{"id":1686,"date":"2025-04-22T09:19:12","date_gmt":"2025-04-22T09:19:12","guid":{"rendered":"http:\/\/causal-discovery.blog\/?page_id=1686"},"modified":"2025-07-24T10:22:00","modified_gmt":"2025-07-24T10:22:00","slug":"anwenderberichte","status":"publish","type":"page","link":"https:\/\/xplain-data.de\/de\/anwenderberichte\/","title":{"rendered":"Anwenderberichte"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row gap=&#8220;35&#8243;][vc_column width=&#8220;1\/2&#8243;]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">.shortcode-single-image-wrap.shortcode-single-image-6f215ae4df99f58aa76bdda2153d5bd6.enable-bg-rollover .rollover i,\n.shortcode-single-image-wrap.shortcode-single-image-6f215ae4df99f58aa76bdda2153d5bd6.enable-bg-rollover .rollover-video i {\n  background: -webkit-linear-gradient();\n  background: linear-gradient();\n}\n.shortcode-single-image-wrap.shortcode-single-image-6f215ae4df99f58aa76bdda2153d5bd6 .rollover-icon {\n  font-size: 32px;\n  color: #ffffff;\n  min-width: 44px;\n  min-height: 44px;\n  line-height: 44px;\n  border-radius: 100px;\n  border-style: solid;\n  border-width: 0px;\n}\n.dt-icon-bg-on.shortcode-single-image-wrap.shortcode-single-image-6f215ae4df99f58aa76bdda2153d5bd6 .rollover-icon {\n  background: rgba(255,255,255,0.3);\n  box-shadow: none;\n}<\/style><div class=\"shortcode-single-image-wrap shortcode-single-image-6f215ae4df99f58aa76bdda2153d5bd6 alignnone  enable-bg-rollover dt-icon-bg-off\" style=\"margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;\"><div class=\"shortcode-single-image\"><div class=\"fancy-media-wrap  layzr-bg\" style=\"\"><img fetchpriority=\"high\" decoding=\"async\" class=\"preload-me lazy-load aspect\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#39;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#39;%20viewBox%3D&#39;0%200%20465%20650&#39;%2F%3E\" data-src=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Coverbild_SW_scaled-465x650.jpg\" data-srcset=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Coverbild_SW_scaled-465x650.jpg 465w, https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Coverbild_SW_scaled.jpg 535w\" loading=\"eager\" sizes=\"(max-width: 465px) 100vw, 465px\" width=\"465\" height=\"650\"  data-dt-location=\"https:\/\/xplain-data.de\/de\/screenshot-2\/\" style=\"--ratio: 465 \/ 650;box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6); -webkit-box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6);\" alt=\"SW Case Study Cover DE\" \/><\/div><\/div><\/div>[vc_empty_space height=&#8220;45px&#8220;][vc_column_text css=&#8220;&#8220;]<\/p>\n<h3><a href=\"https:\/\/sw-machines.com\/\" target=\"_blank\" rel=\"noopener\">Anwenderbericht: Optimierte Prozesse in der diskreten Produktion bei Schw\u00e4bische Werkzeugmaschinen<\/a><\/h3>\n<p>Lesen Sie, wie in einem Projekt mit der Schw\u00e4bischen Werkzeugmaschinen GmbH (SW) im Bereich der diskreten Fertigung beim Automobilzulieferer Schabm\u00fcller durch den Einsatz von Xplain Data Algorithmen Ausschuss und Nacharbeit um fast 85 % reduziert werden konnte.<\/p>\n<p>Durch den Einsatz der <a href=\"https:\/\/xplain-data.de\/de\/causal-discoverer\/\">Causal Discovery<\/a> Verfahren wurden bislang unbekannte Fehlerursachen aufgesp\u00fcrt.[\/vc_column_text]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">#default-btn-5decbb7cdb3365837d0eb430049724db.ico-right-side > i {\n  margin-right: 0px;\n  margin-left: 8px;\n}\n#default-btn-5decbb7cdb3365837d0eb430049724db > i {\n  margin-right: 8px;\n}<\/style><a href=\"https:\/\/vimeo.com\/manage\/videos\/1052933376\" class=\"default-btn-shortcode dt-btn dt-btn-l fadeIn animate-element animation-builder link-hover-off btn-inline-left \" target=\"_blank\" id=\"default-btn-5decbb7cdb3365837d0eb430049724db\" title=\"PDF Herunterladen\" rel=\"noopener\"><span>Video (engl.)