{"id":107624,"date":"2023-02-15T12:00:00","date_gmt":"2023-02-15T12:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=107624"},"modified":"2025-02-05T16:55:43","modified_gmt":"2025-02-05T15:55:43","slug":"integration-ai-factory-control","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/integration-ai-factory-control\/","title":{"rendered":"Integration of Artificial Intelligence into Factory Control"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Artificial intelligence can preliminarily be defined as the development of information systems capable of completing tasks that would normally require human intelligence. The components that make up an AI usually include a suitable knowledge representation, reasoning mechanisms and, especially more recently, the ability to independently expand the knowledge base through learning. <a href=\"https:\/\/industry-science.com\/en\/articles\/machine-learning-ml-production\/\">Deep Learning<\/a>, with which it is possible for an AI to learn without human supervision through a multi-layered structure of neural networks (Figure 1) that classify characteristics, has been developed for this purpose in particular.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"644\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-1024x644.jpg\" alt=\"Difference between traditional Machine Learning and Deep Learning\" class=\"wp-image-100589\" style=\"width:645px;height:auto\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-1024x644.jpg 1024w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-scaled-510x321.jpg 510w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-scaled-64x40.jpg 64w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-597x375.jpg 597w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-768x483.jpg 768w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-465x292.jpg 465w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-1536x965.jpg 1536w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.03-2048x1287.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Figure 1: Difference between traditional Machine Learning and Deep Learning [1].<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Goal of AI use in factory control<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In high-variation series manufacturing, factory control pursues the goal of achieving the best possible task processing order. The different series variants have very different throughput times, and planning that fails to account for this leads to uneven cycle times and below-average utilization of assembly line personnel.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Use of AI to complement the methods previously used in this area should make the following improvements achievable:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shortened throughput times enabled by sequence planning that accounts for the variant-specific processing time<\/li>\n\n\n\n<li>Consideration of dependencies within a high-variation series and wide array of variants<\/li>\n\n\n\n<li>Optimization of sequence organization through appropriate classification and forecasting ability<\/li>\n\n\n\n<li>Optimized compilation of variants within different manufacturing scenarios<\/li>\n\n\n\n<li>High-quality, AI-supported planning of sequence compilations<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Addressing the specific problem<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The product range of the company under consideration is characterized by a high number of variants (approx. 1,300 unique technical characteristics, combined with 10 construction variants). The company uses a designated control center for all manufacturing planning and control. The planning element of this task extends from the coordination of all manufacturing subsections (forming techniques, painting, assembly, logistics) to the timing of outgoing goods. A particular focus is on designing an optimal sequence for variant manufacturing in terms of throughput time and forecasting of resource requirements (employees, interfaces to internal and external suppliers in accordance with Just in Time (JIT) and Just in Sequence (JIS) principles).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This leads to a high degree of planning complexity. With the help of AI, this project plans to implement an intelligent factory control that provides suggestions for optimal control of the factory based on the current situation and causal relationships from historical data, thus enabling the control center to make improved decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Procedure for AI integation in factory control<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The goals outlined above are to be achieved with the help of four AI components, which provide the relevant Deep Learning model types through which the AI can learn to solve this task (see Fig. 2).<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"505\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-1024x505.jpg\" alt=\"Structuring the project\" class=\"wp-image-100591\" style=\"width:649px;height:auto\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-1024x505.jpg 1024w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-scaled-510x251.jpg 510w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-scaled-64x32.jpg 64w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-761x375.jpg 761w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-768x379.jpg 768w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-764x376.jpg 764w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-514x253.jpg 514w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-1536x757.jpg 1536w, https:\/\/industry-science.com\/wp-content\/uploads\/2024\/03\/Bildschirmfoto-2024-03-26-um-13.10.30-2048x1010.