{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T23:44:22Z","timestamp":1772754262289,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["the Korea government(MSIT) (NRF-2018R1D1A1B07050046)"],"award-info":[{"award-number":["the Korea government(MSIT) (NRF-2018R1D1A1B07050046)"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["the Korea government(MSIT) (No. 2021R1F1A1054766)"],"award-info":[{"award-number":["the Korea government(MSIT) (No. 2021R1F1A1054766)"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Leo Innovision Ltd.","award":["the grant funded by Leo Innovision Ltd."],"award-info":[{"award-number":["the grant funded by Leo Innovision Ltd."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22093244","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"3244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3023-2358","authenticated-orcid":false,"given":"Kyoung-Sook","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Mathematical Finance, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hee Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mathematical Finance, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hee Jean","family":"Kim","sequence":"additional","affiliation":[{"name":"Leo Innovision Ltd., #1906, IT Mirae Tower 33, Digital-ro 9-gil Geumcheon-gu, Seoul 08511, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9235-0929","authenticated-orcid":false,"given":"Hongjoong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeehoon","family":"Kang","sequence":"additional","affiliation":[{"name":"Leo Innovision Ltd., #1906, IT Mirae Tower 33, Digital-ro 9-gil Geumcheon-gu, Seoul 08511, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won Chul","family":"Paik","sequence":"additional","affiliation":[{"name":"Leo Innovision Ltd., #1906, IT Mirae Tower 33, Digital-ro 9-gil Geumcheon-gu, Seoul 08511, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/6144.888857","article-title":"Electronic part life cycle concepts and obsolescence forecasting","volume":"23","author":"Solomon","year":"2000","journal-title":"IEEE Trans. Compon. Packag. Manuf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/TCAPT.2007.900058","article-title":"A data mining based approach to electronic part obsolescence forecasting","volume":"30","author":"Sandborn","year":"2007","journal-title":"IEEE Trans. Compon. Packag. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1109\/TCPMT.2014.2316212","article-title":"Strategic proactive obsolescence management model","volume":"4","author":"Meng","year":"2014","journal-title":"IEEE Trans. Compon. Packag. Manuf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.microrel.2010.08.005","article-title":"Forecasting electronic part procurement lifetimes to enable the management of DMSMS obsolescence","volume":"51","author":"Sandborn","year":"2011","journal-title":"Microelectron. Reliab."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s12541-017-0092-6","article-title":"Electronic part obsolescence forecasting based on time series modeling","volume":"18","author":"Ma","year":"2017","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"114330","DOI":"10.1016\/j.microrel.2021.114330","article-title":"A risk-based approach to forecasting component obsolescence","volume":"127","author":"Mastrangelo","year":"2021","journal-title":"Microelectron. Reliab."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103470","DOI":"10.1016\/j.compind.2021.103470","article-title":"Prediction of obsolescence degree as a function of time: A mathematical formulation","volume":"129","author":"Trabelsi","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101347","DOI":"10.1016\/j.techsoc.2020.101347","article-title":"Obsolescence\u2014A review of the literature","volume":"63","author":"Mellal","year":"2020","journal-title":"Technol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_10","unstructured":"Raschka, S., and Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow 2, Packt Publishing. [3rd ed.]."},{"key":"ref_11","unstructured":"G\u00e9ron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media. [2nd ed.]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rom\u00e1n-Portabales, A., L\u00f3pez-Nores, M., and Pazos-Arias, J.J. (2021). Systematic review of electricity demand forecast using ANN-based machine learning algorithms. Sensors, 21.","DOI":"10.3390\/s21134544"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1109\/TCPMT.2016.2589206","article-title":"Forecasting obsolescence risk and product life cycle with machine learning","volume":"6","author":"Jennings","year":"2016","journal-title":"IEEE Trans. Compon. Packag. Manuf. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Grichi, Y., Beauregard, Y., and Dao, T.-M. (2017, January 10\u201313). A random forest method for obsolescenceforecasting. Proceedings of the 2017 IEEE International Conference on Industrial Engineeringand Engineering Management (IEEM), Singapore.","DOI":"10.1109\/IEEM.2017.8290163"},{"key":"ref_15","first-page":"27","article-title":"Optimization of obsolescence forecasting using new hybrid approach based on the RF method and the meta-heuristic genetic algorithm","volume":"2","author":"Grichi","year":"2018","journal-title":"Am. J. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Trabelsi, I., Zeddini, B., Zolghadri, M., Barkallah, M., and Haddar, M. (2021, January 4\u20136). Obsolescence prediction based on joint feature selection and machine learning techniques. Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Online.","DOI":"10.5220\/0010241407870794"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A comparison of random forest variable selection methods for classification prediction modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Noshad, Z., Javaid, N., Saba, T., Wadud, Z., Saleem, M.Q., Alzahrni, M.E., and Sheta, O.E. (2019). Fault detection in wireless sensor networks through the random forest classifier. Sensors, 19.","DOI":"10.3390\/s19071568"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_21","unstructured":"Goodfellow, I., Benjio, Y., and Courville, A. (2016). Deep Learning (Adaptive Computation and Machine Learning Series), Illustrated ed., The MIT Press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long hhort-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Physica D"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TPAMI.2002.1017616","article-title":"An efficient k-means clustering algorithm: Analysis and implementation","volume":"24","author":"Kanungo","year":"2002","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"ref_24","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:59:22Z","timestamp":1760137162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,23]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093244"],"URL":"https:\/\/doi.org\/10.3390\/s22093244","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,23]]}}}