{"id":103471,"date":"2024-02-20T13:09:51","date_gmt":"2024-02-20T12:09:51","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103471"},"modified":"2024-05-14T04:43:22","modified_gmt":"2024-05-14T02:43:22","slug":"internet-of-things","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/internet-of-things\/","title":{"rendered":"Internet of Things"},"content":{"rendered":"\n\n\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 \"Guidelines\n <\/picture>\n <\/div>\n
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Guidelines for the Fair Use of Generative AI<\/h4>\n
Practical examples from production management and social welfare<\/div>
Anja Gerlmaier<\/a>, Paul-Fiete Kramer<\/a> \n \"ORCID<\/a>, Dirk Marrenbach<\/a> \n \"ORCID<\/a>, Ren\u00e9 Wenzel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the rapid spread of assistive AI tools such as ChatGPT, Gemini, and Copilot, companies are being challenged to address the opportunities and challenges of artificial intelligence. Based on two practical examples, this article provides insight into how companies can use company-specific risk and potential analyses to develop guidelines for the fair and responsible use of AI. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 22-28 | DOI 10.30844\/I4SE.26.1.22<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
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Enabling the Future of Manufacturing with Digital Twins<\/h4>\n
Opportunities and obstacles<\/div>
Javad Ghofrani<\/a> \n \"ORCID<\/a>, Darian Lemke<\/a>, Tassilo S\u00f6ldner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 72-81<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
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Open-Source and Cost-Effective Digital Twin<\/h4>\n
A case study with two weeks to succeed<\/div>
Shantall Cisneros Saldana<\/a> \n \"ORCID<\/a>, Sonali Pratap<\/a>, Parth Punekar<\/a>, Sampat Acharya<\/a>, Heike Markus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844\/I4SE.25.3.62<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Intelligent\n <\/picture>\n <\/div>\n
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Intelligent Load Carrier Management<\/h4>\n
AI-supported monitoring and reduction of losses in logistics<\/div>
Dominik Augenstein<\/a>, Lea Basler<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their \u2018simplicity\u2019, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution\u2014without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 78-84<\/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 \u201cInTraLab\u201d Learning Factory<\/h4>\n
Gaining experience and knowledge in digitally transformed work environments<\/div>
Norbert Gronau<\/a> \n \"ORCID<\/a>, Malte Rolf Teichmann<\/a>, Malte Teichmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Learning factories offer a practical environment for simulating production processes in which learners can acquire skills through the direct application of new technologies. The Industrial Transformation Lab (InTraLab) models hybrid production processes by combining real-world demonstrators and virtual simulations. This enables learners to acquire the skills that are crucial for the digitally transformed world of work. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 46-51<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"How\n <\/picture>\n <\/div>\n
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How well do you know robotics and IIoT?<\/h4>\n
Test your knwoledge now!<\/div> <\/td>\n <\/tr>\n <\/table>\n
\nRobotics and the Industrial Internet of Things are two of the most important technologies of the Fourth Industrial Revolution. Do you know your way around? Test your knowledge in our exciting quiz. Expand your knowledge of intelligent machines, networked production and the future of industry. The correct answers will be displayed immediately. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Real-time\n <\/picture>\n <\/div>\n
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Real-time Reactions for Automated Guided Vehicles (AGV)<\/h4>\n
Monitoring and controlling with long latencies<\/div>
Dominik Augenstein<\/a>, Lea Basler<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 56-62<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
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Digital Platform Frameworks for Manufacturing Companies<\/h4>\n
A review<\/div>
Marcel Rojahn<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In recent years, digital platforms have established themselves as a central concept in the IT field. Due to the wide variety of digital platforms available on the market, there is still a need for clear comparison with criteria to enable interested parties to select, change, operate and further develop these platforms. The following paper aims to contribute to the facilitation of this comparison by undertaking a systematic literature review of digital platform frameworks in the context of the Industrial Internet of Things (IIOT) for manufacturing companies and thus providing a basis for a number of potential ways to effectively compare current digital platforms and ecosystems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 2 | Pages 8-15 | DOI 10.30844\/I4SE.24.2.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Energy\n <\/picture>\n <\/div>\n
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Energy Efficiency Through Intelligent Electricity Data Acquisition<\/h4>\n
Wireless retrofit solution based on IoT technologies and open-source software for existing industrial buildings<\/div>
Sergej Kreber<\/a>, Kevin Kutzner<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Facility managers for industrial properties are faced with the challenge of optimizing the energy efficiency of their facilities in the face of ever-increasing energy demand and rising energy costs. Digital processes that enable the comprehensive monitoring, analysis and control of energy demand offer an effective way to reduce costs, increase energy efficiency and make optimal use of resources. Based on IoT technologies and open-source software, a cost-effective, wireless and flexible retrofit solution for real-time energy data collection has been developed. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 2 | Pages 87-93<\/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":103459,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103471","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\/103471","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=103471"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103471\/revisions"}],"predecessor-version":[{"id":103472,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103471\/revisions\/103472"}],"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=103471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}