{"id":92307,"date":"2015-08-15T12:00:00","date_gmt":"2015-08-15T12:00:00","guid":{"rendered":"https:\/\/industry-science.com\/artikel\/levelling-production-in-the-process-industry\/"},"modified":"2015-08-15T12:00:00","modified_gmt":"2015-08-15T12:00:00","slug":"levelling-production-in-the-process-industry","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/levelling-production-in-the-process-industry\/","title":{"rendered":"Levelling Production in the Process Industry"},"content":{"rendered":"

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Learning Factories for the Future of Manufacturing in Brazil<\/h4>\n
Advancing manufacturing through technology and skills development<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nManufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory F\u00e1brica do Futuroin S\u00e3o Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Serious\n <\/picture>\n <\/div>\n
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Serious Gaming and the Energy Transition<\/h4>\n
Collaborative knowledge generation and interactive understanding of complex interrelationships<\/div>
Janine Gondolf<\/a> \n \"ORCID<\/a>, Gert Mehlmann<\/a>, J\u00f6rn Hartung<\/a>, Bernd Schweinshaut<\/a>, Anne Bauer<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 62-69<\/div> <\/div>\n <\/div>\n <\/a>\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 \"Collaborative\n <\/picture>\n <\/div>\n
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Collaborative Robots in Production Environments<\/h4>\n
Employee qualification and acceptance for human-machine interaction<\/div>
Tobias Wienzek<\/a>, Mathias Cuypers<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The introduction of new technologies poses a major challenge, especially for small and medium-sized enterprises (SMEs). At the same time, SMEs must rise to this challenge in order to keep pace technologically and economically. Employee acceptance is an important factor in ensuring that both the introduction and the long-term use of a technology are successful. At the same time, the introduction process also has a central influence on acceptance in the long term. This article uses the implementation of collaborative robotics as an example for examining such an introduction process, identifying the key factors that influence employee acceptance and the important role played by advanced employee training. It serves to highlight how the introduction process and employee training are seamlessly interlinked. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 14-21 | DOI 10.30844\/I4SE.26.2.14<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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\t\t

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
\n \n
\n \n \n \n \"XAI\n <\/picture>\n <\/div>\n
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\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<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Volatility in market demand leads to temporary over- and under utilization of productive assets. Heijunka aims at de-coupling the production system 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 development of a Heijunka model for the process industry in order to close that gap.<\/p>\n","protected":false},"featured_media":97119,"menu_order":0,"template":"","categories":[],"tags":[70945,69553,80211,69335,80212,75129,69024,68691,69096],"product_cat":[3300],"topic":[70058,67701],"technology":[],"knowhow":[],"industry":[],"writer":[82518,82516,82517],"content-type":[],"potential":[],"solution":[67687,67776,67577],"glossary":[],"class_list":{"0":"post-92307","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"tag-heijunka","7":"tag-lean-management","8":"tag-lean-management-en","9":"tag-lean-production","10":"tag-lean-production-en","11":"tag-leveling","12":"tag-production-control","13":"tag-production-planning","14":"tag-toyota-production-system","15":"product_cat-article","16":"topic-lean-production-en","17":"topic-production-system","18":"writer-alexander-giering-en","19":"writer-carsten-feldmann-en","20":"writer-patrick-lueckmann-en","21":"solution-process-management","22":"solution-production-control","23":"solution-production-planning","24":"product","25":"first","26":"instock","27":"downloadable","28":"virtual","29":"sold-individually","30":"taxable","31":"purchasable","32":"product-type-article"},"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in-der-Prozessindustrie_IM-2015-4-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/feldmann_Produktionsnivellierung-in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Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Volatility in market demand leads to temporary over- and under utilization of productive assets. Heijunka aims at de-coupling the production system 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,…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/92307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/97119"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=92307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=92307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=92307"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=92307"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=92307"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=92307"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=92307"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=92307"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=92307"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=92307"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=92307"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=92307"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=92307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}