Title :
A forecast model for pharmaceutical requirements based on an artificial neural network
Author :
Fruggiero, Fabio ; Iannone, Raffaele ; Martino, Giada ; Miranda, Salvatore ; Riemma, Stefano
Author_Institution :
Dept. of Eng. & Environ. Phys., Univ. of Basilicata, Potenza, Italy
Abstract :
This research introduces a support tool for the demand forecast management of local pharmacies. It is based on the forecast of the requirements obtained through the implementation of a Radial Basis Function (RBF) neural network. Having an accurate forecast allows you to reduce the average level of stock and consequently the costs of warehousing and space needed for storage.
Keywords :
demand forecasting; pharmaceutical industry; radial basis function networks; warehousing; RBF neural network; artificial neural network; demand forecast management; forecast model; local pharmacy; pharmaceutical requirements; radial basis function neural network; storage; warehousing; Artificial neural networks; Biomedical monitoring; History; Medical diagnostic imaging; Monitoring; Neurons; Safety;
Conference_Titel :
Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on
Conference_Location :
Suzhou
Print_ISBN :
978-1-4673-2400-7
DOI :
10.1109/SOLI.2012.6273543
