Development of fuzzy logic and genetic fuzzy commodity price prediction systems — An industrial case study

Supply Chain Managers in major automobile or construction industries often rely on the forecasted commodity prices to negotiate with suppliers where products are produced via respective commodity. For example, the natural rubber price is a key factor affecting cost in buying tires in automobile industries. This industrial case study proposes two commodity pricing prediction systems, Fuzzy Logic and Genetic Fuzzy Systems. By reviewing five main factors for this predictive model: (1) historical quarterly NR price; (2) the prices for crude oil; (3) China GDP growth rate; (4) synthetic rubber price index; (5) world natural rubber consumption/production ratio, the Genetic Fuzzy system outperforms the Fuzzy Logic system.