A Fuzzy Analysis Applied on Physical Commodity Market

Numerical algorithms and mathematical methods are regularly used in the commodities and stock markets.  However, this so-called quantitative trading has been thus far typically limited to the paper markets, such as futures, swaps and options, and excluded the vast physical market. Literature relates successful attempts to use artificial intelligence and neural networks on physical commodity market, but essentially the trade of physical oil, so far, does not have a computer program that provides an unquestionable advance that goes beyond the profit margin calculation to include the more complex and partially subjective crude selection factories regularly use by traders. This work applies a fuzzy analysis in a novel approach to deliver a tool to improve the valuations of complex and subjective variables of a well-known hierarchical decision process, the COPPE-Cosenza fuzzy model, now applied on crude oil trade. This work shows that the fuzzy model can be a useful tool for better decision-making, communication, and management of physical commodities trading. In this case study three European refineries present their monthly crude oil demand. Offers are successfully evaluated according to numerical and fuzzy factors which reflect the real-world decision-making process of physical commodities traders.