{"id":103421,"date":"2024-02-20T13:34:36","date_gmt":"2024-02-20T12:34:36","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103421"},"modified":"2026-01-14T15:15:50","modified_gmt":"2026-01-14T14:15:50","slug":"production-planning","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/functions\/production-planning\/","title":{"rendered":"Production Planning"},"content":{"rendered":"\n
\"HANNOVER<\/a><\/figure>\n\n\n\n\n\n
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\n \n \n \n \"Conducting\n <\/picture>\n <\/div>\n
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Conducting Experiments in Hybrid Learning Factories<\/h4>\n
The example of the InTraLab Potsdam<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nIndustrial production is undergoing rapid transformation through digitalization, automation and cyber-physical systems, creating new competence requirements for employees. Learning factories provide experiential environments for developing these competences. This article presents the Industrial Transformation Lab (InTraLab) as a hybrid learning factory combining physical demonstrators and digital simulations. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Industrial\n <\/picture>\n <\/div>\n
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Industrial Transformation via a Machining Learning Factory<\/h4>\n
A learning module to foster competencies for a sustainability-driven transformation<\/div>
Oskay Ozen<\/a> \n \"ORCID<\/a>, Victoria Breidling<\/a> \n \"ORCID<\/a>, Stefan Seyfried<\/a> \n \"ORCID<\/a>, Matthias Weigold<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 38-47 | DOI 10.30844\/I4SE.26.2.38<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
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Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/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 \"Data-Driven\n <\/picture>\n <\/div>\n
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Data-Driven Assistance Systems in the Working Environment<\/h4>\n
Efficient development of target group-specific BI dashboards in companies<\/div>
Martin Schmauder<\/a> \n \"ORCID<\/a>, Gritt Ott<\/a> \n \"ORCID<\/a>, Martin Hahmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Dashboards play a key role in informed business decisions. Based on findings from an action research process, this article shows how company-specific solutions can be systematically developed and bad investments avoided. The provision of IT capacities, securing data access, formulating requirements, and developing the data model prove to be particularly critical. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 136-143 | DOI 10.30844\/I4SE.25.5.130<\/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
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\n \n \n \n \"Automation\n <\/picture>\n <\/div>\n
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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
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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
\n
\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 \"Digital\n <\/picture>\n <\/div>\n
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\t\t

Digital Twins for Production<\/h4>\n
RAPIDZ \u2014 Resource analysis and process integration through digital twins<\/div>
Christian Salzig<\/a> \n \"ORCID<\/a>, Julia Burr<\/a> \n \"ORCID<\/a>, Sophie Hertzog<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In today\u2019s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 6-12 | DOI 10.30844\/I4SE.25.3.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"STAG\n <\/picture>\n <\/div>\n
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\t\t

STAG \u2014 Bridging Data from Shop Floor to IT World<\/h4>\n
An automated mapping approach for improved access to shop floor data<\/div>
Oliver Amft<\/a> \n \"ORCID<\/a>, Dovydas Girdvainis<\/a> \n \"ORCID<\/a>, Christoph Rathfelder<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Collecting data from different sources on the shop floor and making it accessible to different IT systems is one of the core tasks during the process of factory digitization. Due to the different protocols and interfaces, the data collection task comes with unique challenges. With the Sensor Technology Adapter Gateway (STAG), we present a solution that closes the gap between the shop floor and the IT system\u2019s backend. STAG is an industry-grade middleware that automates translations between data models and protocols. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 14-22 | DOI 10.30844\/I4SE.25.3.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n8<\/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-103421","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\/103421","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=103421"}],"version-history":[{"count":2,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103421\/revisions"}],"predecessor-version":[{"id":112699,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103421\/revisions\/112699"}],"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=103421"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}