A new methodology for multi-echelon inventory management in stochastic and neuro-fuzzy environments

Managing inventory in a multi-echelon supply chain is considerably more difficult than managing it in a single-echelon one. A strategy that optimizes inventory one echelon at a time results in excess inventory without necessarily improving service to customer. In this paper, a methodology for effective multi-echelon inventory management is proposed. Subsequently; a neural network simulation of the model is then presented with the support of neuro-fuzzy demand and lead time forecasting, and finally its performance is calculated using performance metrics selected from the SCOR model. The results show that, the inventory is efficiently deployed and uses realistic breakdowns. The proposed methodology aims to provide an important tool for the management of general N-echelon tree-structured supply chains that overcomes some of the deficiencies of competing methodologies.