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

• 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
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.