{"id":7450,"date":"2026-10-08T08:24:45","date_gmt":"2026-10-08T06:24:45","guid":{"rendered":"https:\/\/it.ai-matters.eu\/preserving-industrial-know-how-how-meti-digitalizes-senior-expertise-to-empower-next-gen-operators\/"},"modified":"2026-10-08T08:32:39","modified_gmt":"2026-10-08T06:32:39","slug":"preserving-industrial-know-how-how-meti-digitalizes-senior-expertise-to-empower-next-gen-operators","status":"publish","type":"post","link":"https:\/\/it.ai-matters.eu\/en\/preserving-industrial-know-how-how-meti-digitalizes-senior-expertise-to-empower-next-gen-operators\/","title":{"rendered":"Preserving Industrial Know-How: how Meti digitalizes senior expertise to empower next-gen operators"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"7450\" class=\"elementor elementor-7450 elementor-7443\" data-elementor-post-type=\"post\">\n\t\t\t\t<div data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-element elementor-element-1c1a0af5 e-flex e-con-boxed e-con e-parent\" data-id=\"1c1a0af5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-289821f5 elementor-widget elementor-widget-text-editor\" data-id=\"289821f5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<div>\n<div id=\"pb-star-rating-91b9e54f66f047c980800e83442bee87\" class=\"wp-block-ud-blocks-star-rating\" style=\"--udpb-sr-align: flex-start; --udpb-sr-icon-color: #ffb900; --udpb-sr-icon-size: 18px; --udpb-sr-icon-sizetablet: 17px; --udpb-sr-icon-sizemobile: 16px; --udpb-sr-title-color: #000; --udpb-sr-title-size: 18px; --udpb-sr-title-size-tablet: 17px; --udpb-sr-title-size-mobile: 16px;\">\n<div class=\"pb-star-rating-wrapper\">\n<div class=\"pb-star-rating--title\">\n<div class=\"text-content \">\n<div class=\"text-content \">\n<h2 class=\"title\"><span style=\"color: #000080;\">Introduction<\/span><\/h2>\n<div class=\"content-container\">\n<p class=\"content\"><span style=\"color: #000080;\">Nell\u2019industria manifatturiera la conoscenza di processo \u00e8 un vantaggio competitivo decisivo, ma spesso resta nella testa di pochi tecnici esperti che l\u2019hanno costruita in decenni di lavoro sul campo. Con il supporto di MADE Competence Center, AISent ha sperimentato Meti, un assistente basato sull\u2019intelligenza artificiale che trasforma l\u2019esperienza dei senior in modelli causali e la incrocia con i dati delle macchine in tempo reale. Cos\u00ec guida gli operatori junior nella risoluzione dei fermi produttivi. Al progetto hanno collaborato Siemens e Fasthink.   <\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"text-content \">\n<h2 class=\"title\"><span style=\"color: #000080;\">The Problem<\/span><\/h2>\n<div class=\"content-container\">\n<p class=\"content\"><span style=\"color: #000080;\">Rising staff turnover and a shortage of trained candidates leave companies exposed. If a critical fault happens when the only technician who can fix it isn\u2019t available, production can stop. There are three main issues:<br\/><br\/> <\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Operational vulnerability: without expert guidance, junior operators struggle to find the root cause of a stoppage and fall back on trial and error, which makes recovery take longer<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Loss of know-how: every retirement or staff change risks losing strategic technical knowledge that was never written down.<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Data and expertise kept apart: machine data from PLCs and technicians\u2019 experience are still two separate worlds. Only by bringing them together can companies find out why a malfunction happened. <\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"text-content \">\n<h2 class=\"title\"><span style=\"color: #000080;\">The solution<\/span><\/h2>\n<div class=\"content-container\">\n<p class=\"content\"><span style=\"color: #000080;\">AISent connected Meti to the factory ecosystem through a software connector developed with Siemens and Fasthink. With the connector, Meti can query the machine\u2019s status directly and compare real-time signals with the digitized knowledge models.<br\/>Meti is built on causal models, a form of AI that goes beyond statistics: it reproduces the logic an expert follows when facing a problem, so the diagnosis is guided and transparent. In the test environment, Meti received data from the machine control system through Siemens Industrial Edge and the Fasthink connector. The test checked whether the assistant could suggest a fix right away, based on the current state of the assets. <\/span><br><br><span style=\"color: #000080;\">Meti is built on causal models, a form of AI that goes beyond statistics: it reproduces the logic an expert follows when facing a problem, so the diagnosis is guided and transparent. In the test environment, Meti received data from the machine control system through Siemens Industrial Edge and the Fasthink connector. The test checked whether the assistant could suggest a fix right away, based on the current state of the assets.  <\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"text-content \">\n<h2 class=\"title\"><span style=\"color: #000080;\">Tecnologies<\/span><\/h2>\n<div class=\"content-container\">\n<p class=\"content\"><span style=\"color: #000080;\">\u2013 Meti (AISent): software that combines AI and statistics to map experiential knowledge through causal graphs.<\/span><br><span style=\"color: #000080;\">\u2013 Siemens Industrial Edge: platform that collects data from the machine control system.<\/span><br><span style=\"color: #000080;\">\u2013 Fasthink connector: software module that links Meti to real-time machine data.<\/span><br><span style=\"color: #000080;\">\u2013 Causal models: AI that reproduces an expert\u2019s diagnostic reasoning in a transparent, interpretable way.<\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"text-content \">\n<h2 class=\"title\"><span style=\"color: #000080;\">Expected impacts<\/span><\/h2>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Significantly reduce machine downtime through guided, faster diagnosis.<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Capture senior technicians\u2019 expertise as a shared digital asset that is always available.<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Help junior operators become independent sooner, even when production varies a lot.<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<div class=\"check\"> <\/div>\n<p class=\"content\"><span style=\"color: #000080;\">Combine machine data with human experience, so the assistant becomes an active advisor that responds immediately to error signals rather than a passive archive.<\/span><\/p>\n<\/div>\n<div class=\"content-container\">\n<p class=\"content\"><span style=\"color: #000080;\">Next steps: roll Meti out across multiple sites, add self-learning from newly resolved maintenance events, and add connectors for more industrial protocols and older (legacy) PLC systems.<\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"text-content card-bg\">\n<h2 class=\"title\"><span style=\"color: #000080;\">Benefits for the Company<\/span><\/h2>\n<div class=\"content-container\">\n<p class=\"content\">1. Up to 70% less machine downtime thanks to diagnosis based on causal models.<br\/><br\/>2. Continuous on-the-job training: junior operators learn while solving real problems, with a \u201cvirtual expert\u201d always at their side.<br\/><br\/>3. More effective diagnosis: raw technical data and human intuition work together, even on the most complex cases.<br\/><br\/>4. Fast deployment: reaching full production takes about 3 months on average.<br\/><br\/>5. Less reliance on key individuals: the company keeps its know-how even when staff change.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<figure class=\"wp-block-image size-large\"><\/figure>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Find out how Geckosoft has validated Everplan in a real-world operational setting, using artificial intelligence to optimise task planning, resource allocation and travel across the region.<\/p>\n","protected":false},"author":13,"featured_media":7449,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[258],"class_list":["post-7450","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-non-categorizzato","tag-user_story"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - 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