Doganis, Philip and Alexandridis, Alex and Patrinos, Panagiotis and Sarimveis, Haralambos Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering, 75 (2). pp. 196-204. ISSN 0260-8774 (2006)Full text not available from this repository.
Due to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models. The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products.
|Uncontrolled Keywords:||Sales forecasting; Dairy products; Fresh milk; Neural networks; Evolutionary computation; Genetic algorithms|
|Subjects:||H Social Sciences > HD Industries. Land use. Labor
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
|Research Area:||Computer Science and Applications|
|Depositing User:||Ms T. Iannizzi|
|Date Deposited:||05 Dec 2011 11:15|
|Last Modified:||05 Dec 2011 11:15|
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