Improving the operational performance efficiency index of an airport by managing and reducing controllable delays: A case study of Delhi International Airport Limited, India

Sumangal Narendra

Journal of Airport Management, 2016, vol. 10, issue 4, 359-380

Abstract: This paper presents an approach to the internal benchmarking of the performance of an airport in terms of total aircraft movement, and addresses airport capacity constraints in light of growing traffic and resulting congestion, causing delays that adversely affect the operational performance in terms of total aircraft movement through an airport. Considering the capital-intensive nature of the expansion of capacity and the usual time and cost overruns, the problems and difficulties in land acquisition and the time-consuming requirements of obtaining social and environmental clearances, the paper’s solution provides an option to improve the operational performance of an airport within its existing capacity by managing and reducing controllable delays. A model is presented to develop the performance efficiency index (PI), in terms of aircraft movement through the existing capacity of an airport every 24-hours, which is treated as one decision-making unit (DMU). This paper is a case study of Indira Gandhi International (IGI), now known as the Delhi International Airport (DIAL), in New Delhi, India. The model uses the stylised categories of delays — as per the International Air Transport Association (IATA) codes — as inputs, and the total aircraft movement as the throughput, and presents the efficiency frontier. The model is based on simple data envelopment analysis and linear programming techniques combined with second-stage regression analysis in order to estimate the effect of managing of types of delays on the PI. The model explains 76 per cent of variations in the throughput. The paper finds that the reactionary delays and the average delay at the gate affect the turnaround time of an aircraft and consequently the throughput of the DIAL.

Keywords: airport capacity; congestion; delays; efficiency index; internal benchmarking (search for similar items in EconPapers)
JEL-codes: M1 M10 R4 R40 (search for similar items in EconPapers)
Date: 2016
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