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Figure 2: Structuring the project.<\/em><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The project was thus divided into four sub-projects as shown in Figure 2, each of which individually delivers a benefit for the client. The overall objective is achieved by combining the four sub-projects. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Subproject 1: Classification<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This subproject provides a model for suitable classification of the different variants according to acceptable target dimensions. The aim is to evaluate the variants with regard to achieving certain goals within different target dimensions. This initial project forms the basis for the following sub-projects. For example, the categories \u201cshort\u201d, \u201cmedium\u201d, or \u201clong\u201d are suggested for variant throughput.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same procedure is then followed for the other target dimensions. In order to solve this task, the relevance of the individual variant features on throughput time deviations is examined, for example by analyzing which features lead to longer times spent at which workplaces. These analyses are both exploratory, with a focus on identifying clusters, and exploitative in nature, with a focus on classification of variant characteristics based on where they overlap.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This subproject resulted in an improved and more effective characterization of variant characteristics. The benefit of the implementation of this subproject to the client is improvement of the client\u2019s manufacturing processes by orienting the compilation of variant feature sequences on this static characterization. Further iterations of the approach outlined above were conducted to improve the quality of the classification and thus optimize the effectiveness of the results.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Subproject 2: Forecast<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This subproject consists of creating models for suitable forecasting of variants and manufacturing scenarios according to different selectable target dimensions. The aim is for the AI to be able to forecast manufacturing process depending on different sequences of variant characteristics. In order to accomplish this, the dynamic relevance of different variant characteristics during manufacturing is examined.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, analysis into which features lead to higher throughput times at which workplaces is carried out. A forecast of the expected downtime, number of errors and failure rate can then be based on this. The forecasting ability is then extended to further target dimensions. The result of this subproject include an improved dynamic and successful characterization of the variant characteristics during manufacturing. It is possible to improve manufacturing processes by using dynamic characterization to predict the most suitable combination of variant characteristic sequences. Further iterations were conducted to improve forecast quality in this subproject and thus further support the generation of an optimal manufacturing plan.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Subproject 3: Optimization<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In this subproject, the compilation of vehicle variants is optimized according to the manufacturing scenarios considered according to various selectable target dimensions. For this purpose, the variant characteristic sequences are analyzed with a view to how these are integrated within the overall manufacturing context.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, the subproject investigates how results from various AI components (beyond the focus of subprojects 1 and 2) can be integrated to improve manufacturing throughput. The variables this subproject seeks to optimize are expected downtime, number of errors and failure rate. Further optimization variables are also checked.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This subproject also results in the ability to generate holistic recommendations for actions that could be taken to optimize manufacturing throughput time. The focus is now on overall optimization of the manufacturing process under consideration. This enables sensitivity analyses and plausibility tests to be run for individual scenarios, e.g. with regard to sequence planning or to the effectiveness of implementing certain measures to improve economic manufacturing objectives. A significant benefit for the client here is the ability to improve an entire manufacturing area with regard to the selected target dimension, and to implement more robust manufacturing planning that takes AI forecasts into account. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Subproject 4: Integration<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This subproject is consists of an examination of the client&#8217;s existing planning procedures and the harmonization of these with AI components. The aim is to optimize manufacturing planning by putting together product variants according to different manufacturing scenarios and selectable target dimensions. To this end, the subproject examines how AI planning processes can be connected to and improve the efficiency of existing planning processes within the given context.