A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

EFENDİGİL T., ÖNÜT S., Kahraman C.

EXPERT SYSTEMS WITH APPLICATIONS, vol.36, no.3, pp.6697-6707, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 3
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2008.08.058
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.6697-6707
  • Keywords: Supply chain, Demand forecasting, Fuzzy inference systems, Neural networks, SUPPLY CHAIN, INTEGRATION, BUSINESS, IMPACT, ACCURACY, DESIGN, ANFIS
  • Yıldız Technical University Affiliated: Yes


An organization has to make the right decisions in time depending an demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information, The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey, Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.