Neural computing: theory and practice
Neural computing: theory and practice
Introduction to Grey system theory
The Journal of Grey System
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Sales forecasting using time series and neural networks
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Computers and Operations Research
The inventory management system for automobile spare parts in a central warehouse
Expert Systems with Applications: An International Journal
An Investigation of Forecasting Critical Spare Parts Requirement
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The critical spare parts (CSP) are vital to machine operation, which also have the characteristic of more expensive, larger demand variation, longer purchasing lead time than non-critical spare parts. Therefore, it is an urgent issue to devise a way to forecast the future requirement of CSP accurately. This investigation proposed Moving back-propagation neural network (MBPN) and Moving fuzzy-neuron network (MFNN) to effectively predict the CSP requirement so as to provide as a reference of spare parts control. This investigation also compare prediction accuracy with other forecasting methods, such as grey prediction method, back-propagation neural network (BPN), fuzzy-neuron networks (FNN). All of the prediction methods evaluated the real data, which are provided by famous wafer testing factories in Taiwan, the effectiveness of the proposed methods is demonstrated through a real case study.