{"id":31551,"date":"2023-09-13T14:00:27","date_gmt":"2023-09-13T12:00:27","guid":{"rendered":"https:\/\/www.risc-software.at\/fachbeitraege\/fachbeitrag-industrial-ai\/"},"modified":"2026-03-10T14:23:52","modified_gmt":"2026-03-10T13:23:52","slug":"fachbeitrag-industrial-ai","status":"publish","type":"publication","link":"https:\/\/www.risc-software.at\/en\/technicalarticles\/fachbeitrag-industrial-ai\/","title":{"rendered":"Industrial AI: From raw data to a more efficient production landscape"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\">by Roxana Holom and Evans Doe Ocansey<\/h3>\n<div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group alignwide is-layout-constrained wp-block-group-is-layout-constrained\">\n<p><em>The industrial sector, like other areas, is currently going through a phase of digital transformation. This means that manufacturing companies are involved in various digitalization activities [1]. Within this context, industrial data and the way it is processed, visualized and used play an essential role.  <\/em><\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text has-media-on-the-right is-stacked-on-mobile is-vertically-aligned-center\"><div class=\"wp-block-media-text__content\">\n<p><strong>Contents<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Application perspectives of Industrial AI for manufacturing companies\n<ul class=\"wp-block-list\">\n<li>Pain Points<\/li>\n\n\n\n<li>Needs &amp; goals<\/li>\n\n\n\n<li>Added value through industrial AI<\/li>\n\n\n\n<li>Challenges in the application of Industrial AI<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>A systematic approach to industrial AI<\/li>\n\n\n\n<li>Conclusion: Industrial AI &#8211; the combination of expertise and data science<\/li>\n\n\n\n<li>References<\/li>\n\n\n\n<li>Authors<\/li>\n<\/ul>\n<\/div><figure class=\"wp-block-media-text__media\"><img decoding=\"async\" width=\"1024\" height=\"544\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-1024x544.jpg\" alt=\"\" class=\"wp-image-6313 size-full\" srcset=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-1024x544.jpg 1024w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-300x159.jpg 300w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-768x408.jpg 768w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-1536x816.jpg 1536w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_.jpg 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Application perspectives of Industrial AI for manufacturing companies<\/h2>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<p>Relying solely on technology does not create business value if the industry&#8217;s problems are not thoroughly examined. There are many ways in which industrial AI can contribute to the digital transformation of manufacturing. Some of the most appealing areas where it can be used are: Process applications for productivity improvement (i.e. smart production), product and service applications, insight applications for knowledge discovery (i.e. root cause determination, decision making) [2].  <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<figure class=\"wp-block-image size-large is-style-rounded\"><img decoding=\"async\" width=\"1024\" height=\"683\" sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417-1024x683.jpg\" alt=\"\" class=\"wp-image-6311\" srcset=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417-1024x683.jpg 1024w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417-300x200.jpg 300w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417-768x512.jpg 768w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417-1536x1025.jpg 1536w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/iStock-469974417.jpg 1920w\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p>Specific examples that fit into the above categories are two of our ongoing EU projects: <em>&#8220;Customizable AI-based in-line process monitoring platform for achieving zero-defect manufacturing in the PV industry&#8221; (Platform-Zero)<\/em> and <em>&#8220;Data and Metadata for advanced Digitalization of Manufacturing Industrial Lines&#8221; (<a href=\"https:\/\/metafacturing.eu\/\" target=\"_blank\" rel=\"noopener\">metaFacturing<\/a><\/em><em>)<\/em>. The Platform-Zero project aims to improve the production quality of photovoltaic systems while reducing manufacturing costs through zero-defect manufacturing. This is achieved by using non-destructive testing methods and technologies to detect, correct and prevent critical production defects at an early stage. The data is evaluated in real time in order to optimize the production process and improve product quality. The metaFacturing project focuses on the creation of a digitalized tool chain for the production of metal parts (casting and welding). Trustworthy AI and hybrid methods are analyzed and implemented to gain process insights, improve the efficiency of the production process (e.g.: optimization of process parameters) and product quality (e.g.: defect reduction).     <\/p>\n\n\n\n<p>In order to gain a comprehensive understanding of the topic, several key aspects are considered below, which are divided into four categories. First, the problems and needs of manufacturing companies are discussed. In addition, we look at the added value that industrial AI can offer as a solution to their problems, but also at the challenges that arise when applying industrial AI (see Figure 1).  <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"724\" sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-1024x724.png\" alt=\"\" class=\"wp-image-6315\" srcset=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-1024x724.png 1024w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-300x212.png 300w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-768x543.png 768w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-1536x1086.png 1536w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Industrial-AI-fuer-Fertigungsunternehmen-2048x1448.png 2048w\" \/><\/figure>\n\n\n\n<p><em>Figure 1: Key points of the application perspectives of Industrial AI for manufacturing companies<\/em><\/p>\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Pain points<\/h3>\n\n\n\n<p>Below we look at some of the key pain points that manufacturers face when it comes to successfully introducing and using industrial AI:<\/p>\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_4d6271c4db61e67549ffa8af902b667d\">\n    <h3 class=\" inline-block \">\n        Massive Datenmengen, die undurchl\u00e4ssig sind    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_4d6271c4db61e67549ffa8af902b667d\" class=\"collapse\" aria-labelledby=\"headingblock_4d6271c4db61e67549ffa8af902b667d\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die moderne Fertigung\u00a0generiert heute eine Vielzahl von Daten durch Einsatz von\u00a0technischen Systemen (Sensoren, Kameras). Es ergibt sich eine Mischung aus strukturierten und unstrukturierten Daten, die oft\u00a0so komplex und umfangreich sind, dass es schwierig ist, darin klare Muster und Erkenntnisse zu identifizieren. Au\u00dferdem ist es schwer zu sagen, welche Daten f\u00fcr die weiteren Analysen relevant sind und gespeichert werden sollten. Ein\u00a0konkretes\u00a0Beispiel ist die Produktion von Halbleitern. Enorme Mengen von Sensordaten, Prozessdaten und Qualit\u00e4tsdaten werden erzeugt. Diese Daten m\u00fcssen analysiert werden, um Abweichungen oder Fehler in den Produktionsprozessen zu erkennen. Aufgrund der Vielzahl von Datenquellen und -formaten wird eine Herausforderung sein, die relevanten Informationen zu extrahieren und zu interpretieren.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_c9f19e42a99f08c6fef1a81560e4d563\">\n    <h3 class=\" inline-block \">\n        Komplexe\/unerkennbare Zusammenh\u00e4nge    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_c9f19e42a99f08c6fef1a81560e4d563\" class=\"collapse\" aria-labelledby=\"headingblock_c9f19e42a99f08c6fef1a81560e4d563\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Industrielle Prozesse sind oft durch vielf\u00e4ltige Prozessparameter und Wechselwirkungen gepr\u00e4gt, die es herausfordernd machen, versteckte Zusammenh\u00e4nge zwischen den Daten zu erkennen. Nehmen wir als Beispiel die Herstellung von Gussteilen: In diesem Kontext sind die wechselseitigen Zusammenh\u00e4nge der Prozessparameter \u2013 wie die Schmelztemperatur, Gie\u00dfformtemperatur, Geschwindigkeit der ersten Phase und Geschwindigkeit der zweiten Phase \u2013 komplex, nicht linear und widerspr\u00fcchlich [3].<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_d48fb3647ce8c6f82bb5d917a486c61d\">\n    <h3 class=\" inline-block \">\n        Komplexe Entscheidungssituationen;\u00a0Ineffiziente Produktionsprozesse    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_d48fb3647ce8c6f82bb5d917a486c61d\" class=\"collapse\" aria-labelledby=\"headingblock_d48fb3647ce8c6f82bb5d917a486c61d\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die Entscheidungsfindung in der Fertigung erfordert die Ber\u00fccksichtigung zahlreicher Faktoren und Einschr\u00e4nkungen.