{"id":103480,"date":"2024-02-20T13:20:19","date_gmt":"2024-02-20T12:20:19","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103480"},"modified":"2025-02-10T17:35:13","modified_gmt":"2025-02-10T16:35:13","slug":"adaptability","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/adaptability\/","title":{"rendered":"Adaptability"},"content":{"rendered":"\n\n\n
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\n \n \n \n \"MAKI\u2014A\n <\/picture>\n <\/div>\n
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MAKI\u2014A Digital Assistant for Practice-Based Learning<\/h4>\n
Why every factory is a learning factory<\/div>
Olaf Resch<\/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>With the help of digital assistants, academic teaching is possible in any factory. In order to achieve the best learning effects, however, the interests of all stakeholders must be taken into account. The factory wishes to deploy its employees quickly and productively, the learners desire a positive learning experience, and the educators want to illustrate abstract concepts in a meaningful and practical way. The only way to combine all of these perspectives is via a well-thought-out educational concept and highly functioning technology. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 70-77<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Serious\n <\/picture>\n <\/div>\n
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Serious Gaming and the Energy Transition<\/h4>\n
Collaborative knowledge generation and interactive understanding of complex interrelationships<\/div>
Janine Gondolf<\/a> \n \"ORCID<\/a>, Gert Mehlmann<\/a>, J\u00f6rn Hartung<\/a>, Bernd Schweinshaut<\/a>, Anne Bauer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 62-69<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Industrial Transformation via a Machining Learning Factory<\/h4>\n
A learning module to foster competencies for a sustainability-driven transformation<\/div>
Oskay Ozen<\/a> \n \"ORCID<\/a>, Victoria Breidling<\/a> \n \"ORCID<\/a>, Stefan Seyfried<\/a> \n \"ORCID<\/a>, Matthias Weigold<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 38-47 | DOI 10.30844\/I4SE.26.2.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Data Quality and Domain Expertise for Resilient AI Deployment<\/h4>\n
Integrating anomaly and label error detection in industry<\/div>
Pavlos Rath-Manakidis<\/a>, Henry Huick<\/a>, Erdi \u00dcnal<\/a>, Bj\u00f6rn Kr\u00e4mer<\/a> \n \"ORCID<\/a>, Laurenz Wiskott<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI implementation transforms work and worker-technology relationships in industrial quality control. This paper explores how approaches to data quality and model transparency support ethical AI deployment, fostering worker agency, trust, and sustainable work design in automatic surface inspection systems (ASIS). Recurring problems like data inefficiency, variable model confidence, and limited AI expertise point to key challenges of human-centered AI: user trust, agency and responsible data management. A solution co-developed with an ASIS supplier demonstrates that the challenges extend beyond the purely technical, underscoring the value of AI design that augments human capabilities. Technical solutions such as anomaly, label error, and domain drift detection are proposed to enhance data quality and model reliability. The insights emphasize the following generalizable strategies for resilient AI integration: understanding user-reported problems through a human-AI interaction lens, ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 128-135 | DOI 10.30844\/I4SE.26.1.120<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Digital Competence Lab (DCL) for Speech Therapy<\/h4>\n
Designing a learning platform to advance digital skills<\/div>
Anika Thurmann<\/a> \n \"ORCID<\/a>, Antonia Weirich<\/a> \n \"ORCID<\/a>, Kerstin Bilda<\/a>, Fiona D\u00f6rr<\/a> \n \"ORCID<\/a>, Lars T\u00f6nges<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The digital transformation of healthcare results in lasting changes in speech therapy. Smart technologies and artificial intelligence (AI) are creating new opportunities to ensure therapy quality, address care bottlenecks, and actively involve patients in exercise processes. At the same time, these developments are expanding the role of speech therapists, who increasingly use digital systems as supportive tools in addition to their core therapeutic tasks. Based on a feasibility study of the AI-supported application ISi-Speech-Sprechen in a real-world setting of complex Parkinson's therapy (PKT), this article outlines the key challenges associated with implementing smart technologies. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 110-118 | DOI 10.30844\/I4SE.26.1.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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AI Skills for Responsible Use<\/h4>\n
Realistic learning environments, critical thinking, and role design in teams<\/div>
Valentin Langholf<\/a> \n \"ORCID<\/a>, Niklas Obermann<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, Marco Kuhnke<\/a>, Michael Pr\u00fcfer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) is changing the world of work. But how can work teams learn to use AI support in a way that delivers speed advantages and ensures consistently high quality? One possible approach is to test it in a workplace-like simulation. Trying it out under realistic conditions shows the role that critical thinking plays. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 100-107 | DOI 10.30844\/I4SE.26.1.92<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Adapting AI Work Systems for Human-Centeredness<\/h4>\n
