{"id":103473,"date":"2024-02-20T13:11:13","date_gmt":"2024-02-20T12:11:13","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103473"},"modified":"2024-05-14T04:43:58","modified_gmt":"2024-05-14T02:43:58","slug":"lean-production","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/lean-production\/","title":{"rendered":"Lean Production"},"content":{"rendered":"\n\n\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 \"Data\n <\/picture>\n <\/div>\n
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Data Quality in the Engineering of Circular Products<\/h4>\n
Decision support for circular value creation through data ecosystems<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Sven Rarbach<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Decisions affecting the sustainability of products are made during the engineering process. As product engineering progresses, statements on sustainability can also be substantiated. Initially, only estimates based on related products and processes are possible, but later, operational and machine data can be used. When metrics are used for key figures, the traceability of the data should be ensured. For this purpose, relevant data quality criteria and indicators are selected and analyzed for correlations. Data availability can be increased by relying on partners within data ecosystems for product engineering. Data spaces such as Gaia-X, Catena-X and Manufacturing-X form a basis for this ambition. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 12-19 | DOI 10.30844\/I4SE.25.2.12<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Analyzing Work Processes with Motion Capture Systems<\/h4>\n
Solution and implementation principles<\/div>
Hermann L\u00f6dding<\/a> \n \"ORCID<\/a>, Silas P\u00f6ttker<\/a> \n \"ORCID<\/a>, Tim Jansen<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 5 | Pages 43-49 | DOI 10.30844\/I4SE.24.5.42<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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A Learning Factory in Transition<\/h4>\n
Innovatively meeting the demands of the modern labor market<\/div>
Nick Ackerhans<\/a>, Benjamin M\u00e4rz<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Agile methods are extremely useful in solving complex problems. This is particularly beneficial in market environments where the routines of traditional corporate management are constantly being questioned. Agility is closely linked to the core ideas of Lean Management, as evidenced by the focus on processes and people. Lean factories facilitate a hands-on engagement with Lean principles, thereby promoting agile process management in various production contexts. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 4 | Pages 63-68<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Networked Learning Factories as Trailblazers<\/h4>\n
Digital pioneering work for modern education<\/div>
Julian Buitmann<\/a>, Robert Holling<\/a> \n \"ORCID<\/a>, Steffen Greiser<\/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>Learning factories promote digital transformation through an interdisciplinary approach between lean management, Industry 4.0, energy efficiency, training center or research farm. SME centers are characterized by the on-site integration of small and medium-sized companies. Such a regional strategy, combined with learning factories, promotes a goal-oriented dialog between science and practice where students can put their theoretical knowledge to the test. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 4 | Pages 16-23<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Lean\n <\/picture>\n <\/div>\n
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Lean Empowerment in the Digital Ecosystem<\/h4>\n
Translating cultural values into technical requirements<\/div>
Frank Bertagnolli<\/a> \n \"ORCID<\/a>, Sabrina Karch<\/a> \n \"ORCID<\/a>, Arndt L\u00fcder<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the advent of digitalization, prevailing paradigms \u2013 such as product centricity, face-to-face collaboration and hierarchical structures \u2013 are giving way to the vision of data-driven business models, digital, collaborative ecosystems and an agile, holacratic way of working in flat hierarchies and self-managing teams. Collaboration is made possible through the use of software solutions. In addition to adapted management concepts, the digital space also requires a digital cultural understanding on part of the companies involved. Lean empowerment is a pioneering approach to collaboration based on cultural values. In expert workshops, ideas were developed to explore how these values can be lived in a digital culture and thus in terms of global digital collaboration. This article presents concrete solutions from which requirements for digital collaboration and for implementation within IT structures and software solutions in particular can be derived. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 2 | Pages 32-39 | DOI 10.30844\/I4SE.24.2.32<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Motion-Mining Compared to Traditional Lean Tools<\/h4>\n
Sensor-supported analysis of manual processes in manufacturing and logistics<\/div>
Hendrik Appelhans<\/a>, Christopher Borgmann<\/a>, Carsten Feldmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Motion-Mining\u00ae is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining\u00ae is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 2 | Pages 24-31<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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Tool for Data-Based Continuous Improvement in Manufacturing Companies<\/h4>\n
Konstantin Neumann<\/a>, Nicole Oertwig<\/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 introduction of Lean Management System and their continuous improvement regularly poses challenges for companies. In the face of advancing digitalisation, new opportunities for analysis are opening up that also support the continuous improvement process. The article shows how process orientation, digitalisation and operational activities can be systematically applied for the development and integration of a data-based continuous improvement process in manufacturing companies. (Only in German) <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 39 | 2023 | Edition 5 | Pages 13-16<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Demand\n <\/picture>\n <\/div>\n
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Demand Planning Falcon<\/h4>\n
Precise stochastic demand calculation with a newly developed digital planning method<\/div>
Alexander Schmid<\/a>, Thomas Sobottka<\/a>, Samuel Luthe<\/a>, Wilfried Sihn<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Precise stochastic demand calculation is the key to successful material planning, i. e. to always have exactly the right quantity on hand. However, decision-makers are faced with the dilemma of which of the many forecasting methods they should use, adapted to the item properties as much as possible. This paper examines the optimization potential of a self-developed automatically optimizing forecasting approach based on ten common forecasting methods, which are evaluated using two case studies from the capital goods industry. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 38 | 2022 | Edition 6 | Pages 47-50 | DOI 10.30844\/IM_22-6_47-50<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Levelling\n <\/picture>\n <\/div>\n
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Levelling Production in the Process Industry with the Product Wheel<\/h4>\n
Vorgehensmodell, Erfolgsfaktoren und Case Study<\/div>
Christopher Borgmann<\/a>, Carsten Feldmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>Volatility in market demand leads to temporary over- and under-utilization of production assets and stocks. Levelling (heijunka) as a lean method aims at de-coupling production from market volatility. The production program is spread as even as possible over time. This achieves high asset utilization, short lead times, and low inventories. There are validated heijunka methods for the manufacturing industry, but for the process industry this remains a research gap. This article describes the Product Wheel and its validation at a building material manufacturer in order to close that gap. <\/div>\n <\/div>\n
Industrie 4.0 Management<\/strong> | Volume 36 | 2020 | Edition 5 | Pages 33-37<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n3<\/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-103473","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\/103473","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=103473"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103473\/revisions"}],"predecessor-version":[{"id":103474,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103473\/revisions\/103474"}],"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=103473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}