\u00a0Wie wir im vorigen Abschnitt gesehen haben, ist die Bestimmung des optimalen Toleranzfensters f\u00fcr Parameter keine einfache Aufgabe. Bleiben wir im gleichen Kontext \u2013 dem Gie\u00dfen von Metallteilen \u2013 so stellen wir fest, dass diese Entscheidung Auswirkungen auf die Prozesseffizienz hat (d.h.: Metallteile werden von der Gie\u00dfmaschine automatisch als Ausschuss qualifiziert, wenn die Messungen au\u00dferhalb des Toleranzfensters liegen).<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_e10a9ebf31c422ebb98e5f9750ac5509\">\n    <h3 class=\" inline-block \">\n        \u00d6kologische Nachhaltigkeit    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_e10a9ebf31c422ebb98e5f9750ac5509\" class=\"collapse\" aria-labelledby=\"headingblock_e10a9ebf31c422ebb98e5f9750ac5509\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die Fertigungsindustrie steht vor der Herausforderung, umweltfreundliche Praktiken zu implementieren, um Ressourcenverbrauch und Emissionen zu reduzieren. Z.B.: Die Reduzierung des Wasser- und Energieverbrauchs in der Textilproduktion zur Minimierung des \u00f6kologischen Fu\u00dfabdrucks.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_756dec5b8f2500bae16c891eaaa9729e\">\n    <h3 class=\" inline-block \">\n        Ver\u00e4nderte Kundenbed\u00fcrfnisse    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_756dec5b8f2500bae16c891eaaa9729e\" class=\"collapse\" aria-labelledby=\"headingblock_756dec5b8f2500bae16c891eaaa9729e\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Kundenanforderungen \u00e4ndern sich st\u00e4ndig, und Hersteller m\u00fcssen agil sein, um Produkte anzupassen und den Marktanforderungen gerecht zu werden. Die Wahl der Werkstoffe wirkt sich erheblich auf die Qualit\u00e4t der Produkte aus. Der \u00dcbergang von herk\u00f6mmlichem Stahl zu fortschrittlichen Leichtbauwerkstoffen wie Kohlefaserverbundwerkstoffen f\u00fcr Karosseriebleche beispielsweise bringt aufgrund der einzigartigen Eigenschaften von Kohlenstoff komplexe Produktionsprozesse mit sich.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Needs &amp; goals<\/h3>\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_d4987541dae3011626719cdde46958af\">\n    <h3 class=\" inline-block \">\n        Erkennen von Korrelationen in Daten (Ermittlung der Grundursache)    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_d4987541dae3011626719cdde46958af\" class=\"collapse\" aria-labelledby=\"headingblock_d4987541dae3011626719cdde46958af\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Durch die Ermittlung der Grundursache eines Problems k\u00f6nnen Hersteller gezielte L\u00f6sungen implementieren, um ein erneutes Auftreten des Problems zu verhindern. Dazu muss man tief in die Daten eindringen, um Korrelationen und damit die zugrunde liegenden Faktoren zu entdecken, die wahrscheinlich f\u00fcr bestimmte Probleme oder Anomalien verantwortlich sind. Nehmen wir zum Beispiel ein Szenario in der Elektronikfertigung, bei dem eine bestimmte Charge von Produkten bei Qualit\u00e4tspr\u00fcfungen immer wieder durchf\u00e4llt. Durch eine Ursachenanalyse k\u00f6nnte herausgefunden werden, dass eine bestimmte Maschinenkomponente h\u00f6chstwahrscheinlich nicht korrekt kalibriert ist.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_16a2588efb94dc359a144efa2f520764\">\n    <h3 class=\" inline-block \">\n        Fr\u00fchzeitige Erkennung von Problemen    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_16a2588efb94dc359a144efa2f520764\" class=\"collapse\" aria-labelledby=\"headingblock_16a2588efb94dc359a144efa2f520764\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Durch fortgeschrittene Analyse von Produktionsdaten k\u00f6nnen Probleme fr\u00fchzeitig erkannt werden, noch bevor sie zu ernsthaften Fehlern oder Ausf\u00e4llen f\u00fchren. In der Energieerzeugung k\u00f6nnten ungew\u00f6hnliche Abweichungen im Stromverbrauch eines Generators auf ein potenzielles Problem hinweisen, das behoben werden muss, um einen Ausfall zu vermeiden.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_8951edda2ac5b1bf8457692bde15f6a9\">\n    <h3 class=\" inline-block \">\n        Strategische Datenerfassung, -speicherung und -vorverarbeitung    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_8951edda2ac5b1bf8457692bde15f6a9\" class=\"collapse\" aria-labelledby=\"headingblock_8951edda2ac5b1bf8457692bde15f6a9\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Unternehmen verlagern ihren Schwerpunkt von der Anh\u00e4ufung von Massendaten auf das strategische industrielle Datenmanagement. Die Optimierung des Datenbedarfs und der Datenverarbeitung steht auch im Einklang mit den Zielen der Europ\u00e4ischen Kommission. Eine intelligente Datenauswahl und -aufbereitung verringert die Notwendigkeit, gro\u00dfe Datenmengen und\/oder gro\u00dfe KI-Modelle zu sammeln, zu speichern, zu verarbeiten und zu \u00fcbertragen und damit den Energieverbrauch zu senken [4].