Autor: René Wöstmann

Enabler for the Digital Twin

Enabler for the Digital Twin

Requirements for Technical Documentation 4.0
Christian Koch, Lukas Schulte, René Wöstmann, Jochen Deuse ORCID Icon
The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 76-85
Transdisciplinary competence development for role models in data-driven value creation – The Citizen Data Scientist in the Centre of Industrial Data Science Teams

Transdisciplinary competence development for role models in data-driven value creation - The Citizen Data Scientist in the Centre of Industrial Data Science Teams

Univ.-Prof. Dr.-Ing. Jochen Deuse, Thorben Panusch, Lukas Schulte, René Wöstmann
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 ...
Industry 4.0 Science | 2021 | | DOI 10.30844/wgab_2021_3
Big Data Analytics in Order Management

Big Data Analytics in Order Management

Tapping into untapped potential in the highly varied world of small-batch production
René Wöstmann, Fabian Nöhring, Jochen Deuse ORCID Icon, Ralf Klinkenberg, Thomas Lacker
The advancing digitization leads to new possibilities for the design and digital support of business processes. In particular, non-R&D-intensive, mostly small and medium-sized enterprises, face great challenges in realizing these potentials. In the context of this article, various application scenarios are outlined. A detailed example of a non-R&D-intensive company shows how the procurement can be supported by the analysis and forecasting of relevant data, e.g. process data or the availability and costs of components, as well as the creation of the offer.
Industrie 4.0 Management | Volume 33 | 2017 | Edition 4 | Pages 7-11