A Decision Support System for Demand Forecasting with Artificial Neural

ARTICLE IN PRESS

Expert Systems with Applications xxx (2008) xxx–xxx

Contents lists available at ScienceDirect

Expert Systems with Applications

journal homepage: www.elsevier.com/locate/eswa

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

˘ Tugba Efendigil a,*, Semih Önüt a, Cengiz Kahraman b

a b

Yildiz Technical University, Mechanical Faculty, Department of Industrial Engineering, 34349 Yildiz, Istanbul, Turkey Istanbul Technical University, Faculty of Management, Department of Industrial Engineering, 34367 Macka, Istanbul, Turkey

a r t i c l e

i n f o

a b s t r a c t

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 Copyright Ó 2008 Published by Elsevier Ltd. All rights reserved.

Keywords: Supply chain Demand forecasting Fuzzy inference systems Neural networks

1. Introduction A supply chain (SC) has a dynamic structure involving the constant flow of information, product, and funds between different stages (Chopra & Meindl, 2001). Supply chain process has three important...