Steve Engelen

Using system dynamics in maritime economics: an endogenous decision model for shipowners in the dry bulk sector

In this paper, we consider the benefits of applying system dynamics in maritime economics. We build an endogenous shipowners’ model for the dry bulk sector, incorporating both the decision process of individual shipowners and traditional shipping market conditions. The aim is to arrive at a simulation tool that can be used for a variety of applications, both for teaching/learning and for research purposes. Within the endogenous shipping model, we can distinguish between the strategic and the tactical choices shipowners face. As an application, we focus explicitly on a strategic decision-making process that is relevant to any shipowner, i.e. the sale of a vessel. System dynamics seems to be a tool well-suited for the detailed modelling of strategic and operational behaviour in the maritime business.

Market Efficiency Within Dry Bulk Markets in the Short Run: A Multi-Agent System Dynamics Nash Equilibrium

In this paper, we assess the Efficient Market Hypothesis (EMH) in relation to dry bulk shipping in the short run. The aim is to explain why freight rates in the different ship segments are highly correlated. By building a system dynamics model, which is well-suited to modelling complex and stochastic processes with limited data availability, we attempt to track the arbitrage process in which the different ship types (Handy, Panamax and Capesize) literally seek to transport each others’ cargoes (substitution) when this is beneficial. Also, within one ship segment, we govern the arbitrage opportunities arising from regional differences in freight rates. Although the EMH is ever more contested in the maritime literature, holistic and analytical proof is provided that efficiency is maintained through the intrinsic arbitrage free and evolutionary behaviour in the system towards the Nash equilibrium.

Designing a decision support model for the LNG market

Abstract As the Liquefied Natural Gas (LNG) market is supply-driven and subject to long-term contracts, both liquefaction companies and shipowners need to make strategic decisions on fleet chartering requirements. These planning decisions become ever more difficult in light of the transformations permeating the LNG market, propelling into a more competitive market with more flexible trades and expanding spot markets. The overcapacity of LNG ships during 2008–2009 triggered by massive overcontracting is a good case in point where the use of decision support models would have been beneficial, especially considering the fortunes and risks at stake. In this paper we present an LNG shipping model that effectively supports decision-making in practice. To demonstrate the value added of the model, we study the implications of LNG project delays and increased decommissioning of ships with respect to market balance and fleet requirements.

A Classical Partial Disequilibrium Model of the Gas Shipping Markets

In this paper we present a classical shipping model of the LPG, the ammonia and the petrochemical freight market that has recently been developed for use in industry practice. The aim is on the one hand to fill the gap of modeling the gas markets which to date have basically been ignored in the maritime literature. By innovating demand and productivity, as well as proposing supply with straightforward entry and exit on the other, we overcome the need of a flexible framework that in turn is applicable on modeling other shipping markets. Although this cumbersome exercise requires a considerable amount of information, the benefit of a model based on economic theory is that one can directly test the assumption of market equilibrium at the same time that it could effectively be used by the industry. By so doing, we confirm the positive albeit diffused relation between market utilization levels and freight rates, expounding that market information is instantaneously reflected in prices. A forecasting application of the model and a sensitivity example on some key variables conclude the analysis.