Container Shipping Market Research Articles

АНАЛИЗ ЛОГИСТИЧЕСКИХ РИСКОВ В УСЛОВИЯХ ПЕРЕОРИЕНТАЦИИ ГРУЗОПОТОКОВ В ВОСТОЧНОМ НАПРАВЛЕНИИ

The Russian market for logistics services is undergoing significant changes. In the context of political pressure from a number of foreign countries and the imposition of a large number of sanctions against the Russian Federation, as well as taking into account the differences in the needs of individual regions and the characteristics of foreign trade relations, there is a sharp reorientation of cargo flows in the eastern direction. Such dramatic changes in supply chains occur in parallel with a significant increase in logistics risks, which raises the issue of their timely and comprehensive analysis. Taking into account the peculiarities of the geographical location of the Russian Federation, railway transport, along with road transport, plays a vital role in organizing the transportation process of various types of goods. Therefore, the analysis of open data from official sources on the transportation of container cargo through the network of JSC Russian Railways was chosen as the methodological basis of the paper. Based on data from the container shipping market, an analysis of existing logistics risks, as well as the factors that provoke their emergence, is provided. The key risks in logistics include transport, warehouse, procurement and distribution logistics risks, and uncertainty, chance and resistance are identified as the main factors of their occurrence. The paper outlines the importance of minimizing logistics risks when organizing the transportation process in the eastern direction of Russia and provides a number of ways to reduce the likelihood of risk occurrence. One of these methods is risk dissipation. The implementation of this method is shown by the example of the implementation of an organizational group of activities at the Eastern training ground, which allows reducing the load on the railway and port infrastructure. As a result, the likelihood of risk occurring in logistics processes is reduced.

On the Efficiency Impacts of Berthing Priority Provision

Facing intensified interport competition in the global container shipping market, an increasing number of ports choose to offer berthing priority for carriers to increase their attractiveness. This study is the first to theoretically analyze the efficiency impacts of such prioritization. Specifically, this study models the steady-state dynamics for each terminal in a biterminal port as a prioritized queuing system. We explore the equilibrated shipping flow distribution and resulting total system cost (i.e., bunker consumption cost and waiting time cost) with and without priority provision, along with their major analytical properties. Then, we examine the “second-order” effects of these priority schemes on just-in-time (JIT) arrivals, an increasingly popular green port management tool. Specifically, we investigate how the equilibrium state associated with JIT arrivals could change with priority berthing. These analyses generate some interesting results, including (1) the total system cost increases or remains unchanged when a priority scheme is implemented under a symmetric port with equal service capacities for both terminals; (2) under the asymmetric biterminal case, however, it is also possible that berth prioritization could reduce the total system cost, and such phenomenon occurs only if the terminal which offers prioritization owns larger service capacity; (3) the results indicate that the “price of prioritization” could reach [Formula: see text] in port operation when the berth loading is heavy, implying that priority provision may significantly harm the operational efficiency; and (4) lastly, priority provision has a negative second-order effect on JIT strategies in a symmetric port, and such negative effect may neutralize the positive ones. Those theoretical results are validated by numerical experiments, and some of them are also supported by empirical data. The results provide important practical implications for the decision making of the port (or terminal) agencies. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371143 and 72188101]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0411 .

Identifying recovery coupling in port-maritime-trade networks: Empirical analysis from the global container shipping market

Identifying recovery coupling in port-maritime-trade networks: Empirical analysis from the global container shipping market

