• Korea Trade Review
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• v.48 no.3
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• pp.69-88
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• 2023

This study analyzes the dynamic characteristics of cargo volume (demand), ship fleet (supply), and freight rate (price) of container, dry bulk, and tanker shipping markets by using the VAR and VECM models. This analysis is expected to enhance the statistical understanding of market dynamics, which is perceived by the actual experiences of market participants. The common statistical patterns, which are all shown in the three shipping markets, are as follows: 1) The Granger-causality test reveals that the past increase of fleet variable induces the present decrease of freight rate variable. 2) The impulse-response analysis shows that cargo shock increases the freight rate but fleet shock decreases the freight rate. 3) Among the three cargo, fleet, and freight rate shocks, the freight rate shock is overwhelmingly largest. 4) The comparison of adjR² reveals that the fleet variable is most explained by the endogenous variables, i.e., cargo, fleet, and freight rate in each of shipping markets. 5) The estimation of co-integrating vectors shows that the increase of cargo increases the freight rate but the increase of fleet decreases the freight rate. 6) The estimation of adjustment speed demonstrates that the past-period positive deviation from the long-run equilibrium freight rate induces the decrease of present freight rate.

• Korea Trade Review
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• v.43 no.3
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• pp.125-145
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• 2018

The Baltic Shipping Exchange is reporting the Baltic Dry Index (BDI) which represents the average charter rate for bulk carriers transporting major cargoes such as iron ore, coal, grain, and so on. And the current BDI index is reflected in the proportion of capesize 40%, panamax 30% and spramax 30%. Like mentioned above, the capesize plays a major role among the various sizes of bulk carriers and this study is to analyze the influence of the factors influencing on charter rate of capesize carriers which transport iron ore and coal as the major cargoes. For this purpose, this study verified causality between variables using Vector Error Correction Model (VECM) and tried to derive a long-run equilibrium model between the dependent variable and independent variables. Regression analysis showed that every six independent variable has a significant effect on the capesize charter rate, even at the 1% level of significance. Charter rate decreases by 0.08% when capesize total fleet increases by 1%, charter rate increases by 0.04% when bunker oil price increases by 1%, and charter rate decreases by 0.01% when Yen/Dollar rate increases by 1%. And charter rate increases by 0.02% when global GDP increases by one unit (1%). In addition, the increase in cargo volume of iron ore and coal which are major transportation items of capesize carriers has also been shown to increase charter rates. Charter rate increases by 0.11% in case of 1% increase in iron ore cargo volume, and 0.09% in case of 1% increase in coal cargo volume. Although there have been some studies to analyze the influence of factors affecting the charterage of bulk carriers in the past, there have been few studies on the analysis of specific size vessels. At present moment when ship size is getting bigger, this study carried out research on capesize vessels, which are biggest among bulk carriers, and whose utilization is continuously increasing. This study is also expected to contribute to the establishment of trade policies for specific cargoes such as iron ore and coal.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.546-554
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• 2018

The Bulk market, unlike the line market, is characterized by stiff competition where certain ship or freight owners have no influence on freight rates. However, freights are subject to macroeconomic variables and economic external shock which should be considered in determining management or chartering decisions. According to the results analyzed by use of ARIMA Inventiom model, the impact of the financial crisis was found to have a very strong bearing on the BDI index. First, according to the results of the VEC model, the libor rate affects the BDI index negatively (-) while exchange rate affects the BDI index by positively (+). Secondly, according to the results of the VEC model's J ohanson test, the order ship volume affects the BDI index by negatively (-) while China's economic growth rate affects the BDI index by positively (+). This shows that the shipping company has moved away from the simple carrier and responded appropriately to changes in macroeconomic variables (economic fluctuations, interest rates and exchange rates). It is believed that the shipping companies should be more aggressive in its "trading" management strategy in order to prevent any unfortunate situation such as the Hanjin Shipping incident.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.95-107
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• 2022

Shipping companies earn profits through cargo transportation, and therefore, investment decisions to purchase ships are more important than anything else. Nevertheless, the cash flow discount method was mainly used in the economic analysis method, which assumes that all situations are static. This study shows that the real option model is useful in the economic analysis of ship investment. This economic analysis took into account the irreversibility of investment and uncertainty of benefits. In particular, this study used a binary option price determination model among real options. In addition, the simulation was conducted using actual investment data of A shipping company. As a result of the analysis, the investment value of used ships according to the net present value method was analyzed as negative (-), but the investment value in the real option model reflecting the flexibility of decision-making was evaluated as having positive (+) economic feasibility. It was analyzed that economic feasibility is affected by profit volatility and discount rate. Therefore, this study is expected to help shipping companies make more flexible decisions by using the real option model along with the existing net present value method when making ship investment decisions.