<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_0d0e48cf77654dfc028d4aaa5604607c\">\n    <h3 class=\" inline-block \">\n        Datenauswertung als Grundlage f\u00fcr die Entscheidungsfindung; Ableitung von Gesch\u00e4ftswert aus Daten    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_0d0e48cf77654dfc028d4aaa5604607c\" class=\"collapse\" aria-labelledby=\"headingblock_0d0e48cf77654dfc028d4aaa5604607c\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Hersteller nutzen Datenanalysen, um fundierte Entscheidungen zu treffen. Ein Chemiewerk kann unmittelbare Erkenntnisse aus nahtlos integrierten Industriedaten gewinnen, die sich\u00a0<span class=\"inline-comment-marker valid\" data-ref=\"34e74c9d-6c6f-4771-8632-9a92b82fb66d\">vom Edge<\/span>\u00a0bis zur Cloud erstrecken. Dies kann durch die Fusion verschiedener Datenquellen erreicht werden, was eine agile Entscheidungsfindung im gesamten Unternehmen f\u00f6rdert. Bei komplexen Entscheidungsfindungen k\u00f6nnen diese Daten in Form von Modellen auch in Optimierungsmodelle integriert werden (<a class=\"external-link\" href=\"https:\/\/www.risc-software.at\/fachbeitraege-die-besseren-entscheidungen-treffen-dank-prescriptive-analytics\/\" rel=\"nofollow\">https:\/\/www.risc-software.at\/fachbeitraege-die-besseren-entscheidungen-treffen-dank-prescriptive-analytics\/<\/a>) und so die Verantwortlichen bei Planungsproblemen unterst\u00fctzen.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">Added value through industrial AI<\/h3>\n\n\n\n<p>Innovative approaches and the intelligent use of industrial AI are required to meet the demands of modern industry. In contrast to industrial AI models, general AI models are trained based on extensive plant data, which often does not cover the full range of possible operations. This is because general AI models do not take into account conditions for different purposes (e.g. safety, design) or conditions imposed by physical and chemical laws.  <\/p>\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_34d659005009267c0d4759de12c751dd\">\n    <h3 class=\" inline-block \">\n        Verbesserte Produktivit\u00e4t &amp; Qualit\u00e4tskontrolle    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_34d659005009267c0d4759de12c751dd\" class=\"collapse\" aria-labelledby=\"headingblock_34d659005009267c0d4759de12c751dd\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Industrielle KI tr\u00e4gt zur Verfeinerung des Qualit\u00e4tssicherungsprozesses bei, indem sie den Prozess automatisiert und Defekte fr\u00fchzeitig erkennt. Dadurch\u00a0<span class=\"inline-comment-marker\" data-ref=\"39d382b3-59c0-4a4f-a76b-f4d64ee342f0\">steigert sich die<\/span>\u00a0gesamte Produktions- und Produktqualit\u00e4t.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_760a8532df5613b594f621b4fdfc8c0f\">\n    <h3 class=\" inline-block \">\n        Neue Gesch\u00e4ftsmodelle    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_760a8532df5613b594f621b4fdfc8c0f\" class=\"collapse\" aria-labelledby=\"headingblock_760a8532df5613b594f621b4fdfc8c0f\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Industrielle KI erm\u00f6glicht die Umgestaltung von Arbeitsprozessen und die Schaffung neuer Gesch\u00e4ftsmodelle, die auf datengetriebener Innovation beruhen.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_3b3469be83e96bab6b505602af3663ca\">\n    <h3 class=\" inline-block \">\n        H\u00f6here Effizienz    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_3b3469be83e96bab6b505602af3663ca\" class=\"collapse\" aria-labelledby=\"headingblock_3b3469be83e96bab6b505602af3663ca\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die Anwendung industrieller KI f\u00fchrt zur Optimierung des Energieverbrauchs, zur effizienten Nutzung von Materialien, zur Reduzierung von Abfall und zur Senkung der Kosten. Zus\u00e4tzlich erm\u00f6glicht sie die strategische Zuweisung freigegebener Ressourcen f\u00fcr kritische Aufgaben.