Application Areas

Frameworks for the Structural Integration of Artificial Intelligence

Frameworks for the Structural Integration of Artificial Intelligence

Comparing organizational approaches
Sascha Stowasser
Artificial intelligence is increasingly implemented in companies, but often without clear organizational anchoring. This article evaluates centralized, decentralized, hybrid, and project-based frameworks for the structural integration of artificial intelligence in corporate organizations. A decision table provides guidance for selecting suitable models. In the conclusion, further open research questions are posed.
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 144-151 | DOI 10.30844/I4SE.25.5.138
A Machine Learning Compass for Product Development and Production

A Machine Learning Compass for Product Development and Production

Identification and planning of machine learning algorithms in manufacturing companies
Alexander Jacob, Carmen Krahe, Rebecca Funk, Gisela Lanza ORCID Icon
Engineers are often uncertain about the application of machine learning (ML) due to the amount of different machine learning methods and the complexity of modeling. Thus, the use of ML applications in manufacturing companies remains behind the technical possibilities. This paper presents an intuitive ML guideline for engineers to reduce this uncertainty. The guideline comprises a process model with AI-based solutions to common problems of product development and production. An industrial example is used to demonstrate the functionality and the possibilities of the guide.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 2 | Pages 7-11
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