Identifying the key influencing factors for the growth of air cargo demand

Amani Natheesha Karunathilake · Anuja Fernando

12 citations0/0 files storedJournal of Global Operations and Strategic Sourcing

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Aviation Industry Analysis and Trends Transport and Logistics Innovations Logistics and Transportation Systems

Abstract

Purpose Air transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain. Design/methodology/approach The data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth. Findings According to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables. Research limitations/implications The factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results. Originality/value Previous studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

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References

2008Decision making with the analytic hierarchy process 2018Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach 2019Forecasting air passenger demand with a new hybrid ensemble approach 2002A model of air cargo liberalization: passenger vs. all-cargo carriers 2019Forecast to grow: Aviation demand forecasting in an era of demand uncertainty and optimism bias 2019Air transport networks of global integrators in the more liberalized Asian air cargo industry 2009Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation 2013Simultaneous Modeling of Passenger and Cargo Demand at an Airport

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