[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126871-en":3,"doc-seo-126871-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126871,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Toward the Sustainable Development of Machine Learning Applications in Industry 4.0 - Research Paper","Machine Learning (ML)-based applications are increasingly pursued to improve efficiency and resilience in digitized industrial environments, yet sustained deployment and operation beyond proofs-of-concept remain difficult and resource intensive in Industry 4.0 settings. A design science research approach is used to systematically identify deployment challenges across CRISP-ML process phases. The study is informed by 15 interviews with Industry 4.0 data science practitioners, followed by qualitative content analysis to derive design requirements and principles.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ECIS 2023 Research Papers | ECIS 2023 Proceedings |\n| --- | --- |\n| 5-11-2023\u003Cbr>Toward the Sustainable Development of Machine Applications in Industry 4.0\u003Cbr>Sara Ellenrieder\u003Cbr>Technical University of Darmstadt, ellenrieder@is.tu-darmstadt.de\u003Cbr>Nicolas Jourdan\u003Cbr>Technical University of Darmstadt, [nicolas.jourdan@tu-darmstadt.de](nicolas.jourdan@tu-darmstadt.de)\u003Cbr>Tobias Biegel\u003Cbr>Technical University of Darmstadt, [t.biegel@ptw.tu-darmstadt.de](t.biegel@ptw.tu-darmstadt.de)\u003Cbr>Beatriz B. Cassoli\u003Cbr>Technical University of Darmstadt, [b.cassoli@ptw.tu-darmstadt.de](b.cassoli@ptw.tu-darmstadt.de)\u003Cbr>Joachim Metternich\u003Cbr>Technical University of Darmstadt, [j.metternich@ptw.tu-darmstadt.de](j.metternich@ptw.tu-darmstadt.de)\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/ecis2023_rp](https://aisel.aisnet.org/ecis2023_rp) | Learning |\n\nRecommended Citation  \nEllenrieder, Sara; Jourdan, Nicolas; Biegel, Tobias; Cassoli, Beatriz B.; Metternich, Joachim; and Buxmann, Peter, \"Toward the Sustainable Development of Machine Learning Applications in Industry 4 .0\" (2023) . ECIS 2023 Research Papers. 261.  \n[https://aisel.aisnet.org/ecis2023_rp/261](https://aisel.aisnet.org/ecis2023_rp/261)  \nThis material is brought to you by the ECIS 2023 Proceedings at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2023 Research Papers by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nAuthors  \nSara Ellenrieder, Nicolas Jourdan, Tobias Biegel, Beatriz B. Cassoli, Joachim Metternich, and Peter Buxmann  \nThis article is available at AIS Electronic Library (AISeL): [https://aisel.aisnet.org/ecis2023_rp/261](https://aisel.aisnet.org/ecis2023_rp/261)  \nTOWARD THE SUSTAINABLE DEVELOPMENT OF MACHINE LEARNING APPLICATIONS IN INDUSTRY 4.0  \nResearch Paper  \nSara Ellenrieder, TU Darmstadt, Germany, ellenrieder@is.[tu-darmstadt.de](tu-darmstadt.de)[ ](tu-darmstadt.de)Nicolas Jourdan, TU Darmstadt, Germany, [n.jourdan@ptw.tu-darmstadt.de](n.jourdan@ptw.tu-darmstadt.de)[ ](n.jourdan@ptw.tu-darmstadt.de)Tobias Biegel, TU Darmstadt, Germany, [t.biegel@ptw.tu-darmstadt.de](t.biegel@ptw.tu-darmstadt.de)  \nBeatriz Bretones Cassoli, TU Darmstadt, Germany, [b.cassoli@ptw.tu-darmstadt.de](b.cassoli@ptw.tu-darmstadt.de)[ ](b.cassoli@ptw.tu-darmstadt.de)Joachim Metternich, TU Darmstadt, Germany, [j.metternich@ptw.tu-darmstadt.de](j.metternich@ptw.tu-darmstadt.de)[ ](j.metternich@ptw.tu-darmstadt.de)Peter Buxmann, TU Darmstadt, Germany, [buxmann@is.tu-darmstadt.de](buxmann@is.tu-darmstadt.de)  \nAbstract  \nAs the level of digitization in industrial environments increases, companies are striving to improve efficiency and resilience to unplanned disruptions through the development of Machine Learning (ML) -based applications. Still, sustainable deployment and operation beyond proofs-of-concept is a challenging and resource-intensive task in dynamic enviroments such as industry 4.0, often impeding practical adoption in the long-term and thus sustainable ML product development. In this work, we systematically identify these challenges based on the CRISP-ML process model phases by applying a design science research approach. To this end, we conducted 15 interviews with data science practitioners in industry 4.0. Following a qualitative content analysis, design requirements and design principles for the development and sustainable long-term deployment of ML systems are derived to address identified challenges such as robustness to, and management of data drift caused by timedependencies and machine/product differences, missing metadata, interfaces to other IT systems, expectation management, and MLOps guidelines.  \nKeywords: Machine Learning, Industry 4.0, Long-term Deployment, Desig","cbCaijTzCkpAfkIh","https://ap.wps.com/l/cbCaijTzCkpAfkIh","pdf",891015,1,17,"English","en",105,"# 1 Introduction\n## Machine learning adoption and proof-of-concept limits\n## Sustainability challenges in long-term ML operation\n## Industry 4.0 digitization and data sources","[{\"question\":\"What problem does the paper address for Industry 4.0 machine learning applications?\",\"answer\":\"It addresses the challenge of sustainable deployment and operation of ML systems beyond proofs-of-concept, which is difficult and resource intensive in dynamic Industry 4.0 environments.\"},{\"question\":\"How are the challenges identified in the research?\",\"answer\":\"The work uses a design science research approach and systematically derives challenges from the CRISP-ML process model phases.\"},{\"question\":\"What research method supports the resulting design requirements and principles?\",\"answer\":\"The study conducts 15 interviews with data science practitioners in Industry 4.0 and applies qualitative content analysis to derive design requirements and principles for long-term deployment.\"}]","Toward the Sustainable Development of Machine Learning Applications in Industry 4.0 - 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