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

Tuǧba Efendigil*, Semih Önüt, Cengiz Kahraman

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

189 Atıf (Scopus)

Özet

An organization has to make the right decisions in time depending on 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

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)6697-6707
Sayfa sayısı11
DergiExpert Systems with Applications
Hacim36
Basın numarası3 PART 2
DOI'lar
Yayın durumuYayınlandı - Nis 2009

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