• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.25-39
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• 2023

As the scope of supply chains expands globally, unpredictable risks continue to arise. The occurrence of these supply chain risks affects port cargo throughput and hinders port operation. In order to examine the impact of global supply chain risks on port container throughput, this study conducted an empirical analysis on the impact of variables such as the Global Supply Chain Pressure Index (GSCPI), Shanghai Container Freight Index (SCFI), Industrial Production Index, and Retail Sales Index on port traffic using the vector autoregressive(VAR) model. As a result of the analysis, the rise in GSCPI causes a short-term decrease in the throughput of Busan Port, but after a certain point, it acts as a factor increasing the throughput and affects it in the form of a wave. In addition, the industrial production index and the retail sales index were found to have no statistically significant effect on the throughput of Busan Port. In the case of SCFI, the effect was almost similar to that of GSCPI. The results of this study reveal how risks affect port cargo throughput in a situation where supply chain risks are gradually increasing, providing many implications for establishing port operation policies for future supply chain risks.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.25-37
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• 2016

The economy of South Korea has experienced two financial crises: the 1997 Asian financial crisis and the 2008 global financial crisis. These crises had a significant impact on the nation's macro-economic indicators. Furthermore, they had a profound influence on container traffic in container ports in Busan, which is the largest port in South Korea in terms of TEUs handled. However, the impact of the Asian financial crisis on container throughput is not clear. In this study, we assume that the two financial crises are independent and different, and then analyze how each of them impacted container throughput in Busan ports. To perform this analysis, we use an intervention model that is a special type of ARIMA model with input series. Intervention models can be used to model and forecast a response series and to analyze the impact of an intervention or event on the series. This study focuses on the latter case, and our results show that the impacts of the financial crises vary considerably.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.269-282
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• 2009

This study investigates the difference of behavioral patterns between the import container volume of all ports and that of Gwangyang port in Korea. All series span the period January 1999 to December 2008. I first test whether the series are stationary or not. I can reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. I hitherto make use of variance decompositions and impulse response functions, both of which have now been widely used to examine how much movement in one variable can be explained by innovations in different variables and how rapidly these fluctuations in one variable can be transmitted to another. The variance decompositions for the import container volume show that the proportions of the forecast error variance of import container volumes explained by themselves are 30 and 26 per cent after 12 months, respectively. As a result, innovations in exchange rate and business activity explain 70 and 74 per cent of the variance in the import container volume. All in all, innovation accounting indicates that import container volumes are not exogenous with respect to exchange rate and business activity. The impulse responses indicate that container volumes decrease sharply to the shocks in exchange rate and decay very slowly to its pre-shock level, while container volumes respond positively to the shocks in the business activity and disappear very slowly, showing that the shocks last very long. Furthermore Gwangyang port is more sensitive to the change of the exchange rate and the industrial production than all ports.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1995.10a
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• pp.38-38
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• 1995

국가의 충체적 활성과 경쟁력 확보는 정부의 적절한 정책수행에 의해서만 가능하고 특히, 항만, 공항, 도로 철도 등 사회간접자본의 확충은 민간기업으로서는 할 수 없는 정부 고유의 역할이다. 정부는 여러 전문기관을 통해서 이러한 사업을 위한 연구를 수행하여 왔다. 1980년도 이후 급격히 증대되어온 해운물동량과 특히 컨테이너 물동량의 증대에 대비하면서 장내의 시설수요를 예측하고 현실에 닥치는 물류적체 해소를 위하여 부산항 3단계 공사등 긴급한 시설을 확충하여 왔다. (중략)

• Journal of Korea Port Economic Association
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• v.28 no.3
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• pp.111-125
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• 2012

This study investigates the behavioral characteristic difference of the container volumes of three ports-Gwangyang, Incheon, and Pyeongtaek-Dangjin. All series span the period January 2003 to December 2011. I first test whether the series are stationary or not. I can reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. I hitherto make use of error-correction model and find that Gwangyang port is the slowest in adjusting the short-run disequilibrium, whereas the adjustment speed of Incheon is much faster than that of Gwangyang. The impulse response functions indicate that container volumes increase only a little to the negative shocks in exchange rate, while they respond positively to the shocks in the business activity in a great magnitude and decay very slowly to its pre-shock level. meaning that the shocks last very long. The accumulative response to the exchange rate increase of 20 won per dollar and the 5 point industrial production increase is the smallest in Gwangyang, no more than a half of that of two ports. The intervention-ARIMA models also forecast that Gwangyang port will have much lower growth rate than Incheon and Pyeongtaek-Dangjin port in trading volumes.

• The Journal of Bigdata
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• v.6 no.2
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• pp.85-98
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• 2021

The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.

• Journal of Korea Port Economic Association
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• v.24 no.3
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• pp.57-77
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• 2008

The aim of this paper is to make projection of the demand for export loaded container throughput originating at Incheon port in Korea over the period in question. Systematic analysis is used as a forecasting method instead of quantitative analysis. First of all, the method explores coincident indicators which may reflect the square measure of neighboring industrial complexes which originate/destinate general cargo in export traffic trends. It is noted that in terms of the export loaded container throughput, per unit production scale is intermediated transforming from square measure of production facilities to freight weight in Korea. Consequently, the future progress of the volume can be anticipated relying on the development schemes for developing square measure out of the total square of the industrial complexes. Thus, moving-into percentage of the industrial complexes, percentage of business categories, percentage of capacity and percentage of passing through via Incheon port are adopted and the future traffic demand is projected taking advantage of them.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.165-185
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• 2011

Transshipment traffic has significant meanings because it gives positive effects on increasing the container handling volumes in seaports, and revitalizes the regional and national development. Korean container port's transshipment traffic volumes, however, will slowly decrease due to the direct ships' calling into Chinese ports, which recently has a huge development plan. There are a lot of stress on forecasting the transshipment traffic volumes because the Korean container port development plans are designed based on this container traffic which consists of import and export traffic, and transshipment traffic. The transshipment traffic volumes are assumed to occupy 40% of total container traffic volumes. Despite of the importance of forecasting the transshipment traffic, a little studies are suggested using the concepts of the port competitiveness. In this respect, this study aims to estimate the Port Competitiveness Index and Transshipment traffic Volumes using the System Dynamics methodology. As a result, transshipment traffic volumes are predicted as: 20 million TEUs in Korea under the 4% annual increasing rates, 90 million TEUs in China under the 6% annual increasing rates, and 2.5 million TEUs in Japan under the 1% annual increasing rates respectively. The suggested results can be used to enhance the container port competitiveness and produce more transshipment traffic volumes.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.161-166
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• 2002

In view of the great disparity between forecasts of Shanghai port container handling capacity and its real results, we choose a dynamic forecast method by the causality model dynamic compensation to predict Shanghai port container handling capacity. And we forecast Shanghai port container handling capacity by using this method. We have made a satisfactory achievement, which provides a more reliable and practical way to forecast container handling capacity.