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

Authors
Moon, Kyoung-SookLee, Hee WonKim, Hee JeanKim, HongjoongKang, JeehoonPaik, Won Chul
Issue Date
5월-2022
Publisher
MDPI
Keywords
components obsolescence; machine learning; forecasting; unsupervised clustering; diminishing manufacturing sources and material shortages
Citation
SENSORS, v.22, no.9
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
22
Number
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/141766
DOI
10.3390/s22093244
ISSN
1424-8220
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.
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