A Computational Intelligence Approach to Supply Chain Demand Forecasting

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Author(s): Nicholas Ampazis (University of the Aegean, Greece)
Copyright: 2011
Pages: 15
Source title: Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods
Source Author(s)/Editor(s): Ioannis Minis (University of the Aegean, Greece), Vasileios Zeimpekis (University of the Aegean, Greece), Georgios Dounias (University of the Aegean, Greece)and Nicholas Ampazis (University of the Aegean, Greece)
DOI: 10.4018/978-1-61520-633-9.ch005

Keywords: Business & Management / Business Science Reference / Operations and Service Management / Supply Chain Management

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Abstract

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

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