{"id":94271,"date":"2021-06-15T12:00:00","date_gmt":"2021-06-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=94271"},"modified":"2024-09-25T14:11:34","modified_gmt":"2024-09-25T12:11:34","slug":"humans-in-industry-4-0","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/humans-in-industry-4-0\/","title":{"rendered":"Humans in Industry 4.0"},"content":{"rendered":"<hr>\n<div class=\"gito-pub-content-bibliography\">\n<h2>Bibliography <\/h2>\n[1] Lu, Y., Industry 4.0: A survey on technologies, applications and open research issues. In: Journal of Industrial Information Integration 6 (2017), S. 1-10.<br \/>\n[2] Kagermann, H.; Wahlster, W.; Helbig, J.: Recommendations for implementing the strategic initiative Industrie 4.0, Securing the future of German manufacturing industry, Final report of the Industrie 4.0 Working Group. Berlin 2013.<br \/>\n[3] Neumann, W. P. u. a.: Industry 4.0 and the human factor \u2013 A systems framework and analysis methodology for successful development. In: International Journal of Production Economics 233 (2021), 107992.<br \/>\n[4] Romero, D. u. a.: Towards an Operator 4.0 Typology: A Human-Centric Perspective on the Fourth Industrial Revolution Technologies. In: Proceedings of the International Conference on Computers and Industrial Engineering. Tianjin, China 2016.<br \/>\n[5] Rohleder, B.: Digitalisierung der Logistik. Berlin 2019.<br \/>\n[6] Brynjolfsson, E.; McAfee, A.: The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York 2014.<br \/>\n[7] Rose, L. M.; Orrenius, U. E.; Neumann, W. P.: Work environment and the bottom line: Survey of tools relating work environment to business results. In: Human Factors and Ergonomics in Manufacturing &amp; Service Industries 23 (2013) 5, S. 368-381.<br \/>\n[8] Breque, M.; De Nul, L.; Petridis, A.: Industry 5.0 &#8211; Towards a sustainable, human-centric and resilient European industry, European Union. Bruxelles 2021.<br \/>\n[9] Neumann, W. P.; Dul, J.: Human Factors: Spanning the Gap between OM &amp; HRM. In: International Journal of Operations &amp; Production Management 30 (2010) 9, S. 923-950.<br \/>\n[10] Shipton, H. J. u. a.: When promoting positive feelings pays: Aggregate job satisfaction, work design features, and innovation in manufacturing organizations. In: European Journal of Work and Organizational Psychology 15 (2006) 4, S. 404-430.<br \/>\n[11] Russell-Walling, E.: Scientific Management, in 50 Schl\u00fcsselideen Management. Heidelberg 2011.<br \/>\n[12] Waschull, S. u. a.: Work design in future industrial production: Transforming towards cyber-physical systems. In: Computers &amp; Industrial Engineering 139 (2020), 105679.<br \/>\n[13] L\u00fcdtke, A.: Wege aus der Ironie in Richtung ernsthafter Automatisierung, In: Botthof, A.; Hartmann, E. (Hrsg.): Zukunft der Arbeit in Industrie 4.0. Heidelberg 2015, S. 125-146.<br \/>\n[14] Peissner, M.; Hipp, C.: Potenziale der Mensch-Technik-Interaktion f\u00fcr die effiziente und vernetzte Produktion von morgen. Stuttgart 2013.<br \/>\n[15] Wieland, R.: Digitalisierung und der Mensch. In: Forschungsmagazin der bergischen Universit\u00e4t Wuppertal BUW.OUTPUT 21 (2019), S. 6-11.<br \/>\n[16] Dregger, J. u. a.: Challenges for the future of industrial labor in manufacturing and logistics using the example of order picking systems. In: Procedia CIRP 67 (2018), S. 140-143.<br \/>\n[17] Reiman, A. u.a.: Human factors and ergonomics in manufacturing in the industry 4.0 context \u2013 A scoping review. In: Technology in Society, 65 (2021), 101572.<br \/>\n[18] Ulich, E.: Arbeitspsychologie. 7. Auflage, Stuttgart 2011.<br \/>\n[19] Sgarbossa, F. u. a.: Human factors in production and logistics systems of the future. In: Annual Reviews in Control 49 (2020), S. 295-305.<br \/>\n[20] Kadir, B. A.; Broberg, O.; Concei\u00e7\u00e3o, C. S. d.: Current research and future perspectives on human factors and ergonomics in Industry 4.0. In: Computers &amp; Industrial Engineering 137 (2019), S. 1-12.<\/div>\n<div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\">\n<h2>Your downloads<\/h2>\n<p><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"94271\" data-userid =\"0\" data-filename=\"winklelhaus_Soziotechnische Systeme- Der-Mensch-in-der-Industrie40-IM_3-2021.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF<\/button><\/div>\n<div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/industrie-4-0-en\/\">Industrie 4.0<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Humans%20in%20Industry%204.0 - https:\/\/industry-science.com\/en\/articles\/humans-in-industry-4-0\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/articles\/humans-in-industry-4-0\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); 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return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n<h2 class=\"gito-pub-frontend-post-headline\">You might also be interested in<\/h2>\n<!-- GITO_PUB_POST start flex-container -->\n<div class=\"gito-pub-flex-container\">\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/automotive-body-manufacturing\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/richter_AdobeStock_1887518115_Andrey-Popov-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/richter_AdobeStock_1887518115_Andrey-Popov-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/richter_AdobeStock_1887518115_Andrey-Popov-196x180.webp\" alt=\"Interoperable Data Access in Automotive Body Manufacturing\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Interoperable Data Access in Automotive Body Manufacturing\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Interoperable Data Access in Automotive Body Manufacturing<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Deterministic integration of structured target parameters into tact-time production<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/tim-richter\/\">Tim Richter<\/a> <a href=\"https:\/\/orcid.org\/0009-0007-9110-0187\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/robert-weidner\/\">Robert Weidner<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-1449-3796\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 34-42 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.4\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.4<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/continuing-vocational-education\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/beichter_AdobeStock_1885327612_master1305-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/beichter_AdobeStock_1885327612_master1305-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/09\/beichter_AdobeStock_1885327612_master1305-196x180.webp\" alt=\"Audio-Immersive Learning in Continuing Vocational Education\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Audio-Immersive Learning in Continuing Vocational Education\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Audio-Immersive Learning in Continuing Vocational Education<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">From linear audio playback to AI-supported conversational learning companions<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/tim-beichter\/\">Tim Beichter<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/vanessa-hartmann\/\">Vanessa Hartmann<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/katharina-hoelzle\/\">Katharina H\u00f6lzle<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9733-4650\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/manuel-kaiser\/\">Manuel Kaiser<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 