{"id":93079,"date":"2017-12-15T12:00:00","date_gmt":"2017-12-15T12:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/preventive-work-design-in-digitized-industrial-systems-a-cybernetic-design-approach-using-the-example-of-intralogistics\/"},"modified":"2017-12-15T12:00:00","modified_gmt":"2017-12-15T12:00:00","slug":"preventive-work-design-in-digitized-industrial-systems-a-cybernetic-design-approach-using-the-example-of-intralogistics","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/preventive-work-design-in-digitized-industrial-systems-a-cybernetic-design-approach-using-the-example-of-intralogistics\/","title":{"rendered":"Preventive Work Design in Digitized Industrial Systems"},"content":{"rendered":"

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
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Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Interoperable Data Access in Automotive Body Manufacturing<\/h4>\n
Deterministic integration of structured target parameters into tact-time production<\/div>
Tim Richter<\/a> \n \"ORCID<\/a>, Robert Weidner<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI has long been capable of analyzing production processes, yet why is it still so difficult to bring its insights back into production without intermediate steps? In automotive body-in-white mass production, the challenge is less a lack of data than the absence of holistic integration concepts that extend all the way to the machines. This paper demonstrates why bidirectionally communicative information systems are critical to addressing this challenge and identifies the design principles required to effectively integrate AI-generated results into production processes in the future. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 34-42 | DOI 10.30844\/I4SE.26.5.4<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Audio-Immersive\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Audio-Immersive Learning in Continuing Vocational Education<\/h4>\n
From linear audio playback to AI-supported conversational learning companions<\/div>
Tim Beichter<\/a>, Vanessa Hartmann<\/a>, Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Manuel Kaiser<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence is increasingly shaping continuing vocational education and training by enabling the personalization of individual learning experiences. At the same time, audio-based learning formats are attracting growing interest among learners because of their flexibility and suitability for workplace learning. However, a conceptual framework for AI-supported audio learning, as well as the potential of combining artificial intelligence with audio-based learning, has received little attention to date. This paper therefore presents a conceptual perspective on the design possibilities and educational potential of AI-supported audio learning formats. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 102-108 | DOI 10.30844\/I4SE.26.5.12<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Generative\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Generative AI in Organizational Knowledge Management<\/h4>\n
AI literacy as a prerequisite for augmentation<\/div>
Uta Wilkens<\/a> \n \"ORCID<\/a>, Valentin Langholf<\/a> \n \"ORCID<\/a>, Niklas Obermann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI 10.30844\/I4SE.26.5.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Explaining\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Explaining AI in Industrial Production in an Accessible Way<\/h4>\n
Requirements for AI demonstrators to promote acceptance<\/div>
Jennifer Link<\/a> \n \"ORCID<\/a>, Markus Harlacher<\/a> \n \"ORCID<\/a>, Colin Srebny<\/a>, Sascha Stowasser<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers a wide range of possibilities in industrial production, but it also presents challenges regarding employee acceptance. AI demonstrators are therefore of central importance, as they enable hands-on experience with AI. However, there has been a lack of systematically identified requirements for demonstrators that specifically promote acceptance and address negative emotions. Using a multi-stage research design, 69 requirements were identified, structured into functional requirements, quality requirements, and boundary conditions. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 6-14 | DOI 10.30844\/I4SE.26.5.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI-Based\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

AI-Based Building Inspection for Large Structures<\/h4>\n
A new approach to construction progress monitoring<\/div>
Jan Sender<\/a> \n \"ORCID<\/a>, Konrad Jagusch<\/a> \n \"ORCID<\/a>, Michael Geist<\/a> \n \"ORCID<\/a>, David Jericho<\/a> \n \"ORCID<\/a>, Christian Scharr<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 78-84 | DOI 10.30844\/I4SE.26.5.9<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Inclusive\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Inclusive Work System Design<\/h4>\n
Automation, standardization, and adaptability<\/div>
Sebastian Schlund<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The design of inclusive work systems is gaining importance due to demographic change and the increasing digital penetration of value creation processes. While traditional ergonomic approaches are based primarily on percentile logic and thus address only a portion of the user population, the integration of digital technologies opens up new possibilities for the dynamic and individualized adaptation of work systems. This article presents a conceptual framework for inclusive work system design that integrates standardization, automation, and adaptability. Methodologically, the article is based on a conceptual analysis of existing approaches from ergonomics and human-centered design. The article makes a theoretical contribution to the systematization of inclusive work system design and identifies areas of focus for further research and industrial practice. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI 10.30844\/I4SE.26.5.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

In the course of digitization, the complexity and instability of business networks are considerably increasing. This has also serious effects on intralogistics. Under VUCA conditions, intralogistic systems develop into open, decentralized and self-organization networks of information-processing units. They adapt to the dynamic changes of their environment, by increasing their inner and outer varieties in a preventive manner. Digital technologies are not in a position to adequately control non-deterministic logistic systems; this task is essentially left to the working man. In order to overcome the challenges, appropriate models and methods of preventive work design are developed in the PREVILOG project. The paper presents cybernetic basics and preventive work design principles.<\/p>\n","protected":false},"featured_media":96789,"menu_order":0,"template":"","categories":[],"tags":[79449],"product_cat":[79304],"topic":[67617,68206,67701],"technology":[],"knowhow":[],"industry":[],"writer":[81616,82787],"content-type":[],"potential":[],"solution":[67610],"glossary":[],"class_list":["post-93079","article","type-article","status-publish","has-post-thumbnail","tag-digitalisierung-en","product_cat-articles","topic-adaptability","topic-industry-4-0","topic-production-system","writer-dirk-marrenbach-en","writer-martin-braun-en","solution-logistik-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/braun_Praeventive-Arbeitsgestaltung-in-der-Industrie40_IM2017-6-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"In the course of digitization, the complexity and instability of business networks are considerably increasing. This has also serious effects on intralogistics. Under VUCA conditions, intralogistic systems develop into open, decentralized and self-organization networks of information-processing units. They adapt to the dynamic changes of their environment, by increasing their inner and outer varieties in a…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/93079","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/96789"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=93079"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=93079"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=93079"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=93079"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=93079"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=93079"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=93079"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=93079"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=93079"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=93079"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=93079"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=93079"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=93079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}