The air cargo market is growing due to the spread of information technology (IT) products, the expansion of e-commerce, and high value-added products. Weather deterioration is one factor with a substantial impact on cargo transportation. If the arrival of cargo is delayed, supply chain delays occur. Because delays are directly linked to costs, companies need precise predictions of cargo transportation. This study develops a forecasting model to predict delay times and costs caused by the delayed arrival of cargo due to severe weather in the air cargo service environment. A seasonal autoregressive integrated moving average (SARIMA) model is developed and analyzed to address delay reductions in cargo transportation. Necessary data are identified and collected using time series data provided by Incheon International Airport Corporation, with an emphasis on monthly data on cargo throughput at Incheon International Airport from January 2009 to December 2016. The model makes forecasts for further analysis. The model stands to provide decision makers with strategic and sustainable insights for cargo transportation planning and other similar applications.
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