Development of a Cost Forecasting Model for Air Cargo Service Delay Due to Low Visibility

Abstract

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.

Research on Freight Development of Guangdong Province Based on Grey Theory Model

“…In recent years, logistics development prediction has become a hot issue for scholars. e methods commonly used by scholars include exponential smoothing [5][6][7], linear model [8], BP neural network method [5][6][7][8][9], multiple regression analysis [5,9], seasonal autoregressive model [10][11][12][13], discrete wavelet technology [14], vector autoregressive method [15], and Markov chain theory [16], which are widely used in the prediction of logistics and freight development. In addition to conventional prediction methods, scholars also innovate prediction methods, such as a genetic algorithm and backpropagation (GA-BP) prediction model (optimized backpropagation neural network model using genetic algorithm), which are used to predict freight volume demand with small error [17].…”

Design of An Integration Model for Air Cargo Transportation Network Design and Flight Route Selection

“…Constraint (5) indicates that the distance between a non-hub node and its associated hub shall be within a reasonable distance range. Constraints (6) and 7describe one-time transshipment transportation provided that the origin and destination node are associated with the same hub node. Constraints (8) and (9) describe the corresponding relationship between decision variables.…”

“…For example, Shunfeng (one major logistics company in China) has not only purchased more than 40 airplanes dedicated to air cargo transportation, but also built its own air cargo airport in Hubei province acting as its international logistics hub. The development of air cargo transportation is an inevitable trend along with regional and national economic development [6].…”

The geography of logistics facilities relative to airports: Taxonomy of literature and research agenda

“…The world is characterised by the increasing volumes and flow of goods (Verhetsel et al 2015), which are largely driven by the growth of e-commerce activities (Kim & Lee 2019;Yang et al 2022). The fundamental consideration not only pertains to the nature, origin and destination of freight but also relates to how freight moves across different geographical scales, bringing to the fore the concept of logistics (Hesse & Rodrigue 2004).…”

“…International Air Transport Association (n.d.) reports that 35% of the value of global trade is transported by air, albeit it covers a volume of less than 1%. The air cargo market is growing because of the increasing e-commerce, and also possesses benefits in terms of the timeliness of deliveries (Kim & Lee 2019) compared to other modes of transport. Furthermore, high-value, light-weight products are typically shipped by air transport, whose cost has declined over time (Yuan, Low & Tang 2010).…”