Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data Online publication date: Mon, 04-Sep-2017
by Ilham Slimani; Ilhame El Farissi; Said Achchab
International Journal of Logistics Systems and Management (IJLSM), Vol. 28, No. 2, 2017
Abstract: The purpose of any effective supply chain is to find balance between supply and demand by coordinating all internal and external processes in order to ensure delivery of the right product, to the right customer, at the best time and with the optimal cost. Therefore, the estimation of future demand is one of the crucial tasks for any organisation of the supply chain system who has to make the correct decision in the appropriate time to enhance its commercial competitiveness. In an earlier study, where various artificial neural networks' structures are compared including perceptron, adaline, no-propagation, multi layer perceptron (MLP) and radial basis function for demand forecasting, the results indicate that the MLP structure present the best forecasts with the optimal error. Consequently, this paper focuses on realising a daily demand predicting system in a supermarket using MLP by adding inputs including previous demand, days' classification and average demand quantities.
Online publication date: Mon, 04-Sep-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Logistics Systems and Management (IJLSM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com