{"id":113977,"date":"2026-06-10T13:44:20","date_gmt":"2026-06-10T11:44:20","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=113977"},"modified":"2026-06-10T16:45:29","modified_gmt":"2026-06-10T14:45:29","slug":"human-models-optimized-assembly","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/human-models-optimized-assembly\/","title":{"rendered":"Optimized Manual Processes in Automotive Production"},"content":{"rendered":"

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The automotive industry is undergoing a period of profound transformation characterized by increasing product diversity, shorter product lifecycles, and rising demands for flexibility and sustainability [1]. Among other things, these developments are leading to ever-increasing complexity in production planning and presenting new challenges for planning managers. The need for end-to-end, digitally supported planning is correspondingly high in order to efficiently manage the flow of information between development, planning, and production and to make informed decisions as early as the initial development phases [2]. Despite increasing automation, assembly is still characterized by …<\/div>\n

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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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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 \"Experiential\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Experiential Knowledge Powered by AI\u00a0<\/h4>\n
Practical insights from industry<\/div>
Martin Schmauder<\/a> \n \"ORCID<\/a>, Gritt Ott<\/a> \n \"ORCID<\/a>, Bianca Windisch<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The use of experiential knowledge is a key success factor for companies. Based on four corporate case studies, this article analyzes the technical, organizational, and personnel challenges associated with the use of retrieval-augmented generation (RAG) systems. The results show that the success of such systems depends on the strategic development and maintenance of the knowledge base, as well as on employee engagement. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 128-135 | DOI 10.30844\/I4SE.26.5.15<\/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
\n \n
\n \n \n \n \"Work\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Work Design in the Use of Autonomous Systems<\/h4>\n
Addressing the shortage of skilled workers<\/div>
Tim Jeske<\/a> \n \"ORCID<\/a>, Sascha Stowasser<\/a> \n \"ORCID<\/a>, Nicole Ottersb\u00f6ck<\/a>, Sebastian Terstegen<\/a>, Rasmus Adler<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 44-50 | DOI 10.30844\/I4SE.26.5.5<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"MTM\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

MTM Analyses with AI and Rule-Based Algorithms<\/h4>\n
An approach to interpreting textual process descriptions<\/div>
Constantin Eckart<\/a> \n \"ORCID<\/a>, Martin Benter<\/a> \n \"ORCID<\/a>, Peter Kuhlang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n MTM methods are a proven standard for analyzing and designing human work processes. However, work planners continue to face challenges in applying these methods correctly and efficiently. Artificial intelligence \u2014 particularly in the case of large language models \u2014 holds significant potential for combatting these challenges. However, the use of AI raises legitimate questions regarding the reliability and traceability of the results. The approach presented here combines LLMs with a rule-based algorithm to extract the information required for MTM analyses from textual process descriptions such as work instructions. This information is then translated into MTM analyses in accordance with the MTM methodology. This approach ensures that the resulting analyses can be transparently traced back to the original input data by the user. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 62-69 | DOI 10.30844\/I4SE.26.5.7<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

In the manufacturing industry, the integration of digital human models into the product development and manufacturing process is becoming increasingly important. Particularly in assembly, which is characterized by a high proportion of manual tasks, motion simulations enable a realistic representation of human work and thus make a significant contribution to the evaluation of motion economy, process validation, and efficiency improvement. However, widespread application in production planning faces various challenges, such as the high initial effort required to create human simulations as well as volatile planning conditions. This article presents a practice-oriented solution from the automotive assembly sector that enables the creation of simulations with reduced effort as well as their early and consistent use in the planning process.<\/p>\n","protected":false},"featured_media":113805,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[80275,84171,72429,79661,84285,79629,84344,80214,85879],"product_cat":[79304],"topic":[68005,79333],"technology":[67717],"knowhow":[],"industry":[69251,79494],"writer":[85680,85681,81417,85679],"content-type":[83932],"potential":[],"solution":[69358],"glossary":[],"class_list":["post-113977","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-assembly-en","tag-digital-factory-en","tag-digital-human-models","tag-digitale-fabrik-en","tag-ergonomics-en","tag-montage-en","tag-production-planning-en","tag-simulation-en","tag-time-analysis","product_cat-articles","topic-automation","topic-process-optimization","technology-simulation-en","industry-automotive-en","industry-manufacturing-en","writer-barbara-brockmann","writer-beate-stoffels","writer-jochen-deuse-en","writer-tobias-jurk","content-type-article","solution-assembly","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Brockmann_AdobeStock_1505788468_vegefox.com_-64x36.webp",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"In the manufacturing industry, the integration of digital human models into the product development and manufacturing process is becoming increasingly important. Particularly in assembly, which is characterized by a high proportion of manual tasks, motion simulations enable a realistic representation of human work and thus make a significant contribution to the evaluation of motion economy,…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/113977","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\/113805"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=113977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=113977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=113977"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=113977"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=113977"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=113977"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=113977"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=113977"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=113977"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=113977"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=113977"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=113977"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=113977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}