Container ship investment Decisions―Newbuilding vs second-hand vessels

Ship investment is a crucial strategic issue for shipping companies as the container shipping industry is highly capital-intensive. In practice, a company's ship investment decision mainly concerns new shipbuilding and second-hand vessels. The investment of new container ships with advanced technology is high in cost and the payback period is long. Differently, the second-hand vessels' investment cycle is shorter, but there are certain drawbacks in loading capacity and maintenance. Therefore, it is significant to investigate the investment selection of new-building and second-hand ships in the container shipping market. Previous studies have analyzed this topic at aggregate or industry level. Few studies have attempted to reveal ship type preferences from observed individual data. Therefore, using the data from the Clarkson's World Fleet Register between 2000 and 2021, this study employs the Nested Logit model to analyze the container ship investment preferences, and explore the impact of different variables on decision-making from the perspective of individual companies. The empirical results suggest the higher substitution effect among alternatives within the same nest than that of crossing nests. Along with discussion on the effects of various factors, it discloses the higher preferences of larger types of vessels in responding to competitors' capacity expansion, especially the larger companies. Therefore, it is important to distinguish the larger companies' capacity expansion between responding to demand and competing with competitors, especially for those companies adopting follower strategies.

Costs and Benefits of the Disruptions Caused by the Pandemic Crisis and the Container Shipping Market’s Turbulences and their Allocation within the Global Supply Chains: An Analytical Approach

Costs and Benefits of the Disruptions Caused by the Pandemic Crisis and the Container Shipping Market’s Turbulences and their Allocation within the Global Supply Chains: An Analytical Approach

Construction of resilience mechanisms in response to container shipping market volatility during the pandemic period: From the perspective of market supervision

As a key enabler of international trade, the container shipping market experienced port congestion, space shortages, and skyrocketing freight rates following the outbreak of the COVID-19 pandemic. Therefore, this paper first utilizes the vector autoregressive model with exogenous variables (VARX) to analyze the interactive mechanisms of economy, capacity, freight rate, and port congestion during the pandemic to identify the causes behind the excessive market volatility. We then introduce government supervision to improve the policy deficiencies in pricing rules and assess whether prohibiting liner companies from charging excessive surcharges can solve market dilemmas by constructing a tripartite evolutionary game model. The results show that liner companies deliberately maintained congestion by using strict control over shipping capacity to raise freight rates and obtain high profits, which is one of the main reasons for the problems. However, we also conclude that relying solely on the spontaneous behavior of the market is unlikely to resolve the predicament. Instead, government intervention can play a crucial role in encouraging liner companies to increase appropriate container capacity, stabilize freight rates, and mitigate the risk of supply chain disruptions due to port congestion. This, in turn, can enhance the resilience of the container transport system.

Analysis of fleet deployment in the international container shipping market using simultaneous equations modelling

ABSTRACT The shipping industry is a capital-intensive industry in which ship investment and demolition are directly related to the company’s profitability, transportation capacity, and fleet structure. Thus, companies need to consider investment and demolition activities carefully before making decisions about fleet deployment. This study focuses on decisions related to investment in new and second-hand vessels as well as vessel demolition in different sub-markets of the container shipping market at the same time. By considering market factors, operating conditions, and shipping alliances, we developed a systematic model for the container market. This model innovatively employs a simultaneous equations model (SEM) to analyze fleet deployment decisions and uses the three-stage least squares method to avoid endogenous effects. The results indicate that shipping alliances, fleet deployment, and bunker price all have significant effects on the investment and demolition decisions of shipping companies. We further discuss the strategic decisions of shipping companies related to joining shipping alliances and highlight the considerations of larger companies when expanding (primarily squeezing out competition and responding to the market). The results provide valuable information for shipping companies to determine the best ship deployment strategies and avoid overcapacity.

