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 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 3
  • Publication Date: 2009
  • Doi Number: 10.1016/j.eswa.2008.08.058
  • Page Numbers: pp.6697-6707
  • Keywords: Supply chain, Demand forecasting, Fuzzy inference systems, Neural networks, SUPPLY CHAIN, INTEGRATION, BUSINESS, IMPACT, ACCURACY, DESIGN, ANFIS


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.