• Food Science and Preservation
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• v.4 no.3
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• pp.237-243
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• 1997

Numerous variables affect product cooling rate of pressure cooling system for fruits and vegetables. These include carton vent area, initial and desired final product temperature, flow rate and temperature of the cooling air, product size, shape and thermal properties and product configuration(whether in bulk or packed in shipping cartons). This study was carried out to determine the influence of each of these variables as they affect cooling time. The opening ratio and number of the vent hole were recomended as 4∼10% and 2∼4ea., respectively, for a minimum alt flow resistance and for a uniform air flow pattern. In the cooling experiment for tomatoes and mandarins, optimum air flow rate was 0.04 m3/min.kg in terms of energy saving. The cooling air temperature should be about 2$^{\circ}C$ less than the desired final product temperature for reducing cooling time.

• Journal of the Korean Institute of Navigation
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• v.24 no.5
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• pp.385-395
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• 2000

In the transportation literature, many useful decision making models for ship routing and ship scheduling have been studied. But the majority of these studies are on industrial carriers, bulk carriers, or tankers. It is quite recent that a few optimization models have been developed for liner fleet routing and scheduling problems. However there have been few academic studies on decision making models for the routing or scheduling problems of passenger ships in spite of their economic importance in the entire shipping industry. The purpose of this study is to develop analytic decision making models for ship routing and scheduling for the passenger ship fleet. This study gives two optimization models, one is a linear programming model and the other a goal programming model. These two models are solved easy by commercial linear programming softwares and suggest optimal ship routing plans and many other useful implications for passenger ship fleet managers.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.8-14
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• 2017

Due to the financial crisis in 2008, the world economy collapsed leading to an increase in oil prices and a decrease in freight by shipping. To overcome this crisis, major shipping companies ordered larger ships, changed their trading route and improved operating of ships to overcome deficits. In particular, low-speed navigation was much favored by many companies so that it can reduce fuel consumption. However, the long-term operation of high-speed optimized engines in low-speeds has affected the jacket cooling fresh water (J.C.F.W.) system as they fail to maintain the normal operational temperature. The temperature of J.C.F.W. system dropped leading to low temperature corrosion. As a result, when the engine is operating at minimal load the functioning of existing J.C.F.W cooler is decreased and the use of fresh water generator is substantially limited. Therefore, an improvement in the functioning of J.C.F.W. system is necessary. In this paper, in order to review the improvements required for the operation of J.C.F.W. of low-speed operating marine diesel, an experiment was conducted by comparing and analyzing the results of the main engine J.C.F.W. system of a Panamax class bulk carrier 82k and a Cape class bulk carrier 180k by installing and uninstalling the J.C.F.W. Cooler. Thus, this paper proposed an improved design of the J.C.F.W. system that is suitable for the present low-speed operation.

• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.137-148
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• 2017

Port service level is a metric of competitiveness among ports for the operating/managing bodies such as the terminal operation company (TOC), Port Authority, or the government, and is used as an important indicator for shipping companies and freight haulers when selecting a port. Considering the importance of metrics, we developed software to objectively define and manage six important service indicators exclusive to container and bulk terminals including: berth occupancy rate, ship's waiting ratio, berth throughput, number of berths, average number of vessels waiting, and average waiting time. We computed the six service indicators utilizing berth 1 through berth 5 in the container terminals and berth 1 through berth 4 in the bulk terminals. The software model allows easy computation of expected ship's waiting ratio over berth occupancy rate, berth throughput, counts of berth, average number of vessels waiting and average waiting time. Further, the software allows prediction of yearly throughput by utilizing a ship's waiting ratio and other productivity indicators and making calculations based on arrival patterns of ship traffic. As a result, a TOC is able to make strategic decisions on the trade-offs in the optimal operating level of the facility with better predictors of the service factors (ship's waiting ratio) and productivity factors (yearly throughput). Successful implementation of the software would attract more shipping companies and shippers and maximize TOC profits.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.385-391
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• 2022

In this study, a multivariate time series analysis was conducted to identify various variables that impact ocean freight rates in addition to supply and demand factors. First, we used the ClarkSea Index, Clarksons Average Bulker Earnings, and Clarksons Average Tanker Earnings provided by the Shipping Intelligence as substitute variables for the dependent variable, ocean freight. The following ndependent variables were selected: World Seaborne Trade, World Fleet, Brent Crude Oil Price, World GDP Growth Rate, Industrial Production (IP OECD) Growth Rate, Interest Rate (US$ LIBOR 6 Months), and Inflation (CP I OECD) through previous studies. The time series data comprise annual data (1992-2020), and a regression analysis was conducted. Results of the regression analysis show that the World Seaborne Trade and Brent Crude Oil P rice impacted the ClarkSea Index. Only the World Seaborne Dry Bulk Trade impacted the Clarksons Average Bulker Earnings, World Seaborne Oil Trade, Brent Crude Oil Price, IP, and CP I on the Clarksons Average Tanker Earnings.