TY  - JOUR
SP  - 196
A1  - Doganis, Philip
A1  - Alexandridis, Alex
A1  - Patrinos, Panagiotis
A1  - Sarimveis, Haralambos
PB  - Elsevier
IS  - 2
JF  - Journal of Food Engineering
Y1  - 2006/07//
VL  - 75
N2  - 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.
SN  - 0260-8774
UR  - http://dx.doi.org/10.1016/j.jfoodeng.2005.03.056
AV  - none
TI  - Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing
KW  - Sales forecasting; Dairy products; Fresh milk; Neural networks; Evolutionary computation; Genetic algorithms
EP  - 204
ID  - eprints1026
ER  -