Industrial Data Science - Interdisciplinary Competence for Machine Learning in Industrial Production

Abstract

Within the increasing digitalization, the widespread application of modern information and communication technologies and the technological ability to systematically and comprehensively capture and store data allow to build data storages of unprecedented size and quality. The evaluation and efficient use of the implicit knowledge in the data to support decision-making processes is becoming increasingly significant in manufacturing companies. Thus, new requirements arise for the qualification and competence development to efficiently solve engineering applications and issues in manufacturing and assembly with advanced data-driven methods. This paper presents the contribution of a qualification concept for Machine Learning in industrial production that has been realised within a recent research project funded by the Federal Ministry of Education and Research. This concept has been designed and validated within the university curriculum for graduate students in mechanical and industrial engineering, computer science and statistics. Taking into account the current challenges in manufacturing and assembly, the contribution of this enhanced interdisciplinary competence development can be considered quite significant. The results, findings, and future enhancements are presented within this paper.

Towards artificial intelligence in production: A competence profile for shop floor managers

“…Although numerous researchers dealt with competences in regard of AI in vocational education [9][10][11][12][13], to the best of our knowledge there is no publication directly focusing on shop floor managers and their everyday needs. For example, Rokoss et al describe learning factory courses for AI implementation in production [14].…”

This is how we learn - A Best Practice Case of Qualification in SMEs for Work 4.0

“…In the survey, the companies indicate a high importance for the competence categories of ML, ICT and domain knowledge, but also social and personal competences. (Schulte et al 2020). value creation…”

“…Based on these findings (Bauer et al 2018) derive an approach to enable ML in industrial production by developing the tripartite Industrial Data Science (InDaS) model (Fig 1) as the basis for a transdisciplinary course of the same name at TU Dortmund University as part of the research project InDaS Figure 1: Industrial Data Science as a transdisciplinary approach (Bauer et al 2018;Schulte et al 2020) 1.2.…”

Konzept zur zielgerichteten Kompetenzentwicklung für Initiativen des Maschinellen Lernens

“…Reckelkamm und Deuse (2021) leiten basierend auf einer Literaturrecherche die Kategorien der Fach-, Sozial-, Methoden-und Selbstkompetenz als wesentliche Handlungsfelder ab. Detailliertere Analysen zu erforderlichen ML-Kompetenzen in der produzierenden Industrie bieten Schulte et al (2020), Zschech et al (2018) und Bauer et al (2018).…”

“…Um diese Lücke zu schließen, wurde basierend auf den Arbeiten von Schulte et al (2020), Stark et al (2019) 2016) sowie dem Stufen-Taxonomie-Modell nach Bloom und Engelhart (1976). In Abb.…”