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Other state-of-the-art approaches to sequence planning, such as an approach for more systematic movement through the solution space, are also examined in terms of their integrability [2]. Among other things, this subproject examines whether extending planning for additional areas or use additional sequence planning mechanisms will lead to reduced throughput times. The planning horizon is the monthly, weekly or daily capacity utilization of the manufacturing plant. Here too, multiple iterations were carried out to improve the planning and sequencing quality. Overall, the four-part project creates a uniform planning basis that combines different planning approaches, some of which already exist, with newly created AI components.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Benefit: Manufacturing processes can be improved by basing the planning of the technical code sequence compilation on efficient approaches. This leads to an overall improvement of the manufacturing process with regard to the selected target dimension.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This project shows that the effective integration of AI components must first be preceded by a classification of the available data. Another challenge lies in the integration of the planning mechanisms already used by a client and in achieving overall optimization of multiple economic manufacturing goals. However, once these have been achieved, nothing stands in the way of further optimization through the use of cyber-physical systems [3]!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>The author wishes to thank Dr. Sander Lass, Dr. Edzard Weber, and Marcus Grum for their valuable input<\/em><\/p>\n<hr><div class=\"gito-pub-content-bibliography\"><h2>Bibliography <\/h2>[1] URL: www.guru99.com\/images\/tensorflow\/083018_0454_MachineLear5.png, accessed Oct 11, 2019.\r<br>[2] Weber, E., Tiefenbacher, A., Gronau, N.: Need for Standardization and Systematization of Test Data for Job-Shop Scheduling. In: Data 2019, 4 (1), 32; DOI: doi.org.10.3390\/ data4010032, accessed Oct 11, 2019.\r<br>[3] Lass, S.: Nutzenvalidierung cyber-physischer Systeme in komplexen Fabrikumgebungen. Berlin (2017).<\/div><div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\"><h2>Your downloads<\/h2><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"107624\" data-userid =\"0\" data-filename=\"I4S_01-2023_Gronau.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF<\/button><\/div><br>Solutions: <span class=\"gito-pub-tag-element\"><a href=\"\/en\/functions\/production-control\/\">Production Control<\/a><\/span> <div class=\"gito-pub-tags-social-share\" style=\"display:flex;justify-content:space-between;\"><div>Tags: <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/ai-en\/\">AI<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/ai-in-factories\/\">AI in factories<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/ai-integrated-manufacturing\/\">AI-integrated manufacturing<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/artificial-intelligence\/\">artificial intelligence<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/deep-learning-en\/\">Deep Learning<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/factory-control\/\">factory control<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/high-variation-series-manufacturing\/\">high-variation series manufacturing<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/machine-learning-en\/\">machine learning<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/smart-factory-en\/\">Smart Factory<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/throughput-optimization\/\">throughput optimization<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/variant-sequence-planning\/\">variant sequence planning<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/tag\/variants\/\">variants<\/a><\/span> <br>Industries: <span class=\"gito-pub-tag-element\"><a href=\"https:\/\/industry-science.com\/en\/industries\/manufacturing-en\/\">Manufacturing<\/a><\/span> <\/div><div><div class=\"social-icons share-icons share-row relative\" ><a href=\"whatsapp:\/\/send?text=Integration%20of%20Artificial%20Intelligence%20into%20Factory%20Control - https:\/\/industry-science.com\/en\/articles\/integration-ai-factory-control\/\" data-action=\"share\/whatsapp\/share\" class=\"icon button circle is-outline tooltip whatsapp show-for-medium\" title=\"Share on WhatsApp\" aria-label=\"Share on WhatsApp\"><i class=\"icon-whatsapp\" aria-hidden=\"true\"><\/i><\/a><a href=\"https:\/\/www.facebook.com\/sharer.php?u=https:\/\/industry-science.com\/en\/articles\/integration-ai-factory-control\/\" data-label=\"Facebook\" onclick=\"window.open(this.href,this.title,'width=500,height=500,top=300px,left=300px'); 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return false;\" target=\"_blank\" class=\"icon button circle is-outline tooltip linkedin\" title=\"Share on LinkedIn\" aria-label=\"Share on LinkedIn\" rel=\"noopener nofollow\"><i class=\"icon-linkedin\" aria-hidden=\"true\"><\/i><\/a><\/div><\/div><\/div><hr style=\"margin-top:0px;\">\n<h2 class=\"gito-pub-frontend-post-headline\">You might also be interested in<\/h2>\n<!-- GITO_PUB_POST start flex-container -->\n<div class=\"gito-pub-flex-container\">\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-knowledge-management\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-1-196x180.webp\" alt=\"Generative AI in Organizational Knowledge Management\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Generative AI in Organizational Knowledge Management\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Generative AI in Organizational Knowledge Management<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">AI literacy as a prerequisite for augmentation<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/uta-wilkens-en\/\">Uta Wilkens<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-7485-4186\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/valentin-langholf-en\/\">Valentin Langholf<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-0440-4665\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/niklas-obermann-en\/\">Niklas