{"id":89199,"date":"2006-04-15T12:00:00","date_gmt":"2006-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/intelligent-distributed-and-reconfigurable-automation-and-control-systems-for-the-manufacturing-industry\/"},"modified":"2025-07-29T10:24:07","modified_gmt":"2025-07-29T08:24:07","slug":"intelligent-distributed-and-reconfigurable-automation-and-control-systems-for-the-manufacturing-industry","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/intelligent-distributed-and-reconfigurable-automation-and-control-systems-for-the-manufacturing-industry\/","title":{"rendered":"Reconfigurable Automation Systems in Manufacturing"},"content":{"rendered":"

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
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XAI for Predicting and Nudging Worker Decision-Making<\/h4>\n
Feasibility and perceived ethical issues<\/div>
Jan-Phillip Herrmann<\/a> \n \"ORCID<\/a>, Catharina Baier<\/a>, Sven Tackenberg<\/a> \n \"ORCID<\/a>, Verena Nitsch<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students\u2019 sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students\u2019 preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 70-78<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Improving\n <\/picture>\n <\/div>\n
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Improving Documentation Quality and Creating Time for Core Activities<\/h4>\n
Success factors for implementing AI-based documentation systems in nursing care<\/div>
Sophie Berretta<\/a> \n \"ORCID<\/a>, Elisabeth Liedmann<\/a> \n \"ORCID<\/a>, Paul-Fiete Kramer<\/a> \n \"ORCID<\/a>, Anja Gerlmaier<\/a>, Christopher Schmidt<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Demographic change is accompanied by both a growing demand for care and a shortage of qualified nursing staff. Consequently, AI-based technologies are increasingly becoming a focus of care-related innovations. Their aim is to reduce workload pressure, save time, and enhance the attractiveness of the nursing profession. Using the example of AI-supported documentation systems for admission interviews, this article examines to what extent such systems can contribute to improvements in work processes and care quality, focusing on the perspectives of nursing professionals and nursing experts. The results indicate potential for workload relief, enhanced documentation quality, and the reallocation of time resources toward direct patient care. However, realizing these potentials requires a human-centered and context-sensitive implementation approach. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 154-160 | DOI 10.30844\/I4SE.26.1.146<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Applied\n <\/picture>\n <\/div>\n
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Applied AI for Human-Centric Assembly Workplace Design<\/h4>\n
An ethics-informed approach<\/div>
Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Michael Jonek<\/a> \n \"ORCID<\/a>, Sascha Niethammer<\/a>, Henning Vogler<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection\u00ae. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844\/I4SE.26.1.58<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Applied\n <\/picture>\n <\/div>\n
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Applied Knowledge and Augmented Reality<\/h4>\n
Bridging the gap between learning and application<\/div>
Jana Gonnermann-M\u00fcller<\/a> \n \"ORCID<\/a>, Philip Wotschack<\/a> \n \"ORCID<\/a>, Martin Krzywdzinski<\/a> \n \"ORCID<\/a>, Norbert Gronau<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The increasing complexity of industrial environments demands new competencies from workers, particularly the ability to interact with advanced digital systems. Traditional training methods often fall short in supporting the effective transfer of applied knowledge to such contexts, and the effectiveness of this transfer, as measured by performance-based outcomes, remains to be investigated. To address this gap, the present study employed a between-subjects experimental design comparing augmented reality- and paper-based instructions within a realistic production training scenario. The results show that participants who learned with augmented reality completed the production process significantly faster and with fewer errors than those using paper instructions. In addition, learners using augmented reality reported higher usability and experienced lower cognitive load during training. These findings suggest that augmented reality can enhance the transfer of practical skills in industrial ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 22-29 | DOI 10.30844\/I4SE.25.5.22<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Camera-Based\n <\/picture>\n <\/div>\n
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\t\t

Camera-Based Ergonomics Assessment<\/h4>\n
Developing a method for use in manual assembly<\/div>
Jannik Liebchen<\/a> \n \"ORCID<\/a>, Burak Vur<\/a> \n \"ORCID<\/a>, Michael Freitag<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Targeted ergonomic design of workplaces and processes can counteract the challenges of manual assembly and improve working conditions. However, current expert ergonomics assessments are time-consuming and resource-intensive. This article presents an automated assessment method based on the Rapid Upper Limb Assessment (RULA). Results from a laboratory study within an assembly scenario are consistent with expert evaluations. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 120-126 | DOI 10.30844\/I4SE.25.5.116<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Automation\n <\/picture>\n <\/div>\n
\n \t \n
\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<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The manufacturing and production industry will only survive in a more and more globalized word if they react fast and flexible to new market and customer demands. In order to achieve the required flexibility, technically support for reconfiguration both on the machine (physical) and on the control technical (logical) level is necessary. From the technological point of view a shift from central control and rigidly coupled production systems to distributed, modular and cooperating automation components is essential. An important role of such proposed productions systems plays the automation and control concept.<\/p>\n","protected":false},"featured_media":98683,"menu_order":0,"template":"","categories":[],"tags":[70471,70473,70472],"product_cat":[3300],"topic":[68005],"technology":[],"knowhow":[],"industry":[],"writer":[81293,81311,81312,81310,81309],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":{"0":"post-89199","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"tag-distributed-control","7":"tag-real-time-applications","8":"tag-reconfigurable-automation-systems","9":"product_cat-article","10":"topic-automation","11":"writer-alois-zoitl-en","12":"writer-christoph-suender-en","13":"writer-gunnar-grabmair-en","14":"writer-ingomar-mueller-en","15":"writer-thomas-strasser-en","16":"product","17":"first","18":"instock","19":"downloadable","20":"virtual","21":"sold-individually","22":"taxable","23":"purchasable","24":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/strasser_IM2-2006-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The manufacturing and production industry will only survive in a more and more globalized word if they react fast and flexible to new market and customer demands. In order to achieve the required flexibility, technically support for reconfiguration both on the machine (physical) and on the control technical (logical) level is necessary. 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