Branche: SME

Introduction of Machine Learning in Production

Introduction of Machine Learning in Production

An SME-specific, holistic guide
Manuel Savadogo, Malte Stonis ORCID Icon, Peter Nyhuis ORCID Icon
Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 88-95
Setting Up Assembly Assistance Systems

Setting Up Assembly Assistance Systems

System for the efficient configuration of assembly instructions and assistance functions
Dennis Keiser, Dario Niermann ORCID Icon, Michael Freitag ORCID Icon
In industrial assembly, humans are working more closely with machines due to assembly assistance. However, despite their great potential, the implementation of digital systems is time-consuming, which entails high training requirements. Small and medium-sized businesses, in particular, are reaching their limits. A newly developed setup system is designed to facilitate the introduction and use of such assembly assistance systems and increase their acceptance.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 32-39
Large Language Models (LLM) in Production

Large Language Models (LLM) in Production

An analysis of the potential for transforming production processes in modern factories
Pius Finkel ORCID Icon, Peter Wurster ORCID Icon, Robin Radler
The rapid development of generative artificial intelligence is opening up new avenues for the manufacturing industry amid a shortage of skilled workers. Large language models can potentially make production processes in medium- sized businesses more efficient. But how exactly is this potential measured? Key areas of application such as communication, training and knowledge management show why a lot depends on employee acceptance.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 48-55 | DOI 10.30844/I4SE.24.6.48
I4S 6/2024: Machine Learning

I4S 6/2024: Machine Learning

A technology with optimization potential in terms of efficiency, transparency and sustainability
Machine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application.
Digital Solutions for SMEs’ Circularity Transition

Digital Solutions for SMEs’ Circularity Transition

Examples from the textile industry
Markus Winkler, Dieter Stellmach, Guido Grau, Marcus Winkler, Meike Tilebein ORCID Icon
The EU Strategy for sustainable and circular textiles aims to reduce the industry’s environmental impact while at the same time increasing its competitiveness. In this transition towards circularity, firms in the highly fragmented textile value chains need solutions that help overcome barriers and provide support. This paper presents digital solutions that are particularly suited for SMEs and that have been developed with public funding. It aims at encouraging SMEs, not only from the textile industry, to specify their individual transition paths towards circularity and to use digitalization to foster implementation.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 26-33 | DOI 10.30844/I4SE.24.5.26
I4S 5/2024: Double Transformation

I4S 5/2024: Double Transformation

Integrating digital and ecological change in the world of work
Change is necessary for companies to maintain their competitive edge—both digital and ecological change. But while external support is at hand, the drive for change must come from companies themselves. In this issue of Industry 4.0 Science, experts of the Academic Society for Work and Industrial Organization discuss how the real-world application of innovative technologies lead to resource-efficient manufacturing.
Modular Learning Factories for Industry 4.0

Modular Learning Factories for Industry 4.0

Acquisition of a target-oriented acton competence to accelerate industrial implementation
Maximilian Dommermuth ORCID Icon
Industry 4.0 requires new teaching content due to its innovation potential. Skills profiles currently in demand often aren't reflected in vocational and tertiary education. Additionally, conventional further education and training often costs considerably money and time. Tailor-made learning opportunities and teaching targeted problem-solving skills in a modular learning factory are a more effective approach.
Industry 4.0 Science | Volume 40 | 2024 | Edition 4 | Pages 24-30 | DOI 10.30844/I4SE.24.4.24
Learning Factories as Innovative Training Locations for SMEs

Learning Factories as Innovative Training Locations for SMEs

Qualitative analysis of concepts and cooperations
Kathleen Warnhoff ORCID Icon, Simon Dabrowski ORCID Icon, Lea Müller-Greifenberg, Denise Gramß, Monika Stricker
In the context of Industry 4.0, learning factories are important places for company-based learning. Studies show that they have continued to develop since their emergence and are no longer limited to vocational and academic education. This leads to the question of how much the concept of the learning factory represents an innovative approach to further training in small and medium-sized enterprises (SMEs). This article focuses on three selected learning factories relevant to continuing education that were analyzed using qualitative methods with regard to their concepts and cooperation. The findings are embedded in a theoretical framework that links the scientific discussion on learning locations and educational cooperation. The empirical findings from three learning factories illustrate relevant learning locations for continuing education in SMEs.
Industry 4.0 Science | Volume 40 | 2024 | Edition 4 | Pages 32-41
Digital Platform Frameworks for Manufacturing Companies

Digital Platform Frameworks for Manufacturing Companies

A review
Marcel Rojahn ORCID Icon
In recent years, digital platforms have established themselves as a central concept in the IT field. Due to the wide variety of digital platforms available on the market, there is still a need for clear comparison with criteria to enable interested parties to select, change, operate and further develop these platforms. The following paper aims to contribute to the facilitation of this comparison by undertaking a systematic literature review of digital platform frameworks in the context of the Industrial Internet of Things (IIOT) for manufacturing companies and thus providing a basis for a number of potential ways to effectively compare current digital platforms and ecosystems.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 8-15 | DOI 10.30844/I4SE.24.2.8
Lean Empowerment in the Digital Ecosystem

Lean Empowerment in the Digital Ecosystem

Translating cultural values into technical requirements
Sabrina Karch ORCID Icon, Frank Bertagnolli ORCID Icon, Arndt Lüder ORCID Icon
With the advent of digitalization, prevailing paradigms – such as product centricity, face-to-face collaboration and hierarchical structures – are giving way to the vision of data-driven business models, digital, collaborative ecosystems and an agile, holacratic way of working in flat hierarchies and self-managing teams. Collaboration is made possible through the use of software solutions. In addition to adapted management concepts, the digital space also requires a digital cultural understanding on part of the companies involved. Lean empowerment is a pioneering approach to collaboration based on cultural values. In expert workshops, ideas were developed to explore how these values can be lived in a digital culture and thus in terms of global digital collaboration. This article presents concrete solutions from which requirements for digital collaboration and for implementation within IT structures and software solutions in particular can be derived.
Industry 4.0 Science | Volume 40 | 2024 | Edition 2 | Pages 32-39 | DOI 10.30844/I4SE.24.2.32
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