A methodical approach for exploring the design space in transdisciplinary teams<\/div>
Florian B\u00fclow<\/a> \n \"ORCID<\/a>, Michael Herzog<\/a> \n \"ORCID<\/a>, Sophie Berretta<\/a> \n \"ORCID<\/a>, Dominik Arnold<\/a> \n \"ORCID<\/a>, Christian Els<\/a>, Bernd Kuhlenk\u00f6tter<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Designing adaptations in AI-based work systems poses a central challenge for achieving human-centered AI (HCAI). This paper presents a methodical approach that enables transdisciplinary teams to systematically explore and structure the design space of adaptable work systems. Building on an extended work system model and operationalized through a matrix-based framework, the method supports the identification of interdependencies, stakeholder perspectives, and context-specific goals. Its practical applicability is demonstrated through a real-world case study in radiographic non-destructive testing. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 44-53 | DOI 10.30844\/I4SE.26.1.136<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Production Control in Space<\/h4>\n
An AI-supported approach for industry in orbit<\/div>
Dominik Augenstein<\/a>, Lara Jovic<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Production in space, of semiconductors for example, offers many advantages for companies. At the same time, high transport costs mean that careful consideration must be given to the production materials being transported. The use of Kalman filters enables (real-time) control from Earth, making space production a cost-efficient option. Machine learning could make it a viable approach even for highly complex production systems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 6 | Pages 22-29<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Work Design for Learning and Competence Development<\/h4>\n
Emerging ways of organizing and supporting learning in digitally transformed workplaces<\/div>
Peter Dehnbostel<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Learning and skill-enhancing work designs are essential for new work and organizational concepts such as \u201clearning organizations\u201d and \u201cIndustry 4.0.\u201d Developing and applying criteria for promoting learning and skills development in digitally transformed work environments enables more effective and efficient work processes, makes work more people-centered, and the AI-based future of work more manageable. Digitalization is also introducing work-integrated learning formats such as online communities, learning platforms, and digital cognitive assistance systems that already meet many of these criteria. In the future, designing work environments that promote learning and skills development will become a central task of corporate training and personnel and organizational development. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 6 | Pages 58-64<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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The Bias of \u201cInstructional Systems for the Disabled\u201d<\/h4>\n
Ethnographic insights from deploying augmented reality in a sheltered workshop<\/div>
David Kostolani<\/a> \n \"ORCID<\/a>, Annemarie Ploss<\/a>, Sebastian Schlund<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rehumanization of industrial work has emerged as a key focus in Industry 4.0 research, emphasizing the empowerment of human workers amidst advancing automation. Within this re-search, supporting workers with disabilities through digital assistance technologies serves as a prime example of a human-centric approach to industrial engineering. These technologies often claim to enhance productivity, which aims to promote the integration of workers with disabili-ties in industrial roles. But can they genuinely improve their work experience? This ethnograph-ic study presents insights from two years of developing and deploying augmented reality in a sheltered woodworking workshop. Over this period, we engaged in conversations and facilitat-ed over 30 technology sessions with workers with diverse disabilities. Our experiences chal-lenge the narrative of industrial research, in particular with digital instructional systems serving as \u201cenabler technology\u201d to help them work \u201cbetter.\u201d ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 102-110 | DOI 10.30844\/I4SE.25.5.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n21<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103459,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103480","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"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\/103480","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=103480"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103480\/revisions"}],"predecessor-version":[{"id":107780,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103480\/revisions\/107780"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103459"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}