{"id":103423,"date":"2024-02-20T13:36:13","date_gmt":"2024-02-20T12:36:13","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103423"},"modified":"2026-01-14T15:15:46","modified_gmt":"2026-01-14T14:15:46","slug":"production-control","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/production-control\/","title":{"rendered":"Production Control"},"content":{"rendered":"\n
\"HANNOVER<\/a><\/figure>\n\n\n\n\n\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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\n \n \n \n \"Industrial\n <\/picture>\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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\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\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 \"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>
Erdi \u00dcnal<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Pavlos Rath-Manakidis<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Felix Hoenig<\/a>, Christian Meske<\/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 \"Multi-Stakeholder\n <\/picture>\n <\/div>\n
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Multi-Stakeholder AI Ethics in Radiology<\/h4>\n
Implications for integrated technology and workplace design<\/div>
Valentin Langholf<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Lena Will<\/a>, Robin Denz<\/a> \n \"ORCID<\/a>, Johannes Schwarz<\/a> \n \"ORCID<\/a>, Majd Syoufi<\/a>, Pavlos Rath-Manakidis<\/a>, Marc K\u00e4mmerer<\/a>, Marcus Kremers<\/a>, Axel Mosig<\/a> \n \"ORCID<\/a>, Uta Wilkens<\/a> \n \"ORCID<\/a>, J\u00f6rg Wellmer<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n AI assistance can be seen as a welcome aid in radiology, a highly complex environment characterized by round-the-clock time pressure and quality expectations. However, it must meet high ethical standards from the perspective of both users and patients. It is a challenge to incorporate this human-centered approach into the development and introduction of AI applications. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 136-143 | DOI 10.30844\/I4SE.26.1.128<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Production\n <\/picture>\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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\n \n \n \n \"The\n <\/picture>\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
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\n \n \n \n \"Empathic\n <\/picture>\n <\/div>\n
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Empathic Assembly Assistance<\/h4>\n
Combining AI-based data analysis and empathic human digital twins<\/div>
Matthias L\u00fcck<\/a> \n \"ORCID<\/a>, Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Christian Saba-Gayoso<\/a> \n \"ORCID<\/a>, Joachim Lentes<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial companies in Germany face demographic change and stagnating productivity in an increasingly complex world. Manual assembly remains essential for complex, low-volume products, yet productivity and quality lag due to human variability. This paper introduces a concept and demonstrator for an empathic assembly assistance system that merges a human digital twin and AI-based screwdriver data analytics within a modular architecture. Tightening anomalies are classified, linked to inferred worker states and translated into information and recommendations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 6-13 | DOI 10.30844\/I4SE.25.5.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Automation\n <\/picture>\n <\/div>\n
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\t\t

Automation of Production Planning and Control<\/h4>\n
A deep dive into production control with intelligent agents<\/div>
Jonas Schneider<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a>, Matthias Schmidt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n How can artificial intelligence (AI) automate production planning and control? This study examines its potential to enhance efficiency in modern production environments. The focus is on establishing a robust data infrastructure that integrates real-time, historical, and contextual data to create a solid basis for AI models. Reinforcement learning (RL) is applied to aid automation. A roadmap for implementation, focusing on practical application, is presented. This roadmap incorporates simulation-based training methods and outlines strategies for continuous improvement and adaptation of production processes. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 86-93 | DOI 10.30844\/I4SE.25.5.84<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Digital Twins for Production and Logistics Systems<\/h4>\n
Challenges and focus areas in implementation and use<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Nicolas Wittine<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n For a successful implementation as well as sustainable use and maintenance of digital twins for production and logistics systems, it is necessary to identify relevant use cases and master the associated challenges. This paper analyzes scientific literature on common applications and challenges in the implementation of digital twins for the planning and operation of production and logistics systems. To confirm the practical relevance of the results, the results of an empirical survey have also been included. The findings are used to derive key focus areas for the successful implementation and long-term use of digital twins in production and logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 42-49 | DOI 10.30844\/I4SE.25.3.42<\/a><\/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":0,"parent":103410,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103423","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\/103423","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=103423"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103423\/revisions"}],"predecessor-version":[{"id":112698,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103423\/revisions\/112698"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103410"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103423"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}