<\/span><\/a><style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">#default-btn-c05aee07d0a47718f3635a6bb3037f0f.ico-right-side > i {\n  margin-right: 0px;\n  margin-left: 8px;\n}\n#default-btn-c05aee07d0a47718f3635a6bb3037f0f > i {\n  margin-right: 8px;\n}<\/style><a href=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/SW_CaseStudy_Final_DE_220722.pdf\" class=\"default-btn-shortcode dt-btn dt-btn-l fadeIn animate-element animation-builder link-hover-off btn-inline-left \" target=\"_blank\" id=\"default-btn-c05aee07d0a47718f3635a6bb3037f0f\" title=\"PDF Herunterladen\" rel=\"noopener\"><span>PDF Herunterladen<\/span><\/a>[\/vc_column][vc_column width=&#8220;1\/2&#8243;]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">.shortcode-single-image-wrap.shortcode-single-image-e78d83b3011607a4523d8cb317562849.enable-bg-rollover .rollover i,\n.shortcode-single-image-wrap.shortcode-single-image-e78d83b3011607a4523d8cb317562849.enable-bg-rollover .rollover-video i {\n  background: -webkit-linear-gradient();\n  background: linear-gradient();\n}\n.shortcode-single-image-wrap.shortcode-single-image-e78d83b3011607a4523d8cb317562849 .rollover-icon {\n  font-size: 32px;\n  color: #ffffff;\n  min-width: 44px;\n  min-height: 44px;\n  line-height: 44px;\n  border-radius: 100px;\n  border-style: solid;\n  border-width: 0px;\n}\n.dt-icon-bg-on.shortcode-single-image-wrap.shortcode-single-image-e78d83b3011607a4523d8cb317562849 .rollover-icon {\n  background: rgba(255,255,255,0.3);\n  box-shadow: none;\n}<\/style><div class=\"shortcode-single-image-wrap shortcode-single-image-e78d83b3011607a4523d8cb317562849 alignnone  enable-bg-rollover dt-icon-bg-off\" style=\"margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;\"><div class=\"shortcode-single-image\"><div class=\"fancy-media-wrap  layzr-bg\" style=\"\"><img decoding=\"async\" class=\"preload-me lazy-load aspect\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#39;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#39;%20viewBox%3D&#39;0%200%20465%20650&#39;%2F%3E\" data-src=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Cover_QZ-465x650.jpg\" data-srcset=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Cover_QZ-465x650.jpg 465w, https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/07\/Cover_QZ.jpg 535w\" loading=\"eager\" sizes=\"(max-width: 465px) 100vw, 465px\" width=\"465\" height=\"650\"  data-dt-location=\"https:\/\/xplain-data.de\/de\/screenshot\/\" style=\"--ratio: 465 \/ 650;box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6); -webkit-box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6);\" alt=\"KI Ursachenanalyse\" \/><\/div><\/div><\/div>[vc_empty_space height=&#8220;45px&#8220;][vc_column_text css=&#8220;&#8220;]<\/p>\n<h3><a href=\"http:\/\/www.trumpf.com\">Anwenderbericht: KI-gest\u00fctzte Ursachenanalyse bei Trumpf<\/a><\/h3>\n<p>\u201cKI unterstu\u0308tzt uns bei der Identifikation von Wirkzusammenha\u0308ngen in komplexen Daten\u201c, sagt Dr.-Ing. Mathias Kammu\u0308ller, Chief Digital Officer und <a href=\"http:\/\/trumpf.com\">Trumpf<\/a>-Vorstandsmitglied nach einem erfolgreichen Projekt mit Xplain Data. Ziel des Projektes war es, basierend auf Daten aus der Produktion und dem Betrieb einer Anlage Faktoren zu identifizieren, die urs\u00e4chlich Maschinenausf\u00e4lle bedingen (root cause analysis). Im n\u00e4chsten Schritt wird ein st\u00e4ndiges Monitoring durch die Algorithmen erfolgen, um sich neu entwickelnde Fehlerursachen fr\u00fchzeitig zu detektieren und entsprechende Abteilungen zu benachrichtigen.<\/p>\n<p>Ein Projektbericht zur Co-Innovation mit Trumpf erschien im August 2022 im Qualit\u00e4ts-Management-Magazin <a href=\"https:\/\/www.qz-online.de\/a\/fachartikel\/ki-hilft-bei-der-ursachenanalyse-2836691\">QZ-online<\/a>.