Marius Syberg, Nikolai West, Jörn Schwenken, Rebekka Adams, Jochen Deuse
{"title":"A requirement-driven approach for competency-based collaboration in industrial data science projects","authors":"Marius Syberg, Nikolai West, Jörn Schwenken, Rebekka Adams, Jochen Deuse","doi":"10.4995/ijpme.2024.19123","DOIUrl":null,"url":null,"abstract":"The ongoing digitization of online learning resources has led to a proliferation of collaboration platforms for specific areas of application and disciplines. Simultaneously, especially manufacturing companies need to gain and secure knowledge in the field of Industrial Data Science (IDS) and to collaborate with partners to form a competitive value chain. In this paper, collaborative and competency-based requirements for applying industrial data analytics are adapted into specifications for implementing a collaboration platform. The currently absent requirements of IDS projects are defined and then turned into platform-specific functions. In an ongoing research project the functions are applied in an online platform. The usage in a system of dynamic value networks validates the defined requirements in a practical environment. The innovation of the platform is its clear focus on IDS project practitioners, who are typically comprised of several different domains. It secures a long-term use of deployed data analytics solutions in the industrial environment. The first version of the developed collaboration platform is available online and still in validation.","PeriodicalId":41823,"journal":{"name":"International Journal of Production Management and Engineering","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Production Management and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4995/ijpme.2024.19123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The ongoing digitization of online learning resources has led to a proliferation of collaboration platforms for specific areas of application and disciplines. Simultaneously, especially manufacturing companies need to gain and secure knowledge in the field of Industrial Data Science (IDS) and to collaborate with partners to form a competitive value chain. In this paper, collaborative and competency-based requirements for applying industrial data analytics are adapted into specifications for implementing a collaboration platform. The currently absent requirements of IDS projects are defined and then turned into platform-specific functions. In an ongoing research project the functions are applied in an online platform. The usage in a system of dynamic value networks validates the defined requirements in a practical environment. The innovation of the platform is its clear focus on IDS project practitioners, who are typically comprised of several different domains. It secures a long-term use of deployed data analytics solutions in the industrial environment. The first version of the developed collaboration platform is available online and still in validation.