Search Results (6)

This study evaluates the financial viability of different main engine–fuel configurations for a container vessel on a standardized Trans-Pacific route. Using Net Present Value (NPV) analysis over a 10 year evaluation period (2024–2033), it compares six propulsion scenarios, combining three Wärtsilä engine types and four fuel alternatives (HFO, LFO, LNG, Methanol). The framework integrates technical parameters, including engine power and fuel consumption, with financial instruments such as the Weighted Average Cost of Capital (WACC) and the Capital Asset Pricing Model (CAPM). Results show that the LNG-powered Wärtsilä 8V31DF achieves the highest NPV. Despite requiring the highest initial capital expenditure (CAPEX), this configuration delivers superior financial performance and remains robust under volatile market conditions. Sensitivity tests with ±20% freight–fuel shocks and alternative discount rates (5%, 7.18%, 10%) confirm that the relative ranking of propulsion options is stable. Methanol yields negative NPVs under current prices but could become competitive with bio-methanol cost reductions or strong carbon pricing. Limitations include constant non-fuel OPEX, fixed sea state, and the exclusion of explicit carbon price scenarios. From a policy perspective, LNG appears most viable in the short term, while long-term strategies should consider ammonia and hydrogen in line with IMO decarbonization pathways. Full article

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article

In recent years, the stability of supply chains has been undermined due to many disruptions. Although it is normal for the disruptions in supply chains to occur periodically, they have great impact on the rise of the costs and they create strong imbalances in business. For years, most world trade has been performed by maritime transport due its cost-effectiveness. The COVID-19 pandemic has disrupted the global supply chain like nothing before, and the Russian invasion of Ukraine has only deepened the crisis that is affecting the global supply chain. The focus of this paper has been placed on volatile freight rates in the maritime container industry due to the COVID-19 pandemic and the Russian invasion of Ukraine and the consequences of these crises on the changes of the prices of consumer goods in the European Union. Full article

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. Full article

Finding low-dimensional chaos is a relevant issue as it could allow short-term reliable forecasting. However, the existence of chaos in shipping freight rates remains an open and outstanding matter as previous research used methodology that can produce misleading results. Using daily data, this paper aims to unveil the nonlinear dynamics of the Baltic Dry Index that has been proposed as a measure of the shipping rates for certain raw materials. We tested for the existence of nonlinearity and low-dimensional chaos. We have also examined the chaotic dynamics throughout three sub-sampling periods, which have been determined by the volatility pattern of the series. For this purpose, from a comprehensive view we apply several metric and topological techniques, including the most suitable methods for noisy time series analysis. The proposed methodology considers the characteristics of chaotic time series, such as nonlinearity, determinism, sensitivity to initial conditions, fractal dimension and recurrence. Although there is strong evidence of a nonlinear structure, a chaotic and, therefore, deterministic behavior cannot be assumed during the whole or the three periods considered. Our findings indicate that the generalized autoregressive conditional heteroscedastic (GARCH) model and exponential GARCH (EGARCH) model explain a significant part of the nonlinear structure that is found in the dry bulk shipping freight market. Full article

Freight derivative prices have been modeled assuming that the spot freight follows a particular stochastic process in order to manage them, like freight futures, forwards and options. However, an explicit formula for pricing freight options is not known, not even for simple spot freight processes. This is partly due to the fact that there is no valuation equation for pricing freight options. In this paper, we deal with this problem from two independent points of view. On the one hand, we provide a novel theoretical framework for pricing these Asian-style options. In this way, we build a partial differential equation whose solution is the freight option price obtained from stochastic delay differential equations. On the other hand, we prove lower and upper bounds for those freight options which enables us to estimate the option price. In this work, we consider that the spot freight rate follows a general stochastic diffusion process without restrictions in the drift and volatility functions. Finally, using recent data from the Baltic Exchange, we compare the described bounds with the freight option prices. Full article