[{"data":1,"prerenderedAt":110},["ShallowReactive",2],{"yQCEEZv704-ABeiBv6ZIUzDIpDTHh1gtew20fmXMHLM":3,"_apollo:default":108,"_apollo:identified":109},{"seo":4,"posts":15},{"social":5,"openGraph":11,"__typename":14},{"twitter":6,"__typename":10},{"cardType":7,"username":8,"__typename":9},"summary_large_image","dassault3ds","SEOSocialTwitter","SEOSocial",{"defaultImage":12,"__typename":13},null,"SEOOpenGraph","SEOConfig",{"nodes":16,"__typename":107},[17],{"id":18,"slug":19,"title":20,"uri":21,"excerpt":22,"locale":23,"featuredImage":26,"tableOfContents":34,"content":41,"date":42,"translations":43,"author":44,"tags":57,"globalTags":71,"brands":74,"keywords":85,"seo":95,"__typename":106},"cG9zdDozMTYyMjI=","from-oee-dashboards-to-operational-intelligence-optimizing-manufacturing-metrics","From OEE Dashboards to Operational Intelligence: Optimizing Manufacturing Metrics","\u002Fbrands\u002Fdelmia\u002Ffrom-oee-dashboards-to-operational-intelligence-optimizing-manufacturing-metrics","\u003Cp>While OEE remains manufacturing’s most debated metric, its failure stems from bad deployment rather than the calculation itself. By connecting standalone scores to real-time context and action, modern MOM platforms transform static OEE dashboards into dynamic operational intelligence that drives true factory floor improvement.\u003C\u002Fp>\n",{"locale":24,"__typename":25},"en_US","Locale",{"node":27,"__typename":33},{"large":28,"__typename":29,"medium_large":28,"thumbnail":30,"srcSet":31,"sizes":32},"https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Foee-production-blog-delmia-580x366-1.png","MediaItem","https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Foee-production-blog-delmia-580x366-1-150x150.png","https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Foee-production-blog-delmia-580x366-1-300x189.png 300w, https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Foee-production-blog-delmia-580x366-1.png 580w","(max-width: 300px) 100vw, 300px","NodeWithFeaturedImageToMediaItemConnectionEdge",[35,36,37,38,39,40],"Why OEE Dashboards Fail: Flaws and Common Critiques of OEE|why-oee-dashboards-fail-flaws-and-common-critiques-of-oee-0","What Is Operational Intelligence? Moving Beyond Static OEE Dashboards|what-is-operational-intelligence-moving-beyond-static-oee-dashboards-1","Five Tests to Evaluate Your OEE Dashboards vs. Operational Intelligence|five-tests-to-evaluate-your-oee-dashboards-vs-operational-intelligence-2","Leveraging MOM Software for Actionable Real-Time OEE Metrics|leveraging-mom-software-for-actionable-real-time-oee-metrics-3","Eurowipes Case Study: Improving On-Time Delivery with Real-Time Scheduling|eurowipes-case-study-improving-on-time-delivery-with-real-time-scheduling-4","Conclusion: Transforming OEE into Real-Time Operational Intelligence|conclusion-transforming-oee-into-real-time-operational-intelligence-5","\n\u003Cp>A plant manager spends a year raising Overall Equipment Effectiveness (OEE) from 68 to 74 and can’t point to a single additional good part. The ideal cycle time got renegotiated after the line proved it couldn’t hit the old one. Micro-stops under two minutes quietly fell out of the count. Two shifts learned which downtime codes kept their numbers clean. The dashboard turned green, the output stayed flat, and everyone in the Monday meeting knows it. The metric didn’t fail. The way it was used did.\u003C\u002Fp>\n\n\n\n\u003Cp>OEE is the most argued-about number in manufacturing and the people arguing aren’t wrong about the abuses. Even the analysts who defend the metric lead with the caveats.\u003C\u002Fp>\n\n\n\n\u003Cblockquote class=\"is-layout-flow wp-block-quote-is-layout-flow\">\n\u003Cp>It’s hyped. It’s abused. There’s lots of water to pour into the wine,” Simon Jacobson, vice president with the supply chain practice at Gartner, told the Institute for Supply Management’s \u003Ca href=\"https:\u002F\u002Fwww.ismworld.org\u002Fsupply-management-news-and-reports\u002Fnews-publications\u002Finside-supply-management-magazine\u002Fblog\u002F2020-12\u002Fthe-monthly-metric-overall-equipment-effectiveness\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">Inside Supply Management magazine\u003C\u002Fa>.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Cp>His read is the honest one. The argument flares up on LinkedIn every few months, runs hundreds of comments deep and the pattern never changes. Defenders say context. Critics say gamed. Both sides are describing the same broken deployment. The number belongs inside a wider analytics picture, working alongside other signals, not alone on a pedestal where a decade of dashboard culture has left it.