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 dil | İngilizce |
---|---|
Sayfa (başlangıç-bitiş) | 6697-6707 |
Sayfa sayısı | 11 |
Dergi | Expert Systems with Applications |
Hacim | 36 |
Basın numarası | 3 PART 2 |
DOI'lar | |
Yayın durumu | Yayınlandı - Nis 2009 |