{"id":94535,"date":"2022-06-15T12:00:00","date_gmt":"2022-06-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=94535"},"modified":"2025-08-05T16:08:47","modified_gmt":"2025-08-05T14:08:47","slug":"mes-for-manufacturing-smes-adapted-procedure-model-and-tool-for-an-effcient-selection","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/mes-for-manufacturing-smes-adapted-procedure-model-and-tool-for-an-effcient-selection\/","title":{"rendered":"MES for Manufacturing SMEs"},"content":{"rendered":"

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
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Decentralized Coordination of AMRs<\/h4>\n
Regulations for Autonomous Mobile Robots<\/div>
Peter Nyhuis<\/a> \n \"ORCID<\/a>, Manuel Savadogo<\/a>, Malte Stonis<\/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 increasing automation of intralogistics requires flexible and resilient control concepts for Autonomous Mobile Robots (AMR). While centralized coordination approaches enable stringent control, they quickly reach their limits in terms of scalability and robustness. This paper therefore presents regulations for the decentralized coordination of AMR within the framework of the ORPHEUS project. The focus is on translating known decentralized decision-making principles into a rule framework tailored to industrial material flow scenarios, addressing both operational task assignment and safety-related conflict situations. ORPHEUS thus makes a significant contribution to the methodological structuring, parameterization, and practical transferability of decentralized coordination logics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 96-105<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Immersive\n <\/picture>\n <\/div>\n
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Immersive Human Digital Twins for Industry 4.0<\/h4>\n
Supporting adaptive human-centric production by integrating cognitive and physical states<\/div>
Tajbeed A. Chowdhury<\/a> \n \"ORCID<\/a>, Eric Wagner<\/a> \n \"ORCID<\/a>, Paul Motzki<\/a> \n \"ORCID<\/a>, Martina Lehser<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rapid advancement of immersive technologies has created new opportunities to transform human-machine collaboration in industry. This paper presents an immersive platform with a digital twin that combines both physical and cognitive characteristics of human dynamics. By integrating multimodal sensing, human biomechanics, and cognitive state into digital twin technology, the proposed system enhances operational safety and ensures better ergonomics. The main argument is that human digital twins are not only desirable but essential for next-generation industrial systems. We discuss the limitations of existing human modeling approaches, outline the conceptual foundations of human digital twins, and demonstrate their industrial relevance across safety, productivity, ergonomics and sustainability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 6-13 | DOI 10.30844\/I4SE.26.3.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Serious\n <\/picture>\n <\/div>\n
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Serious Games as a Training Tool<\/h4>\n
Game mechanics design to promote resilience<\/div>
Annika Lange<\/a> \n \"ORCID<\/a>, Thomas Knothe<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Unforeseen events are increasingly challenging manufacturing companies. Being resilient during crises is becoming a key competence. Serious games (SG) can help make resilience-building processes more transparent. This article derives specific requirements for SG from different phases of resilience and shows how these can be implemented in game mechanics in order to effectively support the training of resilience. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 98-104<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"From\n <\/picture>\n <\/div>\n
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From Brownfield to Industry 4.0<\/h4>\n
Learning factories as training and testing environment for digital transformation<\/div>
Jakob Weber<\/a>, Sven V\u00f6lker<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>To succeed in their digital transformation, manufacturing companies need engineers with in-depth knowledge of key technologies and concepts, and a profound understanding of the transition from Industry 3.0 to Industry 4.0. This article describes the concept of a learning factory that is continuously subjected to a digital transformation, thereby creating an environment for the development of transformation competencies. The concept of digital transformation is based on digital worker assistance systems and multi-agent systems for production control. These enable the incremental integration of existing resources into the digitalized factory. The learning factory is not presented to students as a completed solution. Instead, it is continuously developed further as part of student projects. This way, it contributes directly to the qualification of personnel for the implementation of Industry 4.0. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 88-96<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

AI Colleagues?<\/h4>\n
Competence requirements and training for AI use in industry<\/div>
Swetlana Franken<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Artificial intelligence is fundamentally changing tasks, roles, and skills in (industrial) companies. Increasingly, it acts as a colleague, preparing decisions, supporting processes, and interacting with people. This article highlights key competence requirements for AI use in industry, presents an integrated competence model, and outlines practical strategies for the transfer of skills. The aim is to prepare companies and employees for humane, competence-oriented AI implementation that combines technological efficiency with human creativity and judgment. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 2 | Pages 78-86<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Operationalizing\n <\/picture>\n <\/div>\n
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\t\t

Operationalizing Ethical AI with tachAId<\/h4>\n
Validating an interactive advisory tool in two manufacturing use cases<\/div>
Pavlos Rath-Manakidis<\/a>, Henry Huick<\/a>, Bj\u00f6rn Kr\u00e4mer<\/a> \n \"ORCID<\/a>, Laurenz Wiskott<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of\u2014from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool\u2019s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI 10.30844\/I4SE.26.1.48<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Manufacturing SMEs are structurally di erent from other companies and therefore have special requirments for Manufacturing Execution Systems (MES) and the associated software selection. Process models must therefore be adapted. For the identication of requirements, a systematic categorization based on existing models was performed. With the help of an existing market research platform, further selection steps were developed in order to provide a useful and easy-to-use tool for SMEs, which enables them to select efficiently sufficient MES-software according to their needs.<\/p>\n","protected":false},"featured_media":95899,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[79449],"product_cat":[79304],"topic":[68206],"technology":[71524,67596],"knowhow":[],"industry":[68742],"writer":[83482,83480,83481],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-94535","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-digitalisierung-en","product_cat-articles","topic-industry-4-0","technology-additive-manufacturing","technology-software-en","industry-sme","writer-daniel-miller-en","writer-rainer-eber-en","writer-steffen-schwarzer-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Eber_MES_IM-3_2022-1400x788-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Christoph Brocks","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Manufacturing SMEs are structurally di erent from other companies and therefore have special requirments for Manufacturing Execution Systems (MES) and the associated software selection. Process models must therefore be adapted. For the identication of requirements, a systematic categorization based on existing models was performed. With the help of an existing market research platform, further selection…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/94535","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\/95899"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=94535"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=94535"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=94535"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=94535"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=94535"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=94535"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=94535"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=94535"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=94535"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=94535"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=94535"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=94535"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=94535"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}