Obermann<\/a> <a href=\"https:\/\/orcid.org\/0009-0004-3817-3203\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 118-126 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.14\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.14<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/planning-vertical-factories\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kremslehner_AdobeStock_309590593_Aleksei-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kremslehner_AdobeStock_309590593_Aleksei-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kremslehner_AdobeStock_309590593_Aleksei-196x180.webp\" alt=\"Systematically Planning Vertical Factories\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Systematically Planning Vertical Factories\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Systematically Planning Vertical Factories<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Developing factory types as a foundation for intelligent planning support<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/nikolaus-kremslehner\/\">Nikolaus Kremslehner<\/a> <a href=\"https:\/\/orcid.org\/0009-0007-4630-7736\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/sebastian-schlund-en\/\">Sebastian Schlund<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-8142-0255\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/wilfried-sihn-en\/\">Wilfried Sihn<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Vertical factories reduce land use and harness the advantages of urban production. However, their multi-story structure significantly increases planning complexity. To counter such effects, this paper presents a typology that combines proven solutions for recurring challenges in the planning and operation of vertical factories. Relevant characteristics were examined using real-world case studies and structured within a morphological framework. This framework was consolidated into factory types that provide guidance for conceptual planning and serve as the foundation for intelligent planning support.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 68-76 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.6\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.6<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-based-building-inspection\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/sender_AdobeStock_227079093_Aisyaqilumar-196x180.webp\" alt=\"AI-Based Building Inspection for Large Structures\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Based Building Inspection for Large Structures\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Based Building Inspection for Large Structures<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">A new approach to construction progress monitoring<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/jan-sender-en\/\">Jan Sender<\/a> <a href=\"https:\/\/orcid.org\/0009-0003-9697-5709\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/konrad-jagusch-en\/\">Konrad Jagusch<\/a> <a href=\"https:\/\/orcid.org\/0009-0001-7454-1657\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/michael-geist\/\">Michael Geist<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-2780-7538\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/david-jericho\/\">David Jericho<\/a> <a href=\"https:\/\/orcid.org\/0009-0001-8932-8701\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/christian-scharr\/\">Christian Scharr<\/a> <a href=\"https:\/\/orcid.org\/0009-0003-6300-4682\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 78-84 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.9\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.9<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/foundation-models-in-industrial-robotics\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kuhlenkoetter_AdobeStock_2050908266_phonlamaiphoto-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kuhlenkoetter_AdobeStock_2050908266_phonlamaiphoto-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/kuhlenkoetter_AdobeStock_2050908266_phonlamaiphoto-196x180.webp\" alt=\"Foundation Models in Industrial Robotics\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Foundation Models in Industrial Robotics\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Foundation Models in Industrial Robotics<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Requirements for AI-supported assistance in production and logistics<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/bernd-kuhlenkoetter-en\/\">Bernd Kuhlenk\u00f6tter<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-5015-7490\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/daniel-syniawa-en\/\">Daniel Syniawa<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-9061-5663\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Foundation models are increasingly changing the way robots are used in industry. Instead of writing complex programs line by line, programmers will soon be able to collaborate more closely with AI systems, describe tasks, and review generated solutions. This shifts their role from purely generating code to conceptual, supervisory, and validation activities. This article highlights the new possibilities that large AI models create for robot programming and the changes they entail for work and required skills in industrial practice.