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_83b002bdebe69eab235d976d9cd67036\">\n    <h3 class=\" inline-block \">\n        Integrierte Analyse von Produkt- und Prozessdaten    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_83b002bdebe69eab235d976d9cd67036\" class=\"collapse\" aria-labelledby=\"headingblock_83b002bdebe69eab235d976d9cd67036\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <div class=\"vc_tta-container\" data-vc-action=\"collapse\">\n<div class=\"vc_general vc_tta vc_tta-accordion vc_tta-color-grey vc_tta-style-classic vc_tta-shape-rounded vc_tta-o-shape-group vc_tta-controls-align-left\">\n<div class=\"vc_tta-panels-container\">\n<div class=\"vc_tta-panels\">\n<div id=\"1694608638604-f3c1f056-6d12\" class=\"vc_tta-panel vc_active\" data-vc-content=\".vc_tta-panel-body\">\n<div class=\"vc_tta-panel-body\">\n<div class=\"wpb_text_column wpb_content_element \">\n<div class=\"wpb_wrapper\">\n<p>Industrielle KI erm\u00f6glicht eine nahtlose Integration und Analyse von Daten aus Produktions- und Prozessabl\u00e4ufen. Dies erm\u00f6glicht fundierte Entscheidungen, um sowohl die Produktqualit\u00e4t als auch die Effizienz der Produktionslinie zu steigern.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n    <\/div>\n  <\/div>\n<\/div>\n\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Challenges in the application of Industrial AI<\/h2>\n\n\n\n<p>The key to a successful industrial AI application lies in transforming raw data into intelligent insights for rapid decision making. From the intricacies of data management and integration to the complexities of adapting AI models to real-world production environments, manufacturers must proactively address the following challenges. <\/p>\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_25a35ea5a7a2f6f0931029d34e5e861a\">\n    <h3 class=\" inline-block \">\n        Datenqualit\u00e4t    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_25a35ea5a7a2f6f0931029d34e5e861a\" class=\"collapse\" aria-labelledby=\"headingblock_25a35ea5a7a2f6f0931029d34e5e861a\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Obwohl die Datenumgebung in der Industrie heutzutage eine Big-Data-Umgebung ist, gibt es eine Mischung aus strukturierten und unstrukturierten Daten, die von minderer Qualit\u00e4t sein k\u00f6nnen (z. B.: unausgewogene Daten, fehlende Datenpunkte, ungenaue Sensormessungen, Datendrift, inkonsistente Formate, begrenzter Umfang usw.).<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_afdab04069fa43024bed5f89dee1add0\">\n    <h3 class=\" inline-block \">\n        Entwicklung produktionsreifer KI-Modelle    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_afdab04069fa43024bed5f89dee1add0\" class=\"collapse\" aria-labelledby=\"headingblock_afdab04069fa43024bed5f89dee1add0\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Es fehlt ein systematischer Ansatz zur effizienten Entwicklung von KI-Modellen, die f\u00fcr den Einsatz in der Industrie bzw. f\u00fcr die Integration in den Produktionsprozess bereit sind. Neben Herausforderungen wie Datenkomplexit\u00e4t und -qualit\u00e4t, mangelndem Fachwissen und Interpretierbarkeit der Modelle, m\u00fcssen bei der Integration von KI-Modellen in bestehende Produktionssysteme auch Kompatibilit\u00e4tsprobleme und Ressourcenbeschr\u00e4nkungen ber\u00fccksichtigt werden.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_9b2f23cc977863235736e5264411eb62\">\n    <h3 class=\" inline-block \">\n        KI-Akzeptanz\/Vertrauensw\u00fcrdigkeit (Interpretierbarkeit, Vertrauen und Transparenz)    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_9b2f23cc977863235736e5264411eb62\" class=\"collapse\" aria-labelledby=\"headingblock_9b2f23cc977863235736e5264411eb62\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die Glaubw\u00fcrdigkeit von KI-Systemen in der Industrie kann beeintr\u00e4chtigt werden, wenn die Genauigkeit nicht ann\u00e4hernd perfekt ist, da diese Systeme kritische Sicherheits-, Zuverl\u00e4ssigkeits- und Betriebsfragen angehen k\u00f6nnten. Jedes Versagen der KI k\u00f6nnte negative wirtschaftliche und\/oder sicherheitstechnische Auswirkungen haben und vom Einsatz von KI-Systemen abhalten. Durch die Einhaltung der Anforderungen an vertrauensw\u00fcrdige KI (d.h.: in Anlehnung an \u201cThe Assessment List for Trustworthy Artificial Intelligence\u201d (ALTAI) [5]) werden die Datenanalyseergebnisse nachvollziehbar (u.a. interpretierbar und transparent) gemacht.