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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Bibliographic Details
Main Authors:	Kang, J. (Author), Kim, H. (Author), Kim, H.J (Author), Lee, H.W (Author), Moon, K.-S (Author), Paik, W.C (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	Clustering algorithms Clusterings Component obsolescence components obsolescence Cost effectiveness Customer satisfaction Diminishing manufacturing source and material shortage diminishing manufacturing sources and material shortages forecasting Forecasting Hybrid machine learning Hybrid method Learning algorithms Machine components machine learning Machine learning Machine learning algorithms Manufacture Prediction accuracy Product obsolescences Production line unsupervised clustering Unsupervised clustering
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Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm

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