102-108 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.12\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.12<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-knowledge-management\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-196x180.webp\" alt=\"Generative AI in Organizational Knowledge Management\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Generative AI in Organizational Knowledge Management\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Generative AI in Organizational Knowledge Management<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">AI literacy as a prerequisite for augmentation<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/uta-wilkens-en\/\">Uta Wilkens<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-7485-4186\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/valentin-langholf-en\/\">Valentin Langholf<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-0440-4665\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/niklas-obermann-en\/\">Niklas Obermann<\/a> <a href=\"https:\/\/orcid.org\/0009-0004-3817-3203\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.14\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.14<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-demonstrators-manufacturing\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/link_AdobeStock_311608924_Gorodenkoff-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/link_AdobeStock_311608924_Gorodenkoff-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/link_AdobeStock_311608924_Gorodenkoff-196x180.webp\" alt=\"Explaining AI in Industrial Production in an Accessible Way\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Explaining AI in Industrial Production in an Accessible Way\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Explaining AI in Industrial Production in an Accessible Way<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Requirements for AI demonstrators to promote acceptance<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/jennifer-link-en\/\">Jennifer Link<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-2407-3495\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/markus-harlacher-en\/\">Markus Harlacher<\/a> <a href=\"https:\/\/orcid.org\/0009-0007-5817-2920\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/colin-srebny\/\">Colin Srebny<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/sascha-stowasser-en\/\">Sascha Stowasser<\/a> <a href=\"https:\/\/orcid.org\/0009-0006-2725-5793\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 6-14 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.1\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.1<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-based-building-inspection\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-196x180.webp\" alt=\"AI-Based Building Inspection for Large Structures\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Based Building Inspection for Large Structures\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Based Building Inspection for Large Structures<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">A new approach to construction progress monitoring<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/jan-sender-en\/\">Jan Sender<\/a> <a href=\"https:\/\/orcid.org\/0009-0003-9697-5709\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/konrad-jagusch-en\/\">Konrad Jagusch<\/a> <a href=\"https:\/\/orcid.org\/0009-0001-7454-1657\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/michael-geist\/\">Michael Geist<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-2780-7538\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/david-jericho\/\">David Jericho<\/a> <a href=\"https:\/\/orcid.org\/0009-0001-8932-8701\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/christian-scharr\/\">Christian Scharr<\/a> <a href=\"https:\/\/orcid.org\/0009-0003-6300-4682\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 78-84 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.9\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.9<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/inclusive-work-system-design\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\" alt=\"Inclusive Work System Design\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Inclusive Work System Design\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Inclusive Work System Design<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Automation, standardization, and adaptability<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/sebastian-schlund-en\/\">Sebastian Schlund<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-8142-0255\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.8\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.8<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n<\/div>\n<!-- GITO_PUB_POST end flex-container -->\n","protected":false},"excerpt":{"rendered":"<p>The development of Industry 4.0 changes the role of humans in operations systems. In sociotechnical systems, there is ongoing interaction between humans and technology, impacting human life and work. However, human factors are broadly ignored in research on Industry 4.0 technologies and implementation. In this work, a process model is described that supports the evaluation of the impact of a technology implementation on human factors and performance indicators. This can avoid negative consequences for employees as well as phantom profits and can contribute to a successful digital transformation.<\/p>\n","protected":false},"featured_media":96061,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[79627],"product_cat":[],"topic":[67617,68005,68206,67701],"technology":[67790,67634],"knowhow":[],"industry":[],"writer":[83354,83172,83355,83171],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-94271","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-industrie-4-0-en","topic-adaptability","topic-automation","topic-industry-4-0","topic-production-system","technology-artificial-intelligence","technology-tools","writer-anke-sutter-en","writer-eric-grosse-en","writer-stefan-morana-en","writer-sven-winkelhaus-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\/2024\/03\/winklelhaus_Soziotechnische 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In sociotechnical systems, there is ongoing interaction between humans and technology, impacting human life and work. However, human factors are broadly ignored in research on Industry 4.0 technologies and implementation. In this work, a process model is described that supports the evaluation&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94271","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\/96061"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94271"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94271"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94271"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94271"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94271"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94271"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94271"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94271"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94271"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94271"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94271"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94271"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94271"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}