{"title":"A methodology to boost data-driven decision-making process for a modern maintenance practice","authors":"A. Polenghi, I. Roda, M. Macchi, A. Pozzetti","doi":"10.1080/09537287.2021.2010823","DOIUrl":null,"url":null,"abstract":"Abstract Maintenance is evolving due to the double-sided influence of the Asset Management paradigm and digitalization. In this evolution, assessing the maintenance management process status in terms of process completeness, information and data completeness and integration is paramount to boost reliable data-driven decision-making. Grounding on Design Science Research, a methodology is realized to favour the comparison of two data models, a reference one and a company-specific one, used as a means to evaluate the process status. In particular, the methodology embeds a reference data model for the maintenance management process. Both methodology and data model are artefacts tested and refined during action research in an automotive company willing to improve the maintenance management process. The application of both artefacts demonstrates that the company is facilitated in planning improvement actions for various time horizons to foster a modern maintenance practice whose decision-making is more data-driven.","PeriodicalId":20627,"journal":{"name":"Production Planning & Control","volume":"95 1","pages":"1333 - 1349"},"PeriodicalIF":6.1000,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Planning & Control","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/09537287.2021.2010823","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract Maintenance is evolving due to the double-sided influence of the Asset Management paradigm and digitalization. In this evolution, assessing the maintenance management process status in terms of process completeness, information and data completeness and integration is paramount to boost reliable data-driven decision-making. Grounding on Design Science Research, a methodology is realized to favour the comparison of two data models, a reference one and a company-specific one, used as a means to evaluate the process status. In particular, the methodology embeds a reference data model for the maintenance management process. Both methodology and data model are artefacts tested and refined during action research in an automotive company willing to improve the maintenance management process. The application of both artefacts demonstrates that the company is facilitated in planning improvement actions for various time horizons to foster a modern maintenance practice whose decision-making is more data-driven.
期刊介绍：
Production Planning & Control is an international journal that focuses on research papers concerning operations management across industries. It emphasizes research originating from industrial needs that can provide guidance to managers and future researchers. Papers accepted by "Production Planning & Control" should address emerging industrial needs, clearly outlining the nature of the industrial problem. Any suitable research methods may be employed, and each paper should justify the method used. Case studies illustrating international significance are encouraged. Authors are encouraged to relate their work to existing knowledge in the field, particularly regarding its implications for management practice and future research agendas.
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