Has Container Shipping Industry been Fixing Prices in Collusion?: A Korean Market Case

Purpose - The purpose of this study is to analyze the market power of the Korea Container Shipping Market (Intra Asia, Korea-Europe, and Korea-U.S.) to verify the existence of collusion empirically, and to answer whether the joint actions of liner market participants in Korea have formed market dominance for each route. Precisely, it will be verified through the Lerner index as to whether the regional market of Asia is a monopoly, oligopoly, or perfect competition.
 Design/methodology - This study used a Lerner index adjusted with elasticity presented in the New Imperial Organization (NEIO) studies. NEIO refers to a series of empirical studies that estimate parameters to judge market power from industrial data. This study uses B-L empirical models by Bresnahan (1982) and Lau (1982). In addition, NEIO research data statistically contain self-regression and stability problems as price and time series data. A dynamic model following Steen and Salvanes' Error Correction Model was used to solve this problem.
 Findings - The empirical results are as follows. First, λ, representing market power, is nearly zero in all three markets. Second, the Korean shipping market shows low demand elasticity on average. Nevertheless, the markup is low, a characteristic that is difficult to see in other industries. Third, the Korean shipping market generally remains close to perfect competition from 2014 to 2022, but extreme market power appears in a specific period, such as COVID-19. Fourth, there was no market power in the Intra Asia market from 2008 to 2014.
 Originality/value - Doubts about perfect competition in the liner market continued, but there were few empirical cases. This paper confirmed that the Korea liner market is a perfect competition market. This paper is the first to implement dynamics using ECM and recursive regression to demonstrate market power in the Korean liner market by dividing the shipping market into Deep Sea and Intra Asia separately. It is also the first to prove the most controversial problems in the current shipping industry numerically and academically.

Global supply chains' disruptions as a result of the COVID-19 pandemic crisis and the dynamic world container shipping market recovery

Global supply chains' disruptions as a result of the COVID-19 pandemic crisis and the dynamic world container shipping market recovery

Zakłócenia incydentalne w łańcuchu dostaw — analiza ripple effect w świetle badań literaturowych

Zakłócenia incydentalne w łańcuchu dostaw — analiza ripple effect w świetle badań literaturowych

Optimal competitive capacity strategies: Evidence from the container shipping market

For nearly two decades, ocean carriers have been locked in an arms race for capacity, which has led to huge losses for many and even bankruptcy for some. We investigate the nature of this investment race by studying a long-term capacity investment problem in a duopoly under demand uncertainty. In our model, two firms make sequential capacity decisions, responding to each other’s current and future capacity. We consider two types of strategies which differ in terms of how a firm considers the opponent’s future capacity in its own strategy: a proactive strategy where the firm assumes that the opponent will respond using a certain strategy, or a reactive strategy where the firm assumes that the opponent’s future capacity remains unchanged. In the proactive case, we allow the firm to have different assumptions on the opponent’s strategy, representing different amounts of information the firm has on the opponent. For each type of strategies, we derive the firm’s optimal decisions on both the timing and size of capacity adjustments, specified by an array of intervals for the optimal capacity in a given capacity space in each period. Using detailed data from the container shipping market (2000–2015), we illustrate how to plan competitive capacity investments, following our model. By comparing the optimal decisions specified by our model with the reality, we show that the realized capacity decisions of the leading carriers, which were often questioned as irrational, are close to optimal, assuming these carriers follow proactive strategies. By revealing the underlying structures of different strategies, that is, the stayput intervals, we show how a specific strategy brings value to firms under competition. Based on our results, we provide practical guidelines to carriers and firms which operate in a similar competitive market for implementing an effective competitive capacity strategy.

Investment and trading strategies in the maritime sector: an application to the secondhand containership market

As shipping market players operate in a competitive and volatile business environment, their highly capital-intensive investment decisions to target value-generating projects are exposed to critical risks. Therefore, the paper investigates the efficiency of investment strategies based on the combination of technical trading rules and fundamental analysis in selling and purchasing ships in the container shipping market. Using historical datasets of second-hand vessel prices and time-charter rates from October 1996 to June 2021, long-run cointegrating implications and short-run causality spillover effects are examined for three groups of containerships, distinguished by their transportation capacity, viz. 725 TEUs, 1700 TEUs, and 3500 TEUs. In addition, the moving average trading rules are used to indicate the timing of investment or divestment decisions through the analysis period. The results for vessel prices and earnings for 3500 TEU containerships appeared to be more volatile compared to smaller ships (725 TEUs and 1700 TEUs), while time-charter earnings are seen to exert an impact on second-hand prices across all vessel types. Moreover, due to higher volatility, the trading strategies based on price-earning ratios significantly outperform the buy-and-hold strategies for the 3500 TEU and the 1700 TEU containerships. On the contrary, the decision to buy-and-hold smaller container ships (725 TEU) yields higher profits than the active sale and purchase strategy. The insights provided in this paper can be used by multiple stakeholders, such as liner operators, investors, lessors, and researchers.

