An integration methodology based on fuzzy inference systems and neural approaches for multi-stage supply-chains

EFENDİGİL T., ÖNÜT S.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.62, no.2, pp.554-569, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62 Issue: 2
  • Publication Date: 2012
  • Doi Number: 10.1016/j.cie.2011.11.004
  • Journal Name: COMPUTERS & INDUSTRIAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.554-569
  • Yıldız Technical University Affiliated: Yes

Abstract

This paper proposes a methodology for supply chain (SC) integration from customers to suppliers through warehouses, retailers, and plants via both adaptive network based fuzzy inference system and artificial neural networks approaches. The methodology presented provides this integration by finding the requested supplier capacities using the demand and order lead time information across the whole SC in an uncertain environment. The SC structure is investigated stage by stage. The sensitivity analysis is made by comparing the obtained results with the traditional statistical techniques. A company serving in durable consumer goods industry that produces consumer electronics in Istanbul, Turkey was examined to demonstrate the applicability of the proposed methodology. (C) 2011 Elsevier Ltd. All rights reserved.