A type-2 fuzzy system model for reducing bullwhip effects in supply chains and its application in steel manufacturing

The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments bymeans of type-2 fuzzy methodology. in order to reduce the bullwhip effect in a supply chain, we propose anew method for demand forecasting. first, the demand data of a real steel industry in canada is clusteredwith an interval type-2 fuzzy c-regression clustering algorithm. then, a novel interval type-2 fuzzy hybridexpert system is developed for demand forecasting. this system uses fuzzy disjunctive normal forms(fdnf) and fuzzy conjunctive normal forms (fcnf) for the aggregation of antecedents. an interval type-2 fuzzy order policy is developed to determine orders in the supply chain. then, the results of the proposedmethod are compared with the type-1 fuzzy expert system as well as the type-1 fuzzy time series methodin the literature. the results show that the bullwhip effect is significantly reduced; also, the system hasless error and high accuracy.