Ocean carrier alliances and the impact on container freight rate

ABSTRACT Container shipping is widely known as the most cost-effective means of transporting manufactured goods worldwide. However, the current dynamics of the shipping market which started around Q2 of 2020 due to COVID-19 disruption of global supply chain seems to be eroding this inherent benefit of Liner shipping. This study seeks to establish that whilst the outbreak of COVID-19 and disruption of global supply chain may be unplanned, blanking of sails by ocean carriers is planned. Explanatory research method was adopted for the study while market power served as the explanatory framework. Consequently, the study identified indicators of market power that reveal the stronghold of carrier alliances influence in the container shipping market. The study discovered as follows: (i) carrier alliances are critical part of modern shipping and evolving, (ii) skillful capacity management is the New Normal in container shipping, and (iii) final consumers bear the economic brunt of freight rate increases.

Container vessel selection for maritime shipping companies by using an extended version of the Grey Relation Analysis (GRA) with the help of Type-2 neutrosophic fuzzy sets (T2NFN)

Contrary to expectations, based on occurring changes in customer behaviours related to consuming and shopping triggered by the pandemic, the global container shipping market has continued to grow during COVID 19. Experts estimate that these increases will continue in the future due to changing consumption habits. However, container shipping companies (CSCs) may soon encounter many troubles and challenging situations. They have an extremely fragile structure and may be entirely unprotected when they encounter unexpected situations sourced from external factors. (i.e., due to grounding of a ship, complete blockage of the Suez channel for three weeks can be given as a clear example of that). Hence, selecting an appropriate container vessel type can help construct a healthier container shipping system less influenced by adverse conditions for decision-makers and practitioners. Besides, it can provide a more effective and productive maritime transportation environment for all stakeholders. However, selecting a proper container vessel type is a complicated decision-making problem since many conflicting criteria and complex ambiguities exist. The current paper proposes an extended version of the GRA technique with the help of type-2 neutrosophic fuzzy sets (T2NFN) for capturing and processing uncertainties better than the traditional MCDM frameworks. According to the obtained results, C6, container carrying capacity, is the most influential criterion and the type of post-Suezmax container vessel is the best option for the CSCs, as it provides advantages at a satisfactory level for almost all criteria than others. After the proposed model was applied, a comprehensive sensitivity analysis (SA) was performed to test the validity of the T2NFN GRA approach. The results of SA approve the applicability, effectivity, and robustness of the model.

Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment

With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates.

A deep learning approach for port congestion estimation and prediction

ABSTRACT This study proposes high-frequency container port congestion measures based on Automatic Identification System (AIS) data. Vessel movement information of 3,957 container ships from March 2017 to April 2017 is included. The world top 20 container ports’ berth and anchorage areas are identified through Density Based Spatial Clustering of Applications with Noise (DBSCAN) and convex hull methods, and their hourly port congestion statuses are depicted in terms of the traffic volume and turnaround time. The constructed congestion measures overcome the disadvantages of the traditionally used port or industry data, which is heterogenous, behind the time and not easy to obtain publicly. The higher frequency (hourly) of the proposed measures can effectively monitor any slight change in port performance. A Long Short-Term Memory (LSTM) neural network model is then proposed for congestion prediction using constructed congestion measures. Point prediction and sequence prediction are both performed. We innovatively introduce congestion propagation effects into the prediction model as input features. Using Shanghai, Singapore and Ningbo ports as case studies, results show that the inclusion of congestion propagation effect can improve the prediction performance especially for sequence prediction. This study provides significant implications and decision support for container shipping market participants.

