시스템 다이내믹스를 활용한 상하이컨테이너운임지수 미주서안 노선의 예측에 관한 연구
A Study of Forecasting of US West Coast(USWC) Freight Index in SCFI Using System Dynamics
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Due to the recent political and economic situation, uncertainty in the shipping market is increasing, and various predictive studies are being conducted to resolve it. This study conducted predictions of US West Coast(USWC) Freight Index in Shanghai Containerized Freight Index(SCFI) using System Dynamics. In the study, variables were selected by synthesizing previous studies and in-depth interviews with experts. As for demand factors, import volume of three ports, including Los Angeles, Long Beach, and Oakland, corresponding to the western port of the USA, container volume of Shanghai, and bunker price were selected. And as supply factors, the ship’s space and Idling ship volumes were selected. The data used for the variable are monthly data from January 2016 to December 2020. MAPE analysis used in error analysis was used to verify the accuracy of the prediction model. As a result of verifying SCFI’s USWC route index prediction simulation, it was verified with a very reasonable prediction model with a training interval of 22.9%. As a result of predicting future SCFI’s USWC using the prediction model constructed in this study, it was found that it slightly increased to 3093.47 in 2022 and 3130.79 in 2023.
Abstract
Due to the recent political and economic situation, uncertainty in the shipping market is increasing, and various predictive studies are being conducted to resolve it. This study conducted predictions of US West Coast(USWC) Freight Index in Shanghai Containerized Freight Index(SCFI) using System Dynamics. In the study, variables were selected by synthesizing previous studies and in-depth interviews with experts. As for demand factors, import volume of three ports, including Los Angeles, Long Beach, and Oakland, corresponding to the western port of the USA, container volume of Shanghai, and bunker price were selected. And as supply factors, the ship’s space and Idling ship volumes were selected. The data used for the variable are monthly data from January 2016 to December 2020. MAPE analysis used in error analysis was used to verify the accuracy of the prediction model. As a result of verifying SCFI’s USWC route index prediction simulation, it was verified with a very reasonable prediction model with a training interval of 22.9%. As a result of predicting future SCFI’s USWC using the prediction model constructed in this study, it was found that it slightly increased to 3093.47 in 2022 and 3130.79 in 2023.
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