In traditional mechanical and plant engineering, the competitive advantage often lies in the details: energy, availability, cycle times, waste, water, air, solvents, media consumption. Many suppliers deliver cutting-edge technology. What is often missing is the second piece: a reliable picture of what really happens in the business<\/b><\/span> \u2013 and how to improve it.<\/p>\n <\/p>\n D\u00fcrr describes an “energy assessment” that analyses a plant’s consumers in order to provide the data necessary for optimisation. The goal is clear: to identify potential savings, prioritise measures and justify investments.<\/p>\n This is a typical “knowledge problem” in plant engineering:<\/p>\n The customer suspects that energy is expensive.<\/p>\n<\/li>\n But they don’t know where<\/b><\/span> exactly it is being lost.<\/p>\n<\/li>\n And which measures will have the greatest effect.<\/p>\n<\/li>\n<\/ul>\n A structured assessment approach creates precisely this transparency. It turns gut feelings into a basis for decision-making.<\/p>\n <\/p>\n D\u00fcrr also reports that EcoEMOS (shop floor IT\/control\/data platform) has already been implemented in over 60 automotive plants in 20 countries<\/b><\/span> (as of 2012). <\/p>\n This is important because it shows that plant engineering is no longer just about “mechanics + control cabinets”. It is increasingly about data integration<\/b><\/span>.<\/p>\n And this is precisely where classic errors occur:<\/p>\n Data is stored in silos (PLC, MES, energy monitoring, Excel).<\/p>\n<\/li>\n Decisions are slow because no one has the big picture.<\/p>\n<\/li>\n Optimisation remains piecemeal.<\/p>\n<\/li>\n<\/ul>\n <\/p>\n AI is useful in assessments and operational evaluations because it:<\/p>\n structures large amounts of data,<\/p>\n<\/li>\n finds anomalies,<\/p>\n<\/li>\n makes measures sortable by effect,<\/p>\n<\/li>\n compiles texts\/reports more quickly.<\/p>\n<\/li>\n<\/ul>\n <\/p>\n However, the decisive factor is human support:<\/p>\n Which hypotheses are plausible?<\/p>\n<\/li>\n Which measurement is reliable?<\/p>\n<\/li>\n Which measure is realistic in operation?<\/p>\n<\/li>\n What risks (safety, quality, standards) are associated with it?<\/p>\n<\/li>\n<\/ul>\n <\/p>\n This is a proven approach for SMEs in plant engineering (or operators):<\/p>\n Assessment<\/b><\/span>: Transparency regarding main consumers and bottlenecks. <\/p>\n<\/li>\n Pilot<\/b><\/span>: 1 line, 1 plant, 1 clear target value (e.g. kWh\/part, water\/batch).<\/p>\n<\/li>\n Roll-out<\/b><\/span>: Standards, templates, repeatable reports.<\/p>\n<\/li>\n<\/ol>\n <\/p>\n This makes “knowledge” scalable. And that is precisely the point you want to make with Aiquiro Research.<\/p>\n <\/p>\n Those who understand business sell results \u2013 not just technology.<\/p>\n<\/li>\n Assessments are often the fastest way to make reliable decisions. <\/p>\n<\/li>\n AI speeds up analysis. People ensure relevance and feasibility.<\/p>\n<\/li>\n “Measure first, then invest” saves money and discussions.<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":" Those who understand their customers’ operations sell more than just a system. In traditional mechanical and plant engineering, the competitive advantage often lies in the details: energy, availability, cycle times, waste, water, air, solvents, media consumption. Many suppliers deliver cutting-edge technology. What is often missing is the second piece: a reliable picture of what really […]<\/p>\n","protected":false},"author":1,"featured_media":2565,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2566","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-nicht-kategorisiert"],"_links":{"self":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2566","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/comments?post=2566"}],"version-history":[{"count":1,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2566\/revisions"}],"predecessor-version":[{"id":4220,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/posts\/2566\/revisions\/4220"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/media\/2565"}],"wp:attachment":[{"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/media?parent=2566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/categories?post=2566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiquiro-research.de\/en\/wp-json\/wp\/v2\/tags?post=2566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}D\u00fcrr: Energy assessment as a data-based starting point<\/b><\/h3>\n
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EcoEMOS: Why “shop floor IT” is a competitive advantage in plant engineering<\/b><\/h3>\n
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What AI can do here \u2013 in concrete terms and without hype<\/b><\/h3>\n
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The SME transfer: “Assessment \u2192 Pilot \u2192 Roll-out”<\/b><\/h3>\n
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What SMEs can learn from this<\/b><\/h3>\n
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