{"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\/from-private-law-to-private-ordering\/\">\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\/Romeike_AdobeStock_2042534786_DC-Studio-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Romeike_AdobeStock_2042534786_DC-Studio-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Romeike_AdobeStock_2042534786_DC-Studio-196x180.jpg\" alt=\"From Private Law to Private Ordering\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"From Private Law to Private Ordering\">                  <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;\">From Private Law to Private Ordering<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Rule-making through industrial digital platforms<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/pia-c-romeike\/\">Pia C. Romeike<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/frederik-baer\/\">Frederik B\u00e4r<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Industrial digital platforms are considered the driving force behind digital value creation, yet legally they largely operate in a gray area. This article examines what defines industrial digital platforms and what legal framework applies to them. Existing platform regulations apply only to a limited extent to industrial digital platforms, which is why these platforms often establish their own legal framework through their terms and conditions. The article also explores the implications of European regulation, power asymmetries, and the opportunities and limitations of private regulation.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 98-105 | DOI <a style=\"font-weight:bold !important;\" href=\"10.30844\/I4SD.26.4.10\" target=\"_blank\">10.30844\/I4SD.26.4.10<\/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\/industry-4-0-digitalization-limbo\/\">\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\/05\/Donhauser_AdobeStock_507850396_Gorodenkoff-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_507850396_Gorodenkoff-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_507850396_Gorodenkoff-196x180.webp\" alt=\"Industry 4.0\u2014Progress and Digitalization in Limbo\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Industry 4.0\u2014Progress and Digitalization in Limbo\">                  <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;\">Industry 4.0\u2014Progress and Digitalization in Limbo<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Status of sustainable transformation and digitalization in production engineering<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/christian-donhauser\/\">Christian Donhauser<\/a> <a href=\"https:\/\/orcid.org\/0009-0009-0366-1828\" 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-riepl\/\">Daniel Riepl<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/industry-4-0-digitalization-limbo\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\n\t\t\t                      <\/div>Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 56-60<\/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\/application-potentials-of-chinese-knowledge-platforms\/\">\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\/06\/Braun-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Braun-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Braun-196x180.jpg\" alt=\"Application Potentials of Chinese Knowledge Platforms\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Application Potentials of Chinese Knowledge Platforms\">                  <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;\">Application Potentials of Chinese Knowledge Platforms<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Digital platforms for knowledge transfer in research and education<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/yunhao-su\/\">Yunhao Su<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/martin-braun-en\/\">Martin Braun<\/a> <a href=\"https:\/\/orcid.org\/0000-0003-0857-6760\" 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                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/application-potentials-of-chinese-knowledge-platforms\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\n\t\t\t                      <\/div>Knowledge drives innovation, which is why digital platforms are increasingly used for knowledge transfer. The People\u2019s Republic of China (PRC) is a global leader in digitalization and digital platforms are central to Chinese knowledge transfer and innovation systems. This study supplements theoretical concepts of knowledge transfer with empirical findings on the (further) development of relevant knowledge platforms. It examines the influence of specific design features on the functionality and quality of digital knowledge platforms. A literature review identifies seven condensed success criteria. Nine leading Chinese knowledge platforms are categorized based on their transfer logic and functional scope. Online survey participants assess the platform-specific manifestations of the identified criteria and highlight potential and areas for improvement in platform-based knowledge transfer.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 84-93<\/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\/vr-training-for-multimodal-cobot-interaction\/\">\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\/06\/zoller-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/zoller-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/zoller-196x180.jpg\" alt=\"VR Training for Multimodal Cobot Interaction\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"VR Training for Multimodal Cobot Interaction\">                  <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;\">VR Training for Multimodal Cobot Interaction<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Virtual learning environments for  collaborative robots<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/christoph-s-zoller-en\/\">Christoph S. Zoller<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/justus-langer\/\">Justus Langer<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/kristoffer-waldow\/\">Kristoffer Waldow<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-5176-7530\" 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\/merle-meyer\/\">Merle Meyer<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/arnulph-fuhrmann\/\">Arnulph Fuhrmann<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-5118-5461\" 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                     The VIRAMM research project is developing and prototyping a VR-based training concept for the integration of collaborative robots (cobots) in assembly-oriented U-cells. Since the benefits of cobots depend heavily on process, layout, and role integration, VIRAMM addresses the previously lacking consistent scenario design for variant comparisons with Key Performance Indicator (KPI)-based evaluation.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 106-112<\/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\/decentralized-coordination-of-amrs\/\">\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\/06\/Savadogo-640x325.jpg\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Savadogo-196x180.jpg\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Savadogo-196x180.jpg\" alt=\"Decentralized Coordination of AMRs\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Decentralized Coordination of AMRs\">                  <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;\">Decentralized Coordination of AMRs<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Regulations for Autonomous Mobile Robots<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/manuel-savadogo\/\">Manuel Savadogo<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/malte-stonis-en\/\">Malte Stonis<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-5957-3469\" 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\/peter-nyhuis-en\/\">Peter Nyhuis<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-4509-4114\" 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\/juergen-hupp\/\">J\u00fcrgen Hupp<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/decentralized-coordination-of-amrs\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\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               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 96-105<\/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\/site-assessment-for-flexible-intralogistics-in-brownfield-sites\/\">\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\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/06\/Schierbaum_AdobeStock_1925965279_MaryAnn-196x180.webp\" alt=\"Site Assessment for Flexible Intralogistics in Brownfield Sites\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Site Assessment for Flexible Intralogistics in Brownfield Sites\">                  <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;\">Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Innovative decision support in practice<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/jolanda-schierbaum\/\">Jolanda Schierbaum<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/carsten-feldmann-en\/\">Carsten Feldmann<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/lars-renhof\/\">Lars Renhof<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/site-assessment-for-flexible-intralogistics-in-brownfield-sites\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\n\t\t\t                      <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/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}]}}