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_a77acc4da910b5fd7a23503ec14c44fe\">\n    <h3 class=\" inline-block \">\n        Genauigkeit &amp; Geschwindigkeit    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_a77acc4da910b5fd7a23503ec14c44fe\" class=\"collapse\" aria-labelledby=\"headingblock_a77acc4da910b5fd7a23503ec14c44fe\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Produktionsprozesse erfordern schnelle Entscheidungen und die produzierten Werkst\u00fccke k\u00f6nnen teuer sein, daher m\u00fcssen KI-Anwendungen schnell reagieren, um Verschwendung und andere Folgen zu vermeiden. Im Gegensatz zu anderen KI-Systemen (z. B. Empfehlungssystemen) ist bei industriellen KI-Systemen au\u00dferdem eine sehr geringe Toleranz gegen\u00fcber falsch positiven und negativen Ergebnissen erforderlich, damit sie in der Produktion eingesetzt werden k\u00f6nnen.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"accordion\">\n  <div class=\"accordion-header p-1.5 md:px-3 md:py-2 flex items-center justify-between \" id=\"headingblock_ec7c245b64fb9e485134d8dbc66eb402\">\n    <h3 class=\" inline-block \">\n        Dom\u00e4nenverst\u00e4ndnis &amp; Entwicklung von anpassungsf\u00e4higen KI-Modellen    <\/h3>\n    <span class=\"accordion-icon-toggle inline-block\"><\/span>\n  <\/div>\n  <div id=\"collapseblock_ec7c245b64fb9e485134d8dbc66eb402\" class=\"collapse\" aria-labelledby=\"headingblock_ec7c245b64fb9e485134d8dbc66eb402\">\n    <div class=\"accordion-body p-1.5 md:p-3 \">\n      <p>Die Einbeziehung von Fachwissen ist ein Muss, um den Unterschied zwischen allgemeiner KI und industrieller KI deutlich zu machen. Die Dateningenieur*innen und Datenwissenschaftler*innen m\u00fcssen mit den Dom\u00e4nenexpert*innen zusammenarbeiten und Fachwissen in den Modellierungsprozess einbeziehen. Und um die Einbeziehung des Fachwissens zu maximieren, m\u00fcssen die entwickelten Modelle adaptiv lernen und die Erkenntnisse der Fachleute als Wissen akkumulieren.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">A systematic approach to industrial AI<\/h2>\n\n\n\n<p>As we saw in the previous section, the numerous challenges usually take a considerable amount of time until meaningful results are available from production. Sometimes this goal is not achieved because it is too complex and the focus is lost. This is why RISC Software GmbH pursues an agile approach (with strong involvement of the actual stakeholders) to AI-based data analysis in the industrial sector.  <\/p>\n\n\n\n<p>Figure 2 illustrates the proposed agile workflow suitable for an industrial AI project. The workflow begins with a continuous discourse between the industrial stakeholders and the team developing the AI solution (referred to as the AI team in the workflow). After detailed analysis by the stakeholders, the use cases and their requirements are defined. After further dialog with the industrial stakeholders, the AI team designs suitable solutions for the use cases. In order to model the appropriate data environment, the AI team creates a series of questionnaires to be completed by the data providers. These questionnaires form the basis for the requirements for data preparation and integration. Subsequently, the data providers deliver different types of data based on the requirements of the use case. The data engineers in the AI team then process, transform and load this data into a data lake. This data engineering process relies not only on the input of the AI team&#8217;s data scientists, but also on the input of industrial stakeholders, such as domain experts or process engineers. The data available in the Data Lake is thoroughly cleaned and prepared for analysis by the AI team&#8217;s data scientists. They perform exploratory data analysis and work with domain experts to further refine the data analysis process. The pre-processed data is then used to train AI models according to the use case specifications. The results of these AI models are then reviewed with domain experts to make them suitable for production.            <\/p>\n\n\n\n<p>The key aspect of this approach is the active involvement of domain experts throughout the entire design and implementation cycle of the AI solutions.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"724\" sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-1024x724.png\" alt=\"\" class=\"wp-image-6317\" srcset=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-1024x724.png 1024w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-300x212.png 300w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-768x543.png 768w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-1536x1086.png 1536w, https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Agile-like-workflow-for-industrial-AI-2048x1448.png 2048w\" \/><\/figure>\n\n\n\n<p><em>Figure 2: Agile workflow for industrial AI solutions<\/em><\/p>\n<\/div>\n<\/div><div class=\"wp-block-group-container alignfull \">\n<div class=\"wp-block-group alignwide is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Conclusion: Industrial AI &#8211; the combination of expertise and data science<\/h2>\n\n\n\n<p>The development of AI solutions that are valuable for manufacturing processes requires that they are deliberately enriched with the specific expertise of the industry [6]. This is crucial for achieving benefits through AI. Industrial AI achieves this by combining data science, AI and industrial expertise. As part of a systematic industrial AI workflow, machine learning algorithms are therefore developed, implemented and deployed that are tailored to the specific industrial applications.   <\/p>\n<\/div>\n<\/div>\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n<p>[1] L\u00e1zaro, O. et al: &#8220;Model-Based Engineering and Semantic Interoperability for Trusted Digital Twins Big Data Connection Across the Product Lifecycle&#8221;. In: Curry, E., Auer, S., Berre, A.J., Metzger, A., Perez, M.S., Zillner, S. (eds) Technologies and Applications for Big Data Value. Springer, 2022.<\/p>\n\n<p>[2] Deloitte: &#8220;AI Enablement on the Way to Smart Manufacturing&#8221;, Deloitte Survey on AI Adoption in Manufacturing, 2020.<\/p>\n\n<p>[3] Ducic, N. et al: &#8220;Casting Process Improvement by the Application of Artificial Intelligence&#8221;, In Appl. Sci. 2022, 12, 3264. <a href=\"https:\/\/doi.org\/10.3390\/app12073264\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.3390\/app12073264.<\/a><\/p>\n\n<p>[4] European Commission: Horizon Europe &#8211; Work Program 2023-2024, Digital, Industry and Space. European Commission Decision C(2023) 2178 of 31 March 2023. <\/p>\n\n<p>[5] High-Level Expert Group on Artificial Intelligence (AI HLEG): The Assessment List for Trustworthy Artificial Intelligence (ALTAI), July 2020, <a href=\"https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai\" target=\"_blank\" rel=\"noopener\">Ethics guidelines for trustworthy AI | Shaping Europe&#8217;s digital future (europa.eu)<\/a>.<\/p>\n\n<p>[6] AspenTech: &#8220;The future starts with Industrial AI&#8221;, MIT Technology Review, <a href=\"https:\/\/www.technologyreview.com\/2021\/06\/28\/1026960\/the-future-starts-with-industrial-ai\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.technologyreview.com\/2021\/06\/28\/1026960\/the-future-starts-with-industrial-ai\/,<\/a> 2021.<\/p>\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 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name=\"_wpnonce\" value=\"2762796293\" \/><input type=\"hidden\" name=\"_wp_http_referer\" value=\"\/en\/wp-json\/wp\/v2\/publication\/31551\" \/><\/span><input class=\"wpcf7-form-control wpcf7-submit has-spinner btn\" type=\"submit\" value=\"Senden\" \/>\n<\/p><div class=\"wpcf7-response-output\" aria-hidden=\"true\"><\/div>\n<\/form>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<h2 class=\"wp-block-heading\">Authors<\/h2>\n\n\n<div class=\"contact-person\">\n      <picture>\n      \n      \n      \n      \n      <img decoding=\"async\" data-aos=\"fade-zoom-in\"\n           data-aos-offset=\"0\" class=\"w-full\" width=\"212\" height=\"293\"\n           src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/06\/rholom1-removebg-preview.png\"\n           alt=\"\">\n    <\/picture>\n    \n\n<h5 class=\"wp-block-heading\">Dr. Roxana-Maria Holom, MSc<\/h5>\n\n\n\n<p>Data Science Project Manager &amp; Researcher<\/p>\n\n  <\/div>\n\n\n<div class=\"contact-person\">\n      <picture>\n      \n      \n      \n      \n      <img decoding=\"async\" data-aos=\"fade-zoom-in\"\n           data-aos-offset=\"0\" class=\"w-full\" width=\"212\" height=\"293\"\n           src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/eocansey1.jpg\"\n           alt=\"\">\n    <\/picture>\n    \n\n<h5 class=\"wp-block-heading\">Dr. Evans Doe Ocansey<\/h5>\n\n\n\n<p>Data Scientist<\/p>\n\n  <\/div>\n<\/div>\n<\/div>\n\n<h2 class=\"wp-block-heading\">Read more<\/h2>\n<div class=\"posts-slider-block\" data-aos=\"fade-up\" data-aos-offset=\"0\" data-aos-anchor-placement=\"top-bottom\">\n        <section class=\"splide posts-slider\" aria-label=\"Gallery Slides\">\n            <div class=\"splide__arrows\">\n                <button class=\"splide__arrow splide__arrow--prev\">\n                    <span class=\"sr-only\">Previous<\/span>\n                    <img decoding=\"async\" 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blog-post-teaser mb-1 lg:mb-3\">\n                                <div class=\"blog-image\">\n                                                                                                                                <picture>\n                                                                                        <img decoding=\"async\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/06\/iStock-1170740969-360x214.jpg\"\n                                                 alt=\"Can data science lead industrial companies out of the crisis?