TY  - CONF
A1  - Schuh, Günther
A1  - Stroh, Max-Ferdinand
A1  - Benning, Justus
A2  - Kim, Duck Young
A2  - Romero, David
A2  - von Cieminski, Gregor
T1  - Case-Study-Based Requirements Analysis of Manufacturing Companies for Auto-ML Solutions
T2  - APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action
N2  - 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.
T3  - IFIP advances in information and communication technology - 663 
KW  - Machine Learning
KW  - AI
KW  - Auto-ML
Y1  - 2022
UR  - https://epub.fir.de/frontdoor/index/index/docId/1839
UR  - https://link.springer.com/chapter/10.1007/978-3-031-16407-1_6
SN  - 978-3-031-16407-1
SN  - 978-3-031-16406-4
SP  - 43
EP  - 50
PB  - Springer
CY  - Cham [u. a.]
ER  -