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Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm

Authors :
Kyoung-Sook Moon
Hee Won Lee
Hee Jean Kim
Hongjoong Kim
Jeehoon Kang
Won Chul Paik
Source :
Sensors, Vol 22, Iss 9, p 3244 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.

Details

Language :
English
ISSN :
14248220
Volume :
22
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.51df69952c1840539dbbd455a7b0d01d
Document Type :
article
Full Text :
https://doi.org/10.3390/s22093244