Design and development of neuro fuzzy methods to monitor supply chain disruption in manufacturing industries

Abstract

Supply Chain Management (SCM) plays an important role in coordinating the flow of materials from source to destination. SCM depends on many external factors like SCM of other companies that are involved in supplying raw materials and many internal factors like working conditions of machines, working facilities and availability of in-process materials during manufacturing of products. Any disruption in supply chain leads to increase in time for delivery of finished products to the customers. This research work focuses on monitoring and estimating the Supply Chain Disruption (SCD) in the fastener/ automobile/ paper production industries by using Fuzzy Inference System (FIS). Inorder to increase the performance of the FIS, the parameters of the FIS are modified by using Aritificial Neural Network (ANN) algorithms like Back Propagation Algorithm (BPA), Radial Basis Function (RBF), Echo State Neural Network (ESNN) and Cerebellar Model Articulation Controller (CMAC). newline