{"id":103425,"date":"2024-02-20T13:37:20","date_gmt":"2024-02-20T12:37:20","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103425"},"modified":"2024-05-13T19:54:33","modified_gmt":"2024-05-13T17:54:33","slug":"process-management","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/process-management\/","title":{"rendered":"Process Management"},"content":{"rendered":"\n\n\n
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\n \n
\n \n \n \n \"XAI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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 \"Co-Determination\n <\/picture>\n <\/div>\n
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Co-Determination Dialogues<\/h4>\n
A tool for human-centered AI implementation<\/div>
Manfred Wann\u00f6ffel<\/a> \n \"ORCID<\/a>, Fabian Hoose<\/a> \n \"ORCID<\/a>, Alexander Ranft<\/a>, Claudia Niewerth<\/a> \n \"ORCID<\/a>, Dirk St\u00fcter<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n As part of the regional competence center humAIne, funded by the Federal Ministry of Research, Technology, and Space (BMFTR), a process was developed using co-determination dialogues to establish a common understanding of the challenges involved in the introduction of artificial intelligence (AI) between management, employees, and interest groups. Experiences from project partner companies such as Doncasters Precision Castings in Bochum GmbH (DPC) exemplify how co-determination dialogues not only help to develop legally binding regulations for manageable, operationally anchored, sustainable AI use but also initiate continuous qualification processes for all stakeholder groups in accordance with Articles 4 and 5 of the EU AI Act. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 92-98 | DOI 10.30844\/I4SE.26.1.84<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Ethical\n <\/picture>\n <\/div>\n
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Ethical AI in the Workplace Through Value-Based Labels?<\/h4>\n
Lessons learned from applying the VCIO framework to an AI-based assistant<\/div>
Natalie Martin<\/a> \n \"ORCID<\/a>, Tobias Kopp<\/a> \n \"ORCID<\/a>, Natalie Beyer<\/a>, Jochen Wendel<\/a> \n \"ORCID<\/a>, Steffen Kinkel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The AI Ethics Label represents a promising approach to promoting ethical AI and appropriate trust in AI systems. However, its practical application reveals some challenges due to its conservative assessment approach, limited context sensitivity, lack of benchmarks, and interpretation aids. Improvements are needed to unlock its full potential. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 1 | Pages 30-38 | DOI 10.30844\/I4SE.26.1.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Potentials,\n <\/picture>\n <\/div>\n
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Potentials, Premises, Perspectives<\/h4>\n
Using LLMs to reinterpret corporate knowledge management<\/div>
Vanessa Kuks<\/a> \n \"ORCID<\/a>, Pius Finkel<\/a> \n \"ORCID<\/a>, Peter Wurster<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Demographic change is exacerbating the shortage of labor and skilled workers in the manufacturing industry, making knowledge management an increasingly important issue in many companies. Collecting and preserving tacit knowledge poses a particular challenge. This study examines the extent to which large language models (LLMs) can provide meaningful support in knowledge gathering through expert interviews. Three experts test and evaluate a personalized chatbot that has been developed using ChatGPT-5. The results of the interview are promising, but the summary shows room for improvement. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 48-56 | DOI 10.30844\/I4SE.25.6.48<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Human-Centered\n <\/picture>\n <\/div>\n
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Human-Centered AI-Paired Work Systems<\/h4>\n
Integrating GenAI and the human factor in work system theory<\/div>
Katharina H\u00f6lzle<\/a> \n \"ORCID<\/a>, Udo-Ernst Haner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The work system is the key unit of analysis within the discipline of human factors\/ergonomics (HFE); it is also considered a fundamental element for value creation within other domains. Its concept is based on sociotechnical systems theory and, within HFE, it conveys a distinctly human-centered perspective. So far, work system models have focused on one or several people working within a defined setting as the only (intelligent) actors within the system. The introduction of generative artificial intelligence (genAI) into work systems, particularly as an intelligent and autonomous actor (agent) with potentially specific social abilities and personality traits, calls for reconceptualization. This article elaborates on the new requirements related to the introduction of genAI and develops a human-centered AI-paired work system model that recognizes the significantly expanded capabilities of AI-enabled collaborative social robots. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 38-48 | DOI 10.30844\/I4SE.25.5.38<\/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 Smart Workstation for Industrial Quality Control<\/h4>\n
Enhancing productivity through vision systems, real-time assistance, and Axiomatic Design<\/div>
Leonardo Venturoso<\/a> \n \"ORCID<\/a>, Simone Garbin<\/a> \n \"ORCID<\/a>, Dieter Steiner<\/a> \n \"ORCID<\/a>, Dominik T. Matt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system\u2019s practical benefits and its scalability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 128-134 | DOI 10.30844\/I4SE.25.5.124<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Derivation\n <\/picture>\n <\/div>\n
