{"id":108504,"date":"2025-04-15T12:00:00","date_gmt":"2025-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=108504"},"modified":"2026-05-04T13:29:03","modified_gmt":"2026-05-04T11:29:03","slug":"small-batch-body-in-white","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/small-batch-body-in-white\/","title":{"rendered":"Boosting Competitiveness in Small Batch Production"},"content":{"rendered":"

<\/p>\n

Challenges in the\u00a0scalable and flexible (body-in-white) production line With shorter life cycles and greater product variety, the automotive industry needs higher flexibility and reconfigurability of its production systems to evolve at market speed. The production of sheet metal in body-in-white, which is used in car manufacturing, precedes painting and integrating elements like motor and chassis into the structure [1]. The latter differs by the manufacturing steps but has fixed sequences. In each body-in-white production step, the manufacturing processes will define the geometry and …<\/div>\n

<\/p>\n

\n
\n

Access limited<\/center><\/h2>\n

You are currently not logged in \/ not yet registered<\/strong>.<\/p>\n

To read the content in full, you must have an appropriate subscription. Alternatively, you can also obtain access by paying a one-off fee.<\/p>\n

\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

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
Expert<\/td>\n−<\/td>\n26,10 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
\n \t \n
\t\t

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

Systematically Planning Vertical Factories<\/h4>\n
Developing factory types as a foundation for intelligent planning support<\/div>
Nikolaus Kremslehner<\/a> \n \"ORCID<\/a>, Sebastian Schlund<\/a> \n \"ORCID<\/a>, Wilfried Sihn<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Vertical factories reduce land use and harness the advantages of urban production. However, their multi-story structure significantly increases planning complexity. To counter such effects, this paper presents a typology that combines proven solutions for recurring challenges in the planning and operation of vertical factories. Relevant characteristics were examined using real-world case studies and structured within a morphological framework. This framework was consolidated into factory types that provide guidance for conceptual planning and serve as the foundation for intelligent planning support. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 68-76 | DOI 10.30844\/I4SE.26.5.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Competence\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Competence Assurance for the Verification and Validation of Simulation Models<\/h4>\n
Requirements for AI-supported assistance in production and logistics<\/div>
Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Felix \u00d6zkul<\/a> \n \"ORCID<\/a>, Robin Sutherland<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Can artificial intelligence be used to assess the validity and credibility of simulation models and data for production and logistics? And what requirements must AI assistance meet to effectively support experts in verification and validation while simultaneously safeguarding their competence? In an online survey, simulation experts were asked regarding their expectations and needs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 24-33 | DOI 10.30844\/I4SE.26.5.3<\/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<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage.<\/p>\n","protected":false},"featured_media":108506,"menu_order":0,"template":"","categories":[],"tags":[79627],"product_cat":[79304],"topic":[67617,68760,68206,79371,79333,67701],"technology":[71524,68674,67717,67634],"knowhow":[],"industry":[69251,79494,79354],"writer":[83650],"content-type":[],"potential":[67894,67658],"solution":[69358,67610,78673,67687,67776,67577,67824],"glossary":[],"class_list":["post-108504","article","type-article","status-publish","has-post-thumbnail","tag-industrie-4-0-en","product_cat-articles","topic-adaptability","topic-factory-design","topic-industry-4-0","topic-logistics","topic-process-optimization","topic-production-system","technology-additive-manufacturing","technology-robotics","technology-simulation-en","technology-tools","industry-automotive-en","industry-manufacturing-en","industry-smart-objects","writer-martin-manns-en","potential-innovation-en","potential-profitability","solution-assembly","solution-logistik-en","solution-logistics-technology","solution-process-management","solution-production-control","solution-production-planning","solution-risk-management","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/04\/AdobeStock_348747239-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/108504","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\/108506"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=108504"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=108504"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=108504"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=108504"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=108504"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=108504"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=108504"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=108504"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=108504"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=108504"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=108504"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=108504"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=108504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}