Data Analytics

High Performance Technology for SMEs

High Performance Technology for SMEs

Wie auch kleine und mittlere Unternehmen Simulationstechnologien nutzen können
Andreas Wierse
Efficient product development can be a decisive factor in a company’s competition - it quickly falls behind who has to cope without simulation technologies. For large companies, these have long since become part of everyday life. But for small and medium-sized companies, they often remain wishful thinking for reasons of capacity and know-how. But: They, too, can benefit from the advantages of simulation with the right support and appropriate training programs.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 3 | Pages 61-64
Machine Learning in Production

Machine Learning in Production

Application areas and freely available data sets
Hendrik Mende, Jonas Dorißen, Jonathan Krauß, Maik Frye, Robert Schmitt ORCID Icon
Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets. This article therefore answers the following questions: Which application areas in production offer the greatest potential for the use of ML? Which freely accessible data sets are suitable for gaining experience and which learning tasks are associated with them? What are best practices for the application areas?
Industrie 4.0 Management | Volume 35 | 2019 | Edition 4 | Pages 39-42 | DOI 10.30844/I40M_19-4_S39-42
Potentials and Obstacles for Data Analy-tics in Large Scale Manufacturing

Potentials and Obstacles for Data Analy-tics in Large Scale Manufacturing

Heiner Heimes, Achim Kampker, Ulrich Bührer, Stefan Krotil
Handling increasing complexity is a major challenge within the manufacturing industry. Methods from Industrie 4.0, e. g. data analytics, can support in reducing complexity. Currently, benefits of implementing data analytics within large scale manufacturing are limited. For this purpose, a study regarding the potentials and obstacles for data analytics in large scale manufacturing was conducted. The results of this study show the necessity of adaptive data availability, strategic prioritization as well as scalable data analytics in order for data analytics to be successful.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 1 | Pages 57-60 | DOI 10.30844/I40M_19-1_57-60
The Future of Manufacturing Data Analy-tics

The Future of Manufacturing Data Analy-tics

Implications for a Successful Data Exploitation in the Manufacturing Industry
Marian Wenking, Christoph Benninghaus, Sebastian Groggert
In accordance to the study “Manufacturing Data Analytics” published by the University of St. Gallen in cooperation with RWTH Aachen in 2017, various aspects of industrial data usage are examined. Different topics such as technical systems, implementation status and organizational approaches are analysed. While some companies are still in a launching stage, other companies are already able to make predictions through comprehensive data collection and exploitation. Thereby, they can significantly improve their efficiency in production.
Industrie 4.0 Management | Volume 33 | 2017 | Edition 4 | Pages 33-37