{"id":103486,"date":"2024-02-20T14:20:06","date_gmt":"2024-02-20T13:20:06","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103486"},"modified":"2024-05-14T04:53:19","modified_gmt":"2024-05-14T02:53:19","slug":"dynamics","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/management\/dynamics\/","title":{"rendered":"Dynamics"},"content":{"rendered":"\n\n\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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Developing Data Standards in Battery Cell Manufacturing<\/h4>\n
From requirements analysis to standard development procedure<\/div>
David Roth<\/a>, Tom H\u00fclsmann<\/a>, Felix Tidde<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The growing demand for battery cells offers significant potential for the use of digital solutions in their manufacture, which in turn creates opportunities for added value through adaptive and flexible production systems. A key enabler is interoperable data exchange based on formalized data descriptions. Existing ontologies and information models remain too abstract for direct implementation. This paper presents a requirements analysis of data standards in battery cell manufacturing. A procedure for developing domain-specific standards based on OPC UA (Open Platform Communications Unified Architecture) is derived from the results. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 96-103<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Machine Learning to Promote Sustainability\u00a0<\/h4>\n
Company analysis based on expert interviews<\/div>
Niklas Bode<\/a> \n \"ORCID<\/a>, Lukas Nagel<\/a> \n \"ORCID<\/a>, Oskay Ozen<\/a> \n \"ORCID<\/a>, Matthias Weigold<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 44-51<\/div> <\/div>\n <\/div>\n <\/a>\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
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\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
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Can Artificial Intelligence (AI) Act as an Enabler for Industry 4.0 (4IR)?<\/h4>\n
Impacts on the maturity level of Industry 4.0 technologies<\/div>
Dennis Richter<\/a>, Mildred Doe<\/a>, Steffen Kinkel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence is often mentioned often mentioned in the same context as Industry 4.0, but the exact role of AI is unclear. Is AI just another 4IR technology or an essential \"enabler\" for other 4IR technologies? Six experts assess the impact of AI on 41 4IR technologies. AI could indeed be a decisive factor in unleashing the full potential of Industry 4.0. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 80-87 | DOI 10.30844\/I4SE.24.6.80<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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The Utopia of European Cybersecurity Certifications<\/h4>\n
Alexander Lawall<\/a> \n \"ORCID<\/a>, Jesus Luna Garcia<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Interoperable automation can benefit cybersecurity certification processes that result from the EU Cybersecurity Act (e.g. EUCS) so that they represent less overhead for the stakeholders involved. The development of key standardization efforts involving relevant stakeholders (e.g. regulators) is needed to fully realize these benefits. EU projects like H2020 MEDINA, HEU COBALT and communities such as EUROSCAL are well on the way to achieving this goal. However, more practical experience is needed to make continuous certification with automation a reality. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 2 | Pages 48-55<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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On the Way to Energy Efficiency in Logistics Networks<\/h4>\n
State of the Integration of Energy-Related Objectives into the Simulation-Based Analysis<\/div>
Jan Cirullies<\/a>, Michael Toth<\/a>, Andreas Holtz<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Globalization and the growing number of supply chain participants lead to increasing cargo transport service and, thus, to higher energy demand. Although energy prices increase at the same time, the energy balance of production networks remains unconsidered during in the network design phase. Hence, the research project E\u00b2Log analyzes how logistics networks and production environment can be coordinated in order to improve energy efficiency. In the first project phase, based on the supply chain for the production of the Volkswagen Amarok, the use case partners have derived simulation scenarios and enhanced a simulation tool to evaluate measures for the efficiency increase without ignoring classic logistic objectives soon. <\/div>\n <\/div>\n
Industrie Management<\/strong> | Volume 28 | 2012 | Edition 5 | Pages 20-24<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Approximation and Robustness of Dynamic Production networks<\/h4>\n
Bernd Scholz-Reiter<\/a> \n \"ORCID<\/a>, Michael Kosmykov<\/a>, Thomas Makuschewitz<\/a>, Fabian Wirth<\/a>, Michael Sch\u00f6nlein<\/a>, Sergey Dashkovskiy<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Global production networks connect partners with outstanding expertise, and make use of regional cost advantages for purchasing and production operations. This development leads to an increasing structural complexity of the networks, which is accompanied by a closer collaboration of dynamic logistics processes. Hence, the resulting dynamics of a large-scale production network is characterized by the dynamics of the individual logistics processes, the dynamics of the network structure and dynamics of the external processes that affect the production network. However, in practice a lack of adequate procedures for the analysis and design of these networks can be observed. The presented article addresses this need by introducing tools and methods for the approximation of large-scale production networks, analysis of their dynamics and the robust design of the network resources. <\/div>\n <\/div>\n
Industrie Management<\/strong> | Volume 28 | 2012 | Edition 4 | Pages 51-56<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Sustainable Supply Chain Management<\/h4>\n
Assessment of the Sustainable Application of Logistic Concepts in Corporate Networks<\/div>
Jan Helmig<\/a>, Jerome Quick<\/a>, Henrik Wienholdt<\/a>, Kerem Oflazgil<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Since the turn of the millennium companies are confronted with a tightly interwoven network consisting of changed framework conditions. Thus, for many companies it is necessary to form a network, together with a simultaneous securing of liquidity. Additionally, it is important to consider ecological values. The selection of adequate concepts in the supply chain management in combination with the right extent in logistic services poses a major challenge. However the dynamic assessment and selection of logistic concepts is quite possible. This article presents such an approach. <\/div>\n <\/div>\n
Industrie Management<\/strong> | Volume 26 | 2010 | Edition 5 | Pages 58-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Dynamics in Production Processes<\/h4>\n
Identifikation logistikrelevanter struktureller Ver\u00e4nderungen<\/div>
Marco Kennemann<\/a>, Steffen C. Eickemeyer<\/a>, Eugen Schnurr<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>environment poses huge challenges to production enterprises, especially with regards to logistics. The Logistic Operating Curve Theory, developed at the Institute of Production Systems and Logistics (IFA), is a recognized approach to describing logistic interactions, nevertheless, it reaches its limits when it comes to the dynamic aspects. In order to facilitate a timely and optimal Logistic Positioning a method is developed for quickly and reliably identifying dynamic processing states. <\/div>\n <\/div>\n
Industrie Management<\/strong> | Volume 26 | 2010 | Edition 5 | Pages 19-22<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":0,"parent":103482,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103486","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\/103486","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=103486"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103486\/revisions"}],"predecessor-version":[{"id":103487,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103486\/revisions\/103487"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103482"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103486"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}