are involved in the day to day operational activities at the Airport. This uncertainty affects the airlines, customers and the airport authorities in terms of delays, loss of revenue, and customer aggravation. These challenges may be avoided, if the turnaround time is properly predicted.
The study, therefore, attempted to establish the relationship between the Daily Average Aircraft Turnaround time and time (each day of the week) to be able to predict future TAT. Data of Daily Aircraft Turnaround time (TAT) in Nnamdi Azikiwe International Airport for Domestic and Foreign flights for 2017 and 2018 were collected and subjected to various time series analytical methods and quantitative forecasting techniques. Records of Daily Aircraft TAT for Domestic and Foreign flights were collected from Nigeria Airspace Management authority and these averages were computed using simple averages. The Daily Average TAT was smoothened using R statistical package before a trend analysis was carried out on both sets of data. Based on the trend analysis with the use of Autoregressive Integrated Moving Average (ARIMA) Model, J Multi Statistical package was used in generating the optimal model of ARIMA (2,1,3) for Domestic flights given as;
𝑿𝒕= -1.1884Xt-1 – 0.9671 Xt-2 – 0.2609𝜀𝑡−1 + 0.1083𝜀𝑡−2 + 0.9303𝜀𝑡−3 and ARIMA (3,1,3) for Foreign flights given as;
𝑿𝒕= -0.7319Xt-1 + 0.1111 Xt-2 + 0.1492𝑋𝑡−1 + 0.2511𝜀𝑡−1 + 0.7148𝜀𝑡−2 + 0.0127𝜀𝑡−3
The research therefore showed that a model can be generated based on historical data that can be used for forecasting Aircraft TAT to avoid delays and increase productivity at the Airports.