[\/vc_column_text]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">#default-btn-b5c9458f8c0efee653310cfa33f124ee.ico-right-side > i {\n  margin-right: 0px;\n  margin-left: 8px;\n}\n#default-btn-b5c9458f8c0efee653310cfa33f124ee > i {\n  margin-right: 8px;\n}<\/style><a href=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2022\/08\/Trumpf_CaseStudy_QZ_DE_0822.pdf\" class=\"default-btn-shortcode dt-btn dt-btn-l fadeIn animate-element animation-builder link-hover-off btn-inline-left \" target=\"_blank\" id=\"default-btn-b5c9458f8c0efee653310cfa33f124ee\" title=\"PDF Herunterladen\" rel=\"noopener\"><span>PDF Herunterladen<\/span><\/a>[\/vc_column][\/vc_row][vc_row gap=&#8220;35&#8243;][vc_column width=&#8220;1\/2&#8243;]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">.shortcode-single-image-wrap.shortcode-single-image-e1ab316f8d09c45c8c2fa1137d62adb0.enable-bg-rollover .rollover i,\n.shortcode-single-image-wrap.shortcode-single-image-e1ab316f8d09c45c8c2fa1137d62adb0.enable-bg-rollover .rollover-video i {\n  background: -webkit-linear-gradient();\n  background: linear-gradient();\n}\n.shortcode-single-image-wrap.shortcode-single-image-e1ab316f8d09c45c8c2fa1137d62adb0 .rollover-icon {\n  font-size: 32px;\n  color: #ffffff;\n  min-width: 44px;\n  min-height: 44px;\n  line-height: 44px;\n  border-radius: 100px;\n  border-style: solid;\n  border-width: 0px;\n}\n.dt-icon-bg-on.shortcode-single-image-wrap.shortcode-single-image-e1ab316f8d09c45c8c2fa1137d62adb0 .rollover-icon {\n  background: rgba(255,255,255,0.3);\n  box-shadow: none;\n}<\/style><div class=\"shortcode-single-image-wrap shortcode-single-image-e1ab316f8d09c45c8c2fa1137d62adb0 alignnone  enable-bg-rollover dt-icon-bg-off\" style=\"margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px;\"><div class=\"shortcode-single-image\"><div class=\"fancy-media-wrap  layzr-bg\" style=\"\"><img decoding=\"async\" class=\"preload-me lazy-load aspect\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D&#39;http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg&#39;%20viewBox%3D&#39;0%200%20459%20650&#39;%2F%3E\" data-src=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2025\/04\/Frontpage_SiemensAnwenderbericht_dt-pdf-459x650.jpg\" data-srcset=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2025\/04\/Frontpage_SiemensAnwenderbericht_dt-pdf-459x650.jpg 459w, https:\/\/xplain-data.de\/wp-content\/uploads\/2025\/04\/Frontpage_SiemensAnwenderbericht_dt-pdf-918x1300.jpg 918w\" loading=\"eager\" sizes=\"(max-width: 459px) 100vw, 459px\" width=\"459\" height=\"650\"  data-dt-location=\"https:\/\/xplain-data.de\/de\/anwenderberichte\/frontpage_siemensanwenderbericht_dt\/\" style=\"--ratio: 459 \/ 650;box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6); -webkit-box-shadow:5px 5px 5px 5px rgba(0,0,0,0.6);\" alt=\"\" \/><\/div><\/div><\/div>[vc_empty_space height=&#8220;45px&#8220;][vc_column_text css=&#8220;&#8220;]<\/p>\n<h3><a href=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2025\/02\/Siemens-Erlangen_Anwenderbericht_Final_DE.pdf\" target=\"_blank\" rel=\"noopener\">Anwenderbericht: Wie Siemens Digital Industries dank Causal AI die 5-Sigma-Schwelle \u00fcberwindet<\/a><\/h3>\n<p>Erfahren Sie, wie Siemens Motion Control in Erlangen die Qualit\u00e4tssicherung in der Leiterplattenbest\u00fcckung revolutioniert hat. Durch den Einsatz von Causal AI und der patentierten ObjectAnalytics-Technologie von Xplain Data konnte das Werk die anspruchsvolle 5-Sigma-Grenze \u00fcberwinden und Six Sigma Niveau erreichen. Entdecken Sie, wie die patentierte, objektzentrierte Datenhaltung und innovative Causal Discovery Algorithmen eine neue \u00c4ra der Fertigungsexzellenz erm\u00f6glicht haben.