{"title":"在公司中实施预测性维护:专家访谈的行业见解","authors":"Carolin Wagner, B. Hellingrath","doi":"10.1109/ICPHM.2019.8819406","DOIUrl":null,"url":null,"abstract":"The implementation of predictive maintenance as a proactive maintenance approach is gaining increasing importance in the age of digitization and the fourth industrial revolution. Various studies in the German industry have shown that the majority of companies already follow up on the topic. However, successful implementations of predictive maintenance in businesses are still a rarity. Due to the lack of knowledge and guidance during the implementation process, companies experience many difficulties for the realization of this proactive maintenance approach. Even though much research has been conducted in the fields of predictive maintenance and prognostics and health management, little attention was devoted to the design and analysis of process models for industrial applications. Common process models are theoretically derived without capturing the complexity of reality. This paper communicates the results of interviews conducted with six industry experts. In particular, experts from management consultancies are addressed with experience in multiple successful implementations. Based on the collected data, industry insights in terms of steps and phases of process models, best practices and challenges are provided.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Implementing Predictive Maintenance in a Company: Industry Insights with Expert Interviews\",\"authors\":\"Carolin Wagner, B. Hellingrath\",\"doi\":\"10.1109/ICPHM.2019.8819406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The implementation of predictive maintenance as a proactive maintenance approach is gaining increasing importance in the age of digitization and the fourth industrial revolution. Various studies in the German industry have shown that the majority of companies already follow up on the topic. However, successful implementations of predictive maintenance in businesses are still a rarity. Due to the lack of knowledge and guidance during the implementation process, companies experience many difficulties for the realization of this proactive maintenance approach. Even though much research has been conducted in the fields of predictive maintenance and prognostics and health management, little attention was devoted to the design and analysis of process models for industrial applications. Common process models are theoretically derived without capturing the complexity of reality. This paper communicates the results of interviews conducted with six industry experts. In particular, experts from management consultancies are addressed with experience in multiple successful implementations. Based on the collected data, industry insights in terms of steps and phases of process models, best practices and challenges are provided.\",\"PeriodicalId\":113460,\"journal\":{\"name\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"volume\":\"156 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPHM.2019.8819406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPHM.2019.8819406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementing Predictive Maintenance in a Company: Industry Insights with Expert Interviews
The implementation of predictive maintenance as a proactive maintenance approach is gaining increasing importance in the age of digitization and the fourth industrial revolution. Various studies in the German industry have shown that the majority of companies already follow up on the topic. However, successful implementations of predictive maintenance in businesses are still a rarity. Due to the lack of knowledge and guidance during the implementation process, companies experience many difficulties for the realization of this proactive maintenance approach. Even though much research has been conducted in the fields of predictive maintenance and prognostics and health management, little attention was devoted to the design and analysis of process models for industrial applications. Common process models are theoretically derived without capturing the complexity of reality. This paper communicates the results of interviews conducted with six industry experts. In particular, experts from management consultancies are addressed with experience in multiple successful implementations. Based on the collected data, industry insights in terms of steps and phases of process models, best practices and challenges are provided.