Measuring volatility spillover effects in dry bulk shipping market

Dry bulk shipping market, as an indispensable part of maritime supply chains, can be affected by the transport demand from commodity market, the costs from bunker market, the macroeconomic environment, and threats from the competitor market. To analyze such interactions between these markets, this paper proposes the use of a vector-autoregression (VAR) model to explore volatility spillover effects in dry bulk shipping markets in terms of the secondhand ship prices of Capesize, Panamax and Handymax during 2009–2019. The characteristics of sub-segments of dry bulk shipping market are deeply analyzed, which are necessary for asset allocation in shipping industry. A series of numerical experiments are carried out, by means of variance decomposition approach and historical decomposition approach. Results reveal the existence of different responses to shocks in sub-segments of dry bulk shipping market. It is also shown that smaller vessels are more likely to be affected by related markets except the outlier of the relationship between China interest rate and Panamax market. In addition, responses to both negative and positive contributions are discussed. For example, commodity market has a profound effect on dry bulk shipping market when the market is stable, whereas influences of the bunker market represents the volatility of a market. Finally, this paper finds insignificant effects of the US-CNY exchange rate market and the container shipping market on the dry bulk shipping market.

Container Freight Rates and International Trade Causality Nexus: Evidence from Panel VAR Approach for Shanghai and ASEAN‐6 Countries

Container shipping drives the development of international trade, yet previous studies have not fully appreciated the possible bidirectional causal relationship between container freight rates and international trade. In the context of the “twenty‐first Century Maritime Silk Road,” we use data on export container freight rates and import/export data from Shanghai to ASEAN‐6 countries (Singapore, Vietnam, Thailand, Philippines, Malaysia, and Indonesia) from February 2017 to January 2020 and apply a panel VAR to explore the relationship between maritime transport costs and international trade in the container shipping market. We use the freight rate data are from the Southeast Asia Freight Index, which is used for the first time in an empirical study. The quality and reliability of the freight rate data allow this paper to better identify causal relationships. The results of the panel Granger causality test and the orthogonal impulse response function suggest a bidirectional causal relationship between freight rates and export trade; to further explain, an increase in export trade lowers export freight rates, but an increase in export freight rates hinders export trade, and a growth in import trade unidirectionally raises export freight rates. We believe that international trade may impact freight rates through economies of scale and trade imbalances.

Optimal Competitive Capacity Strategies: Evidence from the Container Shipping Market

Optimal Competitive Capacity Strategies: Evidence from the Container Shipping Market

Monopoly in the container shipping market: an econometric approach

PurposeSince the 2010s, market conditions for container shipping companies have been deteriorating owing to decreasing container cargo trade and increasing supply capacity. This study aims to contribute to the empirical literature on the container shipping industry market structure. Specifically, this study aims to investigate the extent of market competition.Design/methodology/approachThis study analyzes the market structure and evaluates the market power of shipping companies through a non-structural test.FindingsThe H-statistic for the entire period of 2004–2018 was 0.37, which is significantly different from zero. This indicates the absence of monopoly pricing throughout the entire period. For the time-phased estimates, the H-statistic between 2004 and 2008 is 0.15, which is not significantly different from zero. On the other hand, the H-statistic from 2009 to 2018 was 0.40, which differs significantly from zero.Originality/valueAs the Far East Freight Conference had released tariffs and charge rates by item for container shipping routes, monopolistic pricing is said to have appeared until the European Union abolished the European Economic Community (No. 4056/86) in 2008, before the economic crisis. However, this study indicates that pricing in the container shipping industry has been distinctly non-monopolistic; further, competition seems to have intensified since 2008. Industry competitiveness is of interest not only to academics but also to practitioners, including policymakers, especially when considering competition policies.