\">\n                                        <\/picture>\n                                                                    <\/div>\n                                <div class=\"blog-content px-2 py-3 xl:px-4 xl:py-5\">\n                                    <h3>Can data science lead industrial companies out of the crisis?<\/h3>\n                                    <div class=\"blog-post-excerpt mt-2\">\n                                        How it is possible to minimize costs, respond flexibly to fluctuations in demand, and avoid production downtime due to disruption.\n                                    <\/div>\n                                    <span class=\"inline-block mt-2 more\">mehr erfahren <span class=\"ml-1 icon-more\"><\/span><\/span>\n\n                                <\/div>\n                            <\/a>\n                                                    <a href=\"https:\/\/www.risc-software.at\/en\/technicalarticles\/technical-article-making-better-decisions-thanks-to-prescriptive-analytics\/\" class=\"splide__slide blog-post-teaser mb-1 lg:mb-3\">\n                                <div class=\"blog-image\">\n                                                                                                                                <picture>\n                                                                                        <img decoding=\"async\" src=\"https:\/\/www.risc-software.at\/app\/uploads\/2023\/06\/iStock-872019580-2-360x214.jpg\"\n                                                 alt=\"Making better decisions thanks to Prescriptive Analytics\">\n                                        <\/picture>\n                                                                    <\/div>\n                                <div class=\"blog-content px-2 py-3 xl:px-4 xl:py-5\">\n                                    <h3>Making better decisions thanks to Prescriptive Analytics<\/h3>\n                                    <div class=\"blog-post-excerpt mt-2\">\n                                        By combining forecasting models with optimization models, future decision options are simulated from company data and the best alternative course of action is selected.\n                                    <\/div>\n                                    <span class=\"inline-block mt-2 more\">mehr erfahren <span class=\"ml-1 icon-more\"><\/span><\/span>\n\n                                <\/div>\n                            <\/a>\n                                            <\/div>\n                <\/div>\n            <\/div>\n        <\/section>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p>The industrial sector, like other areas, is currently going through a phase of digital transformation. This means that manufacturing companies are involved in various digitalization activities [1]. Within this context, industrial data and the way in which it is processed, visualized and used play a key role.<\/p>\n","protected":false},"featured_media":6314,"template":"","publication-category":[50,74],"class_list":["post-31551","publication","type-publication","status-publish","has-post-thumbnail","hentry","publication-category-data-science-and-a-i","publication-category-industry-4-0"],"acf":[],"portrait_thumb_url":"https:\/\/www.risc-software.at\/app\/uploads\/2023\/09\/Fotolia_64178357_XL-c-industrieblick-Fotolia.com_-360x214.jpg","_links":{"self":[{"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/publication\/31551","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/types\/publication"}],"version-history":[{"count":2,"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/publication\/31551\/revisions"}],"predecessor-version":[{"id":36134,"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/publication\/31551\/revisions\/36134"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/media\/6314"}],"wp:attachment":[{"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/media?parent=31551"}],"wp:term":[{"taxonomy":"publication-category","embeddable":true,"href":"https:\/\/www.risc-software.at\/en\/wp-json\/wp\/v2\/publication-category?post=31551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}