{"id":103467,"date":"2024-02-20T13:06:55","date_gmt":"2024-02-20T12:06:55","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103467"},"modified":"2024-05-14T04:41:19","modified_gmt":"2024-05-14T02:41:19","slug":"factory-planning","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/factory-planning\/","title":{"rendered":"Factory Planning"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"Systematically\n <\/picture>\n <\/div>\n
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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
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\n \n \n \n \"AI-Based\n <\/picture>\n <\/div>\n
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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
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\n \n \n \n \"Site\n <\/picture>\n <\/div>\n
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Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n
Innovative decision support in practice<\/div>
Jolanda Schierbaum<\/a>, Carsten Feldmann<\/a>, Lars Renhof<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Conducting\n <\/picture>\n <\/div>\n
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Conducting Experiments in Hybrid Learning Factories<\/h4>\n
The example of the InTraLab Potsdam<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nIndustrial production is undergoing rapid transformation through digitalization, automation and cyber-physical systems, creating new competence requirements for employees. Learning factories provide experiential environments for developing these competences. This article presents the Industrial Transformation Lab (InTraLab) as a hybrid learning factory combining physical demonstrators and digital simulations. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Learning Factories for the Future of Manufacturing in Brazil<\/h4>\n
Advancing manufacturing through technology and skills development<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nManufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory F\u00e1brica do Futuroin S\u00e3o Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Implementation in Industrial Quality Control<\/h4>\n
A design science approach bridging technical and human factors<\/div>
Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation\/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844\/I4SE.26.1.112<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Applied\n <\/picture>\n <\/div>\n
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Applied AI for Human-Centric Assembly Workplace Design<\/h4>\n
An ethics-informed approach<\/div>
Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Michael Jonek<\/a> \n \"ORCID<\/a>, Sascha Niethammer<\/a>, Henning Vogler<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection\u00ae. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844\/I4SE.26.1.58<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Increasing\n <\/picture>\n <\/div>\n
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Increasing Resilience in Logistics with IT<\/h4>\n
Investigating supply chain risk management information systems<\/div>
Alexander Baur<\/a>, Jasmin Hauser<\/a>, Dieter Uckelmann<\/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>The blockage of the Suez Canal in 2021, caused by the accident involving the container ship Ever Given, clearly illustrates the need to design global supply chains in such a way that they can respond quickly to disruptions. In a volatile, uncertain, complex, and ambiguous (VUCA) environment, conventional logistics processes that focus on efficiency, and supply chain management methods in particular, are increasingly reaching their limits. Resilience, achieved through a combination of robustness and agility, is essential to ensure responsiveness. This article analyzes how risk management information systems (RMIS) can increase resilience. The analysis covers data availability, data transparency, modeling and simulation of risk scenarios, and the development of appropriate emergency action plans. Despite existing challenges in designing IT infrastructure, the measures mentioned have the potential to increase resilience in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 36-42<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"The\n <\/picture>\n <\/div>\n
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The Core Principles of the Digital Twin<\/h4>\n
Transformingorder processes and the automation pyramid<\/div>
Wilmjakob Herlyn<\/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>The digital twin [DT] is considered a key technology of Industry 4.0. Its basic concept is now being successfully applied in practice, as demonstrated by the commissioning of Mercedes' Factory56 in 2022. New identification technologies, tracking systems and communication solutions faciliate new ways of controlling production and managing material flows, particularly at the shop floor level. With precise technical data permanently available not only for products, but also for material availability and order fulfillment status, production processes can be managed more dynamically and efficiently. This is precisely where the concept of the DT comes into play, enabling the immediate use and evaluation of this data.Its relevance continues to grow, especially in the context of make-to-order production, the rising variety of product configurations, and the globalization of production and supply networks. This article introduces the basic concept of the DT and illustrates how it connects to ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 92-101<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Boosting\n <\/picture>\n <\/div>\n
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Boosting Competitiveness in Small Batch Production<\/h4>\n
Scalable and flexible body-in-white production line with collaborative mobile robots<\/div>
Walid Elleuch<\/a>, Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 60-67<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n7<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":113755,"parent":103459,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103467","page","type-page","status-publish","has-post-thumbnail","hentry"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-64x36.webp",64,36,true]},"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\/103467","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=103467"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103467\/revisions"}],"predecessor-version":[{"id":103468,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103467\/revisions\/103468"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103459"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/113755"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103467"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}