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A hybrid neural network and traditional approach for forecasting lumpy demand

Nasiri Pour, Ali, Rostami-Tabar, Bahman and Rahimzadeh, Ayoub 2008. A hybrid neural network and traditional approach for forecasting lumpy demand. Proceedings of the World Academy of Science, Engineering and Technology 2 (4)

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Abstract

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: World Academy of Science, Engineering and Technology (WASET)
ISSN: 2010-376X
Date of First Compliant Deposit: 15 February 2019
Last Modified: 15 Feb 2019 12:00
URI: http://orca.cf.ac.uk/id/eprint/119131

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