Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.