{"id":103496,"date":"2024-02-20T14:15:20","date_gmt":"2024-02-20T13:15:20","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103496"},"modified":"2024-05-14T07:37:49","modified_gmt":"2024-05-14T05:37:49","slug":"sme","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/management\/sme\/","title":{"rendered":"SME"},"content":{"rendered":"\n\n\n
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Industry 4.0\u2014Progress and Digitalization in Limbo<\/h4>\n
Status of sustainable transformation and digitalization in production engineering<\/div>
Christian Donhauser<\/a> \n \"ORCID<\/a>, Daniel Riepl<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 56-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Frameworks for the Structural Integration of Artificial Intelligence<\/h4>\n
Comparing organizational approaches<\/div>
Sascha Stowasser<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 144-151 | DOI 10.30844\/I4SE.25.5.138<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Enabler for the Digital Twin<\/h4>\n
Requirements for Technical Documentation 4.0<\/div>
Christian Koch<\/a>, Lukas Schulte<\/a>, Ren\u00e9 W\u00f6stmann<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 4 | Pages 76-85<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Machine Learning to Promote Sustainability\u00a0<\/h4>\n
Company analysis based on expert interviews<\/div>
Matthias Weigold<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 44-51<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Introduction of Machine Learning in Production<\/h4>\n
An SME-specific, holistic guide<\/div>
Manuel Savadogo<\/a>, Malte Stonis<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 88-95<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Setting Up Assembly Assistance Systems<\/h4>\n
System for the efficient configuration of assembly instructions and assistance functions<\/div>
Dennis Keiser<\/a>, Dario Niermann<\/a> \n \"ORCID<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 32-39<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Large Language Models (LLM) in Production<\/h4>\n
An analysis of the potential for transforming production processes in modern factories<\/div>
Pius Finkel<\/a> \n \"ORCID<\/a>, Peter Wurster<\/a> \n \"ORCID<\/a>, Robin Radler<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 48-55 | DOI 10.30844\/I4SE.24.6.48<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Solutions for SMEs\u2019 Circularity Transition<\/h4>\n
Examples from the textile industry<\/div>
Markus Winkler<\/a>, Dieter Stellmach<\/a>, Guido Grau<\/a>, Marcus Winkler<\/a>, Meike Tilebein<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The EU Strategy for sustainable and circular textiles aims to reduce the industry\u2019s 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 5 | Pages 26-33 | DOI 10.30844\/I4SE.24.5.26<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Modular Learning Factories for Industry 4.0<\/h4>\n
Acquisition of a target-oriented acton competence to accelerate industrial implementation<\/div>
Maximilian Dommermuth<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 4 | Pages 24-30 | DOI 10.30844\/I4SE.24.4.24<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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