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Derivation of MTM Analyses from Motion Capture Data<\/h4>\n
Evaluation of the procedure and comparison with a manual MTM analysis<\/div>
Silas P\u00f6ttker<\/a> \n \"ORCID<\/a>, Maria Neumann<\/a> \n \"ORCID<\/a>, Martin Benter<\/a> \n \"ORCID<\/a>, Constantin Eckart<\/a> \n \"ORCID<\/a>, Ulrike Wolf<\/a> \n \"ORCID<\/a>, Peter Kuhlang<\/a> \n \"ORCID<\/a>, Hermann L\u00f6dding<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n For around 15 years, German labor productivity per working hour has been increasing at significantly less than 1% per year. At the same time, more detailed productivity analyses reveal high potential in companies. The issue is that the required MTM analyses are complex and not yet employed as broadly and frequently as would be necessary. One solution is the use of digital technologies such as motion capture. These make it possible to carry out productivity analyses with little effort, as they provide data that accelerates the analysis. The MTMmotion\u00ae tool from the MTM ASSOCIATION e.\u00a0V. was developed with the aim of carrying out valid and compliant MTM analyses using data provided by other technologies. This article compares the method developed for a motion capture system and MTMmotion\u00ae with a conventional MTM-1\u00ae analysis. The main result is that digital technologies can be used to create valid MTM analyses in early planning phases with little effort in order to make early ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 112-119 | DOI 10.30844\/I4SE.25.5.108<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Increased\n <\/picture>\n <\/div>\n
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Increased Productivity in Engineer-to-Order Production<\/h4>\n
Digital assistance between design and production in shipbuilding<\/div>
Jan Sender<\/a> \n \"ORCID<\/a>, David Jericho<\/a> \n \"ORCID<\/a>, Konrad Jagusch<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In engineer-to-order production systems, design and production processes are often carried out simultaneously to achieve shorter throughput times. Shipbuilding frequently adopts this approach. In practice, whilst this may lead to time savings, it can also result in efficiency losses. This article analyzes the causes of these inefficiencies and, as a counteractive measure, develops digital assistance systems for integration in the shipbuilding process chain. Digital assistance systems are based on a digital shadow of the shipbuilding process. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 78-85 | DOI 10.30844\/I4SE.25.5.76<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Increasing\n <\/picture>\n <\/div>\n
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Increasing Resilience in Logistics with IT<\/h4>\n
Investigating supply chain risk management information systems<\/div>
Alexander Baur<\/a>, Jasmin Hauser<\/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>The blockage of the Suez Canal in 2021, caused by the accident involving the container ship Ever Given, clearly illustrates the need to design global supply chains in such a way that they can respond quickly to disruptions. In a volatile, uncertain, complex, and ambiguous (VUCA) environment, conventional logistics processes that focus on efficiency, and supply chain management methods in particular, are increasingly reaching their limits. Resilience, achieved through a combination of robustness and agility, is essential to ensure responsiveness. This article analyzes how risk management information systems (RMIS) can increase resilience. The analysis covers data availability, data transparency, modeling and simulation of risk scenarios, and the development of appropriate emergency action plans. Despite existing challenges in designing IT infrastructure, the measures mentioned have the potential to increase resilience in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 36-42<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Field\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Field Meets Code<\/h4>\n
Artificial intelligence for better collaboration in software development<\/div>
Andreas Groche<\/a>, Dominik Augenstein<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Software development is fundamental to digital transformation. A good foundation of data is required for developers to tailor software to the needs of the commissioning department. Unfortunately, the data models required for this are incomplete, often created unilaterally by the development department and not embedded in the business context. This makes it difficult for both developers and AI to find the right algorithms. The present approach increases understanding and exchange between the specialist and development departments and offers digital assistance with data modeling as a basis for software development. Furthermore, AI approaches can help to increase the quality and completeness of the data. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 104-110<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n13<\/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-103425","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\/103425","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=103425"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103425\/revisions"}],"predecessor-version":[{"id":103427,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103425\/revisions\/103427"}],"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=103425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}