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

Abstract

Increasing digitalisation is fundamentally changing the understanding and possi-bilities of value creation as well as labour organisation. The systematic collection, storage and analysis of data is becoming a decisive competitive factor and is the basis for intelligent products, processes and production technology. This results in new competence requirements and roles in mechanical and plant engineering and in the manufacturing industry in general. Machine Learning in particular, as the basis of Artificial Intelligence, poses great challenges for companies, as the demand for experts, so-called Data Scientists, significantly exceeds the offer and furthermore, these experts rarely have the required domain knowledge - the core competences of manufacturing companies. In this context, the new job descrip-tion of the Citizen Data Scientist as a link between the most important disci-plines of information technology, domain knowledge and data science enters the focus of attention. The article presents a role model as a basis for team building and systematic development of required competences in the manufacturing in-dustry and combines the results of various research projects and industrial im-plementations. For this purpose, competences of the future are derived in sec-tion 1 and transferred into a transdisciplinary role model in section 2. Section 3 addresses the exemplary practical application in an industrial use case, while section 4 gives an outlook on the possibilities of target-oriented competence development for the individual roles and actors.