{"id":114108,"date":"2026-06-08T22:55:17","date_gmt":"2026-06-08T20:55:17","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=114108"},"modified":"2026-06-08T23:13:24","modified_gmt":"2026-06-08T21:13:24","slug":"digital-factory-planning-for-startups","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/digital-factory-planning-for-startups\/","title":{"rendered":"Digital Factory Planning for Startups"},"content":{"rendered":"

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Startups face unique challenges in factory planning. Unlike established companies, they tend to lack historical planning and process data, making it difficult to accurately determine capacity requirements. Furthermore, sales forecasts in young, dynamic markets are highly volatile, meaning that factory planning decisions must be made under conditions of high uncertainty. At the same time, startups usually operate with limited planning resources, facing barriers such as high acquisition costs for specialized software and insufficient know-how. Given the strong pressure to reduce time-to-market, planning errors in the physical environment can threaten the very existence of young companies. <\/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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Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Loam\n <\/picture>\n <\/div>\n
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Loam Construction and Wooden Shelving<\/h4>\n
A contribution to sustainability in warehouse logistics<\/div>
Viviano De Giacomo<\/a> \n \"ORCID<\/a>, Nathalie Fritsch<\/a> \n \"ORCID<\/a>, Jakob Kennert<\/a> \n \"ORCID<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 82-89<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Electric\n <\/picture>\n <\/div>\n
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Electric Trucks in Intermodal Terminal Pre- and Post-Carriage<\/h4>\n
Impact on terminal processes in combined road-rail freight transport<\/div>
Ralf Elbert<\/a> \n \"ORCID<\/a>, Samira Ghaneian Sebdani<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Electric trucks (e-trucks) play an important role in reducing CO\u2082 emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 70-77<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Assistance\n <\/picture>\n <\/div>\n
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Assistance for Simulation in Production and Logistics<\/h4>\n
A literature-based classification<\/div>
Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Felix \u00d6zkul<\/a>, Robin Sutherland<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Despite the commercial availability of simulation tools, using of discrete-event simulation for complex production and logistics systems is becoming increasingly challenging. It requires extensive expertise, high data quality, and considerable time and financial resources. For many years, therefore, there has been high demand for methodological and organizational support for the conduction of simulation studies. This article is based on an analysis of relevant publications and aims to classify previous research on improving the use of simulation. It also raises the question of the need for assistance in applying discrete event simulation and identifies areas for action. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 66-76 | DOI 10.30844\/I4SE.25.5.64<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Bridging\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Bridging Automated and Traditional Approaches in Material Transport<\/h4>\n
Why manual tugger train systems remain prevalent in intralogistics<\/div>
Christoph S. Zoller<\/a>, Wladimir Rempel<\/a>, Justus Langer<\/a>, Bonita Grzechca<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>The ongoing automation of production logistics through driverless transport systems (DTS) can significantly enhance the efficiency and quality of transport processes. Despite these advantages, many companies still choose manual tugger train systems for material supply. Semi-structured interviews with industry experts provide insight into the reasons behind these decisions, with particular emphasis factors that extend beyond purely economic assessment. The findings indicate that the lack of flexibility of driverless transport systems and the effort required for implementation effort are key reasons why manual transport solutions are often preferred in intralogistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 4 | Pages 60-66<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

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. <\/p>\n","protected":false},"featured_media":114101,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[85915,85913,85914,80127,68670],"product_cat":[],"topic":[79371],"technology":[68434],"knowhow":[],"industry":[],"writer":[81130,85912],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-114108","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-3d-visualization","tag-digital-factory-planning","tag-immersive-user-interfaces","tag-industry-4-0-en","tag-production-optimization","topic-logistics","technology-virtual-reality-en","writer-herwig-winkler-en","writer-tobias-isau","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\/Isau.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Isau-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Malou Baumann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"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…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/114108","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\/114101"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=114108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=114108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=114108"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=114108"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=114108"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=114108"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=114108"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=114108"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=114108"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=114108"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=114108"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=114108"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=114108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}