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

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 satisf...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 9; S. 3244
Hauptverfasser: Moon, Kyoung-Sook, Lee, Hee Won, Kim, Hee Jean, Kim, Hongjoong, Kang, Jeehoon, Paik, Won Chul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 23.04.2022
MDPI
Schlagworte:
Accuracy
Algorithms
Clustering
components obsolescence
Data mining
Decision trees
diminishing manufacturing sources and material shortages
Feature selection
forecasting
Machine learning
Manufacturing
Neural networks
unsupervised clustering
South Korea
diminishing manufacturing sources and material shortages
components obsolescence
machine learning
unsupervised clustering
forecasting
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s22093244