<\/p>\n<p>Laden Sie den vollst\u00e4ndige Bericht herunter und informieren Sie sich, wie KI-gest\u00fctzte Erkenntnisse die Elektronikfertigung nachhaltig ver\u00e4ndern k\u00f6nnen![\/vc_column_text]<style type=\"text\/css\" data-type=\"the7_shortcodes-inline-css\">#default-btn-66e943717f39244bdc68a5b17d107146.ico-right-side > i {\n  margin-right: 0px;\n  margin-left: 8px;\n}\n#default-btn-66e943717f39244bdc68a5b17d107146 > i {\n  margin-right: 8px;\n}<\/style><a href=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2025\/02\/Siemens-Erlangen_Anwenderbericht_Final_DE.pdf\" class=\"default-btn-shortcode dt-btn dt-btn-l fadeIn animate-element animation-builder link-hover-off btn-inline-left \" target=\"_blank\" id=\"default-btn-66e943717f39244bdc68a5b17d107146\" title=\"PDF Herunterladen\" rel=\"noopener\"><span>PDF Herunterladen<\/span><\/a>[\/vc_column][vc_column width=&#8220;1\/2&#8243;][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row gap=&#8220;35&#8243;][vc_column width=&#8220;1\/2&#8243;][vc_empty_space height=&#8220;45px&#8220;][vc_column_text css=&#8220;&#8220;] Anwenderbericht: Optimierte Prozesse in der diskreten Produktion bei Schw\u00e4bische Werkzeugmaschinen Lesen Sie, wie in einem Projekt mit der Schw\u00e4bischen Werkzeugmaschinen GmbH (SW) im Bereich der diskreten Fertigung beim Automobilzulieferer Schabm\u00fcller durch den Einsatz von Xplain Data Algorithmen Ausschuss und Nacharbeit um fast 85 % reduziert werden konnte. Durch den Einsatz&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-1686","page","type-page","status-publish","hentry","description-off"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Anwenderberichte I Xplain Data GmbH<\/title>\n<meta name=\"description\" content=\"Real World Daten optimal nutzen: Anwender berichten, wie durch Causal AI aus Daten wirksame Ma\u00dfnahmen entstehen.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/xplain-data.de\/de\/anwenderberichte\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Anwenderberichte I Xplain Data GmbH\" \/>\n<meta property=\"og:description\" content=\"Real World Daten optimal nutzen: Anwender berichten, wie durch Causal AI aus Daten wirksame Ma\u00dfnahmen entstehen.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/xplain-data.de\/de\/anwenderberichte\/\" \/>\n<meta property=\"og:site_name\" content=\"Xplain Data\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-24T10:22:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/xplain-data.de\/wp-content\/uploads\/2023\/01\/social-image.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"720\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data1\" content=\"3\u00a0Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/anwenderberichte\\\/\",\"url\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/anwenderberichte\\\/\",\"name\":\"Anwenderberichte I Xplain Data GmbH\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/#website\"},\"datePublished\":\"2025-04-22T09:19:12+00:00\",\"dateModified\":\"2025-07-24T10:22:00+00:00\",\"description\":\"Real World Daten optimal nutzen: Anwender berichten, wie durch Causal AI aus Daten wirksame Ma\u00dfnahmen entstehen.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/anwenderberichte\\\/#breadcrumb\"},\"inLanguage\":\"de\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/xplain-data.de\\\/de\\\/anwenderberichte\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/anwenderberichte\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Anwenderberichte\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/#website\",\"url\":\"https:\\\/\\\/xplain-data.de\\\/de\\\/\",\"name\":\"Xplain Data\",\"description\":\"Uncover cause &amp; 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