Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 3, Pages 6697-6707Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.08.058
Keywords
Supply chain; Demand forecasting; Fuzzy inference systems; Neural networks
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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.
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