DocumentCode
3048904
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
fYear
2012
fDate
8-10 July 2012
Firstpage
263
Lastpage
268
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on
Conference_Location
Suzhou
Print_ISBN
978-1-4673-2400-7
Type
conf
DOI
10.1109/SOLI.2012.6273543
Filename
6273543
Link To Document