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 16-23 | DOI <a style=\"font-weight:bold !important;\" href=\"10.30844\/I4SE.26.5.2\" target=\"_blank\">10.30844\/I4SE.26.5.2<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-driven-organization-as-a-new-work-paradigm\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/hoelzle_AdobeStock_1913936617_Chaosamran_Studio-640x325.png\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/hoelzle_AdobeStock_1913936617_Chaosamran_Studio-196x180.png\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/hoelzle_AdobeStock_1913936617_Chaosamran_Studio-196x180.png\" alt=\"AI-Driven Organization as a New Work Paradigm\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Driven Organization as a New Work Paradigm\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Driven Organization as a New Work Paradigm<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Implications for individual and organizational change<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/katharina-hoelzle\/\">Katharina H\u00f6lzle<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9733-4650\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/leonie-krauch\/\">Leonie Krauch<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/wolfgang-beinhauer\/\">Wolfgang Beinhauer<\/a> <a href=\"https:\/\/orcid.org\/0009-0001-3812-7715\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/carsten-schmidt\/\">Carsten Schmidt<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/josephine-hofmann\/\">Josephine Hofmann<\/a> <a href=\"https:\/\/orcid.org\/0000-0003-4453-7339\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Is it sufficient to train employees in the use of AI tools, or does the AI organization require an entirely new set of competencies? This paper introduces a digital enablement model comprising three competency dimensions and demonstrates, through an upskilling program implemented at the Fraunhofer Institute for Industrial Engineering IAO, how organizations can sustainably bridge the AI adoption gap.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 94-100 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.11\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.11<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/designing-effective-ai-certification\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-640x325.png\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-196x180.png\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-196x180.png\" alt=\"Designing Effective AI Certification\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Designing Effective AI Certification\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Designing Effective AI Certification<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Insights from established certification domains for the standardization of human-centered AI<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/katharina-morsch\/\">Katharina Morsch<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-9511-6569\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Certification for human-centered AI is becoming increasingly important\u2014as a source of competitive differentiation, a response to regulatory expectations, and a mechanism for fostering human-centered work environments. But how can organizations determine whether the underlying requirements are truly embedded in practice? Drawing on expert interviews from established certification domains, this paper shows that the decisive question is answered not during the audit itself, but in the period between audit cycles. Ultimately, it is not the certificate that matters, but the commitment of organizational leadership and the organization as a whole to engage seriously in the certification process and its continuous implementation.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 86-92 | DOI <a style=\"font-weight:bold !important;\" href=\"10.30844\/I4SE.26.4.10\" target=\"_blank\">10.30844\/I4SE.26.4.10<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n<\/div>\n<!-- GITO_PUB_POST end flex-container -->\n","protected":false},"excerpt":{"rendered":"<p>With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.<\/p>\n","protected":false},"featured_media":107625,"menu_order":0,"template":"","categories":[79167,79298],"tags":[80169,79078,79079,4723,79359,79075,79076,79574,79671,79080,79077,71859],"product_cat":[],"topic":[68760,68206],"technology":[67790,71297],"knowhow":[],"industry":[79494],"writer":[80397],"content-type":[],"potential":[],"solution":[67776],"glossary":[],"class_list":["post-107624","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","tag-ai-en","tag-ai-in-factories","tag-ai-integrated-manufacturing","tag-artificial-intelligence","tag-deep-learning-en","tag-factory-control","tag-high-variation-series-manufacturing","tag-machine-learning-en","tag-smart-factory-en","tag-throughput-optimization","tag-variant-sequence-planning","tag-variants","topic-factory-design","topic-industry-4-0","technology-artificial-intelligence","technology-machine-learning","industry-manufacturing-en","writer-norbert-gronau-en","solution-production-control","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/02\/AdobeStock_130150949-min-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/107624","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\/107625"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=107624"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=107624"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=107624"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=107624"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=107624"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=107624"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=107624"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=107624"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=107624"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=107624"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=107624"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=107624"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=107624"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}