A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis - Efendigil, Tugba; Onut, Semih; Kahraman, Cengiz (PERGAMON-ELSEVIER SCIENCE LTD, 2009)
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.
Keywords: Supply chain; Demand forecasting; Fuzzy inference systems; Neural networks; INTEGRATION; BUSINESS; IMPACT; ACCURACY; DESIGN; ANFIS; Computer Science; Engineering; Operations Research & Management Science