{"id":114044,"date":"2026-06-08T15:05:40","date_gmt":"2026-06-08T13:05:40","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=114044"},"modified":"2026-06-10T17:31:25","modified_gmt":"2026-06-10T15:31:25","slug":"site-assessment-for-flexible-intralogistics-in-brownfield-sites","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/site-assessment-for-flexible-intralogistics-in-brownfield-sites\/","title":{"rendered":"Site Assessment for Flexible Intralogistics in Brownfield Sites"},"content":{"rendered":"

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Industrial companies are under increasing pressure to adapt their production and logistics structures at ever-shorter intervals. Key drivers include the introduction of new products and technologies, portfolio changes, increasing product variety, and growing cost and competitive pressures [1]. As a result, factory planning is evolving from a one-time task into a recurring decision-making process. In practice, adjustments to production and logistics systems are predominantly implemented within existing facilities, commonly referred to as brownfield sites [2]. This creates conflicting objectives between product requirements, production resources, technical building equipment, and structural conditions [1]. …<\/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
Expert<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\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 \"Competence\n <\/picture>\n <\/div>\n
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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
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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 \"Data-Driven\n <\/picture>\n <\/div>\n
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Data-Driven Supplier Selection in Industry 4.0<\/h4>\n
Towards an industrial platform for selection and configuration<\/div>
Purushothaman Ganesh<\/a> \n \"ORCID<\/a>, Baris E. Albayrak<\/a> \n \"ORCID<\/a>, J\u00f6rg Franke<\/a> \n \"ORCID<\/a>, Till Sindel<\/a> \n \"ORCID<\/a>, Jens F\u00fcrst<\/a>, Sebastian Lang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844\/I4SE.26.4.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
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Digital Factory Planning for Startups<\/h4>\n
A simulation-based production structure design<\/div>
Herwig Winkler<\/a> \n \"ORCID<\/a>, Tobias Isau<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 68-75<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

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.<\/p>\n","protected":false},"featured_media":114033,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[75575,69732,79662,80111,80205,85899,85898],"product_cat":[79304],"topic":[68760,79371],"technology":[67599],"knowhow":[],"industry":[79494],"writer":[82516,85893,85894],"content-type":[83932],"potential":[68057],"solution":[],"glossary":[],"class_list":["post-114044","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-brownfield","tag-decision-support","tag-fabrikplanung-en","tag-factory-planning-en","tag-intralogistics-en","tag-relocatability","tag-space-evaluation","product_cat-articles","topic-factory-design","topic-logistics","technology-analytics-en","industry-manufacturing-en","writer-carsten-feldmann-en","writer-jolanda-schierbaum","writer-lars-renhof","content-type-article","potential-management-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\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-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":"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…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/114044","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\/114033"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=114044"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=114044"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=114044"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=114044"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=114044"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=114044"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=114044"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=114044"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=114044"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=114044"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=114044"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=114044"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=114044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}