{"id":103492,"date":"2024-02-20T14:23:30","date_gmt":"2024-02-20T13:23:30","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103492"},"modified":"2025-02-10T17:45:08","modified_gmt":"2025-02-10T16:45:08","slug":"business-models","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/management\/business-models\/","title":{"rendered":"Business Models"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Ideating\n <\/picture>\n <\/div>\n
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Ideating Ethical AI Business Models<\/h4>\n
A dual card approach for the ethical development of AI business models<\/div>
Marie-Christin Barton<\/a> \n \"ORCID<\/a>, Lisa Skrzyppek<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, J\u00fcrgen Mazarov<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI opens up entirely new forms of value creation, but most business model tools have not kept pace. They overlook both the strategic potential that AI holds and the ethical challenges that it introduces. This study introduces a dual-card toolkit that helps interdisciplinary teams design AI-enabled business models with built-in ethical reflection. The key insight: to harness AI responsibly, we must rethink how we innovate, starting from the business model itself. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 40-49 | DOI 10.30844\/I4SE.26.1.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Quiz:\n <\/picture>\n <\/div>\n
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Quiz: Manufacturing in Space<\/h4>\n
Test your knowledge!<\/div> <\/td>\n <\/tr>\n <\/table>\n
\nWeightless production\u2014just science fiction or already reality? Thanks to new space technologies, the first production processes are now emerging in space that enable materials and structures to be created that are virtually impossible to manufacture on Earth. From ultra-pure fibers to 3D printing of organs, weightlessness is opening up completely new perspectives for industries\u2014and bringing space manufacturing closer to the present than many people think. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Human-Centered\n <\/picture>\n <\/div>\n
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Human-Centered AI-Paired Work Systems<\/h4>\n
Integrating GenAI and the human factor in work system theory<\/div>
Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Udo-Ernst Haner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The work system is the key unit of analysis within the discipline of human factors\/ergonomics (HFE); it is also considered a fundamental element for value creation within other domains. Its concept is based on sociotechnical systems theory and, within HFE, it conveys a distinctly human-centered perspective. So far, work system models have focused on one or several people working within a defined setting as the only (intelligent) actors within the system. The introduction of generative artificial intelligence (genAI) into work systems, particularly as an intelligent and autonomous actor (agent) with potentially specific social abilities and personality traits, calls for reconceptualization. This article elaborates on the new requirements related to the introduction of genAI and develops a human-centered AI-paired work system model that recognizes the significantly expanded capabilities of AI-enabled collaborative social robots. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 38-48 | DOI 10.30844\/I4SE.25.5.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Smart Business Models in Intralogistics<\/h4>\n
A service-oriented approach to customized logistics solutions<\/div>
Anja Wiebusch<\/a>, Niklas Wilkowski<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Equipment-as-a-Service (EaaS) enables logistics companies to offer their customers tailored solutions, helping them to remain flexible and reduce costs as well as risks even in difficult times. Customers no longer pay for the object itself but only for the service provided, such as the usage time of a forklift truck. This allows them to focus on their core competencies and convert high investment costs into more flexible operating costs [1]. High capital commitment and the risk of underutilization of machines can thus be avoided and transferred to the logistics provider. This article examines the adjustments that logistics providers must make to accommodate this business model as well as some possible use cases. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 30-35<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Machine\n <\/picture>\n <\/div>\n
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Machine Learning to Promote Sustainability\u00a0<\/h4>\n
Company analysis based on expert interviews<\/div>
Niklas Bode<\/a> \n \"ORCID<\/a>, Lukas Nagel<\/a> \n \"ORCID<\/a>, Oskay Ozen<\/a> \n \"ORCID<\/a>, 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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\n \n \n \n \"Distributed\n <\/picture>\n <\/div>\n
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Distributed Application Integration in Industry<\/h4>\n
Employing microservices for enterprise application integration (EAI)<\/div>
Jan-Peer Rudolph<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In line with current digital transformations, the number of software applications in use by companies is continuously increasing. This particularly affects industrial enterprises, which face challenges due to their often complex business processes. A holistic and sustainable integration of these business processes requires a strong link between the different information systems used. In this context, application integration, also known as enterprise application integration (EAI), is becoming more important. Modern approaches such as the use of microservices offer a particularly flexible and efficient solution for seamlessly connecting different applications and thus promoting the agility and scalability of a company\u2019s IT landscape. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 1 | Pages 74-80<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Computer\n <\/picture>\n <\/div>\n
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Computer Use in Industrial Business Processes<\/h4>\n
A systematic literature review of the last 40 years<\/div>
Norbert Gronau<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The most important value-adding industrial business processes are product creation and order processing. For 40 years now, Industry 4.0 Science and its predecessor titles have supported the development and implementation of the software used in these industrial business processes. In honor of the journal\u2019s 40th anniversary, a systematic literature review is carried out to identify trends that played a role in the past but no longer do today. The research provides some surprising insights into the topics that have been important in the last 40 years \u2013 as well as those that will remain relevant in the future. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 1 | Pages 8-14 | DOI 10.30844\/I4SE.25.1.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Improving\n <\/picture>\n <\/div>\n
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Improving Social Media Moderation with Generative Language Models<\/h4>\n
Study on the detection and correction of disinformation<\/div>
Anton Schegolev<\/a>, Maximilian Ambros<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Fake news are increasingly dominating the digital world. The question arises: Can modern technologies reverse this trend? The article highlights the potential of the GPT-4o language model for identifying fake news in online comments and news articles and for correcting false information. With impressive accuracy, the model shows how language technology can combat misinformation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 72-79 | DOI 10.30844\/I4SE.24.6.72<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Can\n <\/picture>\n <\/div>\n
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Can Artificial Intelligence (AI) Act as an Enabler for Industry 4.0 (4IR)?<\/h4>\n
Impacts on the maturity level of Industry 4.0 technologies<\/div>
Dennis Richter<\/a>, Mildred Doe<\/a>, Steffen Kinkel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence is often mentioned often mentioned in the same context as Industry 4.0, but the exact role of AI is unclear. Is AI just another 4IR technology or an essential \"enabler\" for other 4IR technologies? Six experts assess the impact of AI on 41 4IR technologies. AI could indeed be a decisive factor in unleashing the full potential of Industry 4.0. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 80-87 | DOI 10.30844\/I4SE.24.6.80<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Promoting\n <\/picture>\n <\/div>\n
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Promoting Agility in Entrepreneurial Innovation<\/h4>\n
A competence platform for small and medium-sized enterprises<\/div>
Justus von Geibler<\/a> \n \"ORCID<\/a>, Julius Piwowar<\/a> \n \"ORCID<\/a>, Patrik Fr\u00f6hlich<\/a> \n \"ORCID<\/a>, Filiz Meidrodt<\/a> \n \"ORCID<\/a>, Dominik Lenz<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Companies with traditional working and manufacturing structures face the challenge of progressive digitalization and internationalization. In order to adapt, many companies aim to develop digital and agile working skills and competences. This paper describes the conception of a digital platform to promote agility in innovation of small and mediumsized enterprises (SMEs) in structurally weak regions and to contribute to their innovativeness and future viability. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 6 | Pages 27-31 | DOI 10.30844\/IM_23-6_27-31<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n9<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103482,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103492","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103492","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103492"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103492\/revisions"}],"predecessor-version":[{"id":107783,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103492\/revisions\/107783"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103482"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103492"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}