1
Associate Professor, Shahid Beheshti University.
2
MA, Semnan University
3
MA, Tarbiat Modares University.
Abstract
Forecasting the product demand is one of the most important activities of every organization for planning the sale and finally making a comprehensive plan. It in fact determines the amount of the activities an organization carries out in the future and also provides the managers with a clear understanding of the amount and quality of the respected activities. In this article, a model based on Artificial Intelligence and Data Mining algorithms is proposed for predicting the amount of sale in tile and ceramic industry. The proposed model is a hybrid model consisting of dimensionality reduction, clustering and forecasting. In order to construct the model, this research uses independent component analysis, Manifold learning, K-means clustering and support vector regression. The present research studies 50 cases of Irana Tile Company's past 3 years of monthly sales. Because of the decrease in generic and sampling errors, the results obtained from this model show a superior precision compared to other traditional forecasting methods.
Motameni, A., Rezaei, M., & Ehghaghi, M. (2013). A Model for Forecasting the Product Demand in Tile and Ceramic Industry. Journal of Industrial Management Perspective, 3(1), 159-176.
MLA
Alireza Motameni; Mostafa Rezaei; Maryam Ehghaghi. "A Model for Forecasting the Product Demand in Tile and Ceramic Industry". Journal of Industrial Management Perspective, 3, 1, 2013, 159-176.
HARVARD
Motameni, A., Rezaei, M., Ehghaghi, M. (2013). 'A Model for Forecasting the Product Demand in Tile and Ceramic Industry', Journal of Industrial Management Perspective, 3(1), pp. 159-176.
VANCOUVER
Motameni, A., Rezaei, M., Ehghaghi, M. A Model for Forecasting the Product Demand in Tile and Ceramic Industry. Journal of Industrial Management Perspective, 2013; 3(1): 159-176.
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