Cited By
View all- Li GXu SLi Z(2010)Short-Term Price Forecasting For Agro-products Using Artificial Neural NetworksAgriculture and Agricultural Science Procedia10.1016/j.aaspro.2010.09.0351(278-287)Online publication date: 2010
Daily demand forecasting of new products utilizing diffusion models and genetic algorithms
Pages 1106 - 1107
Abstract
New high technology consumer products are released frequently. The manufacturers have to avoid dead stock because the value of the products drops sharply after the launch of new products. Thus, the importance of daily demand forecasting is increasing. In this paper, we propose a daily demand forecasting method for new products. The method uses diffusion models to forecast demand. A Genetic Algorithm (GA) is used to estimate the parameters of the model. In order to apply the diffusion model to daily demand forecast, we introduce time-variant parameters, which depend on the day of the week. The proposed method is applied to the daily demand forecasting of high technology consumer products. The result shows that the proposed method has an excellent daily demand forecasting ability.
References
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C. Crum and G. E. Palmatier. Demand Management Best Practices. J. Ross Publishing, Inc, 2003.
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L. J. Eshelman and J. D. Schaffer. Real-coded genetic algorithms and interval-schemata. In L. D. Whitley, editor, Foundations of Genetic Algorithms 2. Morgan Kaufman, 1993.
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V. Mahajan, E. Muller, and Y. Wind. New-product diffusion models: From theory to practice. In V. Mahajan, E. Muller, and Y. Wind, editors, New-Product Diffusion Models, pages 3--24. Kluwer Academic Publishers, 2000.
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Index Terms
- Daily demand forecasting of new products utilizing diffusion models and genetic algorithms
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March 2009
2347 pages
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Published: 08 March 2009
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View all- Li GXu SLi Z(2010)Short-Term Price Forecasting For Agro-products Using Artificial Neural NetworksAgriculture and Agricultural Science Procedia10.1016/j.aaspro.2010.09.0351(278-287)Online publication date: 2010
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