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

Gumus A. T., GÜNERİ A. F., Ulengin F.

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, vol.128, no.1, pp.248-260, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 128 Issue: 1
  • Publication Date: 2010
  • Doi Number: 10.1016/j.ijpe.2010.06.019
  • Journal Name: INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.248-260
  • Keywords: Multi-echelon inventory management, Stochastic cost model, Neuro-fuzzy approximation, SUPPLY CHAIN NETWORKS, DEMAND UNCERTAINTY, INFERENCE SYSTEM, OPTIMAL POLICIES, MODEL, FRAMEWORK, APPROXIMATIONS, OPTIMIZATION, SIMULATION, SCOR
  • Yıldız Technical University Affiliated: Yes

Abstract

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. (C) 2010 Elsevier B.V. All rights reserved.