Case-Study-Based Requirements Analysis of Manufacturing Companies for Auto-ML Solutions
- Methods of machine learning (ML) are difficult for manufacturing companies to employ productively. Data science is not their core skill, and acquiring talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratizing machine learning by introducing elements such as low-code or no-code functionalities into its model creation process. Due to the dynamic vendor market of Auto-ML, it is difficult for manufacturing companies to successfully implement this technology. Different solutions as well as constantly changing requirements and functional scopes make a correct software selection difficult. This paper aims to alleviate said challenge by providing a longlist of requirements that companies should pay attention to when selecting a solution for their use case. The paper is part of a larger research effort, in which a structured selection process for Auto-ML solutions in manufacturing companies is designed. The longlist itself is the result of six case studies of different manufacturing companies, following the method of case study research by Eisenhardt. A total of 75 distinct requirements were identified, spanning the entire machine learning and modeling pipeline.
Verfasserangaben: | Günther SchuhORCiDGND, Max-Ferdinand Stroh, Justus Benning |
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URL: | https://link.springer.com/chapter/10.1007/978-3-031-16407-1_6 |
DOI: | https://doi.org/10.1007/978-3-031-16407-1_6 |
ISBN: | 978-3-031-16407-1 |
ISBN: | 978-3-031-16406-4 |
Titel des übergeordneten Werkes (Englisch): | APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action |
Schriftenreihe (Bandnummer): | IFIP advances in information and communication technology (663) |
Verlag: | Springer |
Ort: | Cham [u. a.] |
Herausgeber*in: | Duck Young Kim, David Romero, Gregor von Cieminski |
Dokumentart: | Konferenzveröffentlichung |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 19.09.2022 |
Datum der Erstveröffentlichung: | 19.09.2022 |
Datum der Freischaltung: | 17.01.2023 |
Freies Schlagwort / Tag: | AI; Auto-ML; Machine Learning |
Erste Seite: | 43 |
Letzte Seite: | 50 |
FIR-Nummer: | SV7596 |
Konferenzname: | APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action |
Konferenzort: | Gyeongju, Korea |
Konferenzzeitraum: | 25.09.2022-29.09.2022 |
Institut / Bereiche des FIR: | FIR e. V. an der RWTH Aachen |
Informationsmanagement | |
DDC-Klassifikation: | 6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften |