{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:03:52Z","timestamp":1771520632115,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.<\/jats:p>","DOI":"10.3390\/info11070354","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T11:07:42Z","timestamp":1594033662000},"page":"354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Early Prediction of Quality Issues in Automotive Modern Industry"],"prefix":"10.3390","volume":"11","author":[{"given":"Reza","family":"Khoshkangini","sequence":"first","affiliation":[{"name":"Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0051-0954","authenticated-orcid":false,"given":"Peyman","family":"Sheikholharam Mashhadi","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden"}]},{"given":"Peter","family":"Berck","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden"}]},{"given":"Saeed","family":"Gholami Shahbandi","sequence":"additional","affiliation":[{"name":"Volvo Group, Connected Solutions, 40508 G\u00f6teborg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3272-4145","authenticated-orcid":false,"given":"Sepideh","family":"Pashami","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-5201","authenticated-orcid":false,"given":"S\u0142awomir","family":"Nowaczyk","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden"}]},{"given":"Tobias","family":"Niklasson","sequence":"additional","affiliation":[{"name":"Volvo Group, Q&amp;CS, 40508 G\u00f6teborg, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.ijimpeng.2004.04.004","article-title":"Failure prediction for advanced crashworthiness of transportation vehicles","volume":"30","author":"Pickett","year":"2004","journal-title":"Int. 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