Table of Contents
The global dry bulk shipping industry is an essential part of the international shipping sector, with ocean-going vessels representing the most efficient, and often the only method of transporting large volumes of basic commodities and finished products. The dry bulk shipping is defined as the transportation of homogenous bulk cargoes by bulk vessels on an irregular scheduled line.
Dry bulk shipping is involved in seaborne transport of commodities such as iron ore, coal (both thermal and coking), grain, bauxite and phosphate (major bulks), and other metals, minerals, and agribulks, such as fertilizer (minor bulks). The industry itself is extremely cyclical and volatile, characterized by large booms and busts. The global dry bulk carrier fleet may be divided into four categories based on a vessel's carrying capacity. These categories consist of: Capesize vessels, Panamax vessels, Handymax vessels, and Handysize vessels.
The key factors driving growth of the global dry bulk industry include rising urban population, accelerating global economic development, growing steel production and increasing global pig iron industry. Some of the noteworthy developments of this industry include downturn in the dry bulk charter market, containerization of dry bulk. However, the growth of respective industry is hindered by Chinese economic conditions, the acts of piracy and risk of oversupply and cyclicality.
This report offers in-depth analysis of the global dry bulk shipping market, along with an analysis of its commodities market including iron ore, coal, grain, bauxite/alumina, phosphate and minor bulk. The competition in the respective market is highly diversified, fragmented and also capital intensive. Some of the major players are Diana Shipping, Dry Ships, and Genco Shipping.
By combining SPSS Inc.’s data integration and analysis capabilities with our relevant findings, we have predicted the future growth of the market. We employed various significant variables that have an impact on this industry and created regression models with SPSS Base to determine the future direction of the industry. Before deploying the regression model, the relationship between several independent or predictor variables and the dependent variable was analyzed using standard SPSS output, including charts, tables and tests.
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