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"why-oee-dashboards-fail-flaws-and-common-critiques-of-oee-0\">Why OEE Dashboards Fail: Flaws and Common Critiques of OEE\u003C\u002Fh2>\n\n\n\n\u003Cp>Start with the arithmetic. OEE multiplies availability, performance, and quality into one number and multiplication is a shredder. A 73 tells you something is wrong and nothing about what. The line that lost a shift to a breakdown and the line running slow on a bad batch of material can post the same score and the composite treats them as the same problem. The three components were the information. The product of the three is a summary of a summary.\u003C\u002Fp>\n\n\n\n\u003Cp>The number is also softer than it looks, because two of its inputs are choices. Set the ideal cycle time at what the line demonstrated on its best sustained run and performance means something. Set it at what the line negotiated after a rough quarter and performance flatters everyone. Definitions drift the same way, plant by plant, until the corporate scorecard compares numbers that were never calculated the same. That’s how a company ends up benchmarking eight sites on eight different rulers and crowning a winner nobody should copy.\u003C\u002Fp>\n\n\n\n\u003Cp>The folklore makes it worse. The 85 percent benchmark that gets treated as the finish line traces to the Total Productive Maintenance (TPM) movement in 1980s Japan and the high-volume automotive lines it grew up on. Hold a high-mix job shop or a regulated pharmaceutical line to that number and the target isn’t ambitious, it’s irrelevant. And even a well-built OEE arrives late. A month-end dashboard is an autopsy. The batch already shipped, the shift already ended and the loss already happened by the time the number turns red.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"what-is-operational-intelligence-moving-beyond-static-oee-dashboards-1\">What Is Operational Intelligence? Moving Beyond Static OEE Dashboards\u003C\u002Fh2>\n\n\n\n\u003Cp>All three critiques are true and none of them argue for measuring less. They argue against stopping at a number on a wall. Jacobson’s read cuts through the war itself. He told ISM he wouldn’t call the metric polarizing and his prescription is placement, OEE working inside a wider analytics dashboard where other signals can check it, challenge it and explain it. The payoff for measurement built that way is documented. Manufacturers in Deloitte’s \u003Ca href=\"https:\u002F\u002Fwww.deloitte.com\u002Fus\u002Fen\u002Finsights\u002Findustry\u002Fmanufacturing-industrial-products\u002F2025-smart-manufacturing-survey.html\" target=\"_blank\" rel=\"noreferrer noopener\">2025 Smart Manufacturing and Operations Survey\u003C\u002Fa> reported a 10 to 20 percent average improvement in production output from their smart manufacturing initiatives and none of that came from admiring a composite score. It came from finding losses and removing them, which is the job OEE was designed to start, not finish. The metric is a flare. It tells you where to look. The looking is a different discipline and it has a name.\u003C\u002Fp>\n\n\n\n\u003Cp>Operational intelligence is what metrics become when they’re wired to context and action. \u003Ca href=\"https:\u002F\u002Fwww.cesmii.org\u002Fabout\u002Fwhat-is-smart-manufacturing\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">CESMII\u003C\u002Fa>, the United States’ national Smart Manufacturing institute, defines the target state as processes continuously evaluated “based on all available contextual information to support near real-time, informed decision making.” Read that against the standard OEE dashboard and the gap is the whole story. Contextual means the number knows which work order, which product, which asset and which shift produced it. Near real-time means the number arrives while the loss is still happening. Informed decision means somebody, or something, acts on it before the shift ends.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"five-tests-to-evaluate-your-oee-dashboards-vs-operational-intelligence-2\">Five Tests to Evaluate Your OEE Dashboards vs. Operational Intelligence\u003C\u002Fh2>\n\n\n\n\u003Cp>Run your own metrics program against five tests. Each one is checkable in an afternoon and each one converts a critique into a requirement.\u003C\u002Fp>\n\n\n\n\u003Col class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>A headline number decomposes in two clicks.\u003C\u002Fstrong> From the 73 to the loss behind it, a breakdown, a changeover overrun, a slow cycle or scrap, without exporting anything to a spreadsheet. If decomposition takes a meeting, you have a dashboard.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>One calculation governs every site.\u003C\u002Fstrong> Same definitions, same treatment of planned downtime, same discipline on ideal cycle times, enforced by the platform rather than by memo. Comparison is only honest when the ruler is.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>A moving metric triggers an action in-shift.\u003C\u002Fstrong> A quality slide or a repeating micro-stop reaches the person who can respond while the affected order is still on the line. Metrics that only inform the Monday meeting only improve the Monday meeting.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>The number runs forward, not just backward.\u003C\u002Fstrong> You can load next week’s plan and see the lead times, the bottlenecks and the delivery risk before committing to it. Even a perfect plan stops being perfect minutes after it hits the floor, because a rush order lands or a machine drops, and the schedule that looked airtight at six is fiction by nine. A metric you can only read after the fact is a scorecard. A metric you can test a decision against is intelligence.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Nobody gets paid on the composite alone.\u003C\u002Fstrong> The moment a bonus hangs on the OEE number by itself, the ideal cycle times start softening and the downtime codes start migrating. Reward the loss reduction the number points to, and the number takes care of itself.\u003C\u002Fli>\n\u003C\u002Fol>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"leveraging-mom-software-for-actionable-real-time-oee-metrics-3\">Leveraging MOM Software for Actionable Real-Time OEE Metrics\u003C\u002Fh2>\n\n\n\n\u003Cp>Theory is fine. Execution is what turns the Monday meeting into a shorter one.\u003C\u002Fp>\n\n\n\n\u003Cblockquote class=\"is-layout-flow wp-block-quote-is-layout-flow\">\n\u003Cp>It’s not just about putting in place the right technology,” Jacobson said in \u003Ca href=\"https:\u002F\u002Fmanufacturingdigital.com\u002Farticles\u002Fgartner-making-smart-manufacturing-smarter\" target=\"_blank\" rel=\"noreferrer noopener\">Gartner’s briefing on supply chain technology trends\u003C\u002Fa>. \u003C\u002Fp>\n\u003C\u002Fblockquote>\n\n\n\n\u003Cp>\u003C\u002Fp>\n\n\n\n\u003Cp>Nowhere does that land harder than on metrics programs, where the software was never the missing piece. A \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\u002Fmanufacturing-operations\u002Fmanufacturing-execution-system\" target=\"_blank\" rel=\"noreferrer noopener\">standalone MES\u003C\u002Fa> can score its own line. Carrying one governed calculation and one comparable truth, across a network is \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\u002Fmanufacturing-operations\u002Fmanufacturing-operations-management\" target=\"_blank\" rel=\"noreferrer noopener\">MOM\u003C\u002Fa> work. \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\u002Fapriso\" target=\"_blank\" rel=\"noreferrer noopener\">DELMIA Apriso\u003C\u002Fa> treats metrics as an output of execution rather than a report bolted on afterward. Because the platform runs the work order, every reading arrives with its context attached, the asset, the order, the operator, the material lot, so decomposition is native rather than a data project. Global process templates enforce the same calculation at every plant, which closes the ruler problem and \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\u002Fapriso\u002Fproduction\" target=\"_blank\" rel=\"noreferrer noopener\">production monitoring and analytics\u003C\u002Fa> turn live signals into alerts the current shift can act on instead of findings the next one reads about. And because the operational record connects to planning, the same data that scores last shift can stress-test next week’s schedule.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"eurowipes-case-study-improving-on-time-delivery-with-real-time-scheduling-4\">Eurowipes Case Study: Improving On-Time Delivery with Real-Time Scheduling\u003C\u002Fh2>\n\n\n\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Finsights\u002Fcustomer-stories\u002Feurowipes\" target=\"_blank\" rel=\"noreferrer noopener\">Eurowipes\u003C\u002Fa> needed more agility and clearer visibility into its production and planning schedules, and found both by putting \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\u002Fortems\" target=\"_blank\" rel=\"noreferrer noopener\">DELMIA Ortems\u003C\u002Fa> under all of its production planning and optimization. The French manufacturer, part of the Anjac Health and Beauty Group, makes wipes, cotton pads and sheet masks for skincare and cosmetics brands, work that runs completely different orders across the same lines. Its planners now manage production planning in real time and run simulations to test a schedule before locking it in.\u003C\u002Fp>\n\n\n\n\u003Cp>As Sophie Haouisee, Supply Chain Manager, puts it:\u003C\u002Fp>\n\n\n\n\u003Cblockquote class=\"is-layout-flow wp-block-quote-is-layout-flow\">\n\u003Cp>Thanks to this visual solution, we can see production orders, line loads in finite or infinite capacity, and material shortages.” \u003C\u002Fp>\n\u003C\u002Fblockquote>\n\n\n\n\u003Cp>That’s the first test and the fourth passing at once, decomposition you can see and a plan you can audition. The scoreboard moved where budgets can see it. On-time delivery climbed from 88% to 92%, a number you can trace from the method straight to the result.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"conclusion-transforming-oee-into-real-time-operational-intelligence-5\">Conclusion: Transforming OEE into Real-Time Operational Intelligence\u003C\u002Fh2>\n\n\n\n\u003Cp>The OEE argument has been good for manufacturing. The critics forced the right question, whether the number actually drives anything, and plenty of dashboards deserve the beating. The answer isn’t to retire the metric. It’s to stop asking a composite to do a platform’s job. Wire the number to context so it explains itself, to governance so it compares honestly, to alerts so it acts in-shift, and to planning so it looks forward, and the argument resolves itself line by line. That’s how the water Jacobson described comes out of the wine. Put your metrics on a modern MOM platform like \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\" target=\"_blank\" rel=\"noreferrer noopener\">DELMIA\u003C\u002Fa>, then run the five tests this quarter. Wherever your program fails one, that’s not an indictment of OEE. It’s the next item on your roadmap, and it’s the cheapest improvement you’ll fund this year.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n\u003Cdiv class=\"wp-block-button is-style-fill\">\u003Ca class=\"wp-block-button__link has-text-color has-background has-link-color wp-element-button\" href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fdelmia\" style=\"color:#000000fc;background-color:#ffcd00\" target=\"_blank\" rel=\"noreferrer noopener\">\u003Cstrong>Visit DELMIA’s Website\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\n\n\n\u003Cp>\u003Cem>DELMIA, from Dassault Systèmes, enables manufacturers to keep factory operations running smoothly. Powered by the \u003Cstrong>3D\u003C\u002Fstrong>EXPERIENCE platform, our Manufacturing Operations Management (MOM) and Manufacturing Execution Systems (MES) solutions establish a unified digital environment that provides real-time visibility and AI-enhanced control. By connecting the virtual and real worlds, we enable you to streamline complex processes, minimize waste and guarantee quality. Harnessing data-driven insights and intelligent automation allows for optimized production, enhanced adaptability to disruptions and the delivery of sustainable, customer-focused manufacturing performance at scale.\u003C\u002Fem>\u003C\u002Fp>\n","2026-10-23T01:56:00",[],{"node":45,"__typename":56},{"nicename":46,"description":47,"slug":46,"name":48,"firstName":49,"lastName":50,"avatar":51,"__typename":55},"louiscolumbus","Louis Columbus is currently serving as a member of the DELMIA Marketing team, where he assists with DELMIAWorks and robotics solutions. Previous positions include product management at Ingram Cloud, product marketing at iBASEt, Plex Systems, senior analyst at AMR Research (now Gartner), marketing and business development at Cincom Systems, Ingram Micro, a SaaS start-up and at hardware companies. Professional experience includes marketing, product management, sales and industry analyst roles in the enterprise software and IT industries. Columbus' academic background includes an MBA from Pepperdine University and completion of the Strategic Marketing Management and Digital Marketing Programs at the Stanford University Graduate School of Business. He teaches MBA courses in international business, global competitive strategies, international market research, and capstone courses in strategic planning and market research. Columbus has taught at California State University, Fullerton: University of California, Irvine; Marymount University, and Webster University.","Louis Columbus","Louis","Columbus",{"default":52,"url":53,"__typename":54},"mm","https:\u002F\u002Fsecure.gravatar.com\u002Favatar\u002F3d07956a519d5c4d98b3a22d5efa5a6f76a14015afbce4388179a36c8eab3914?s=96&d=mm&r=g","Avatar","User","NodeWithAuthorToUserConnectionEdge",{"edges":58,"nodes":66,"__typename":70},[59],{"isPrimary":60,"node":61,"__typename":65},true,{"slug":62,"name":63,"__typename":64},"manufacturing","Manufacturing","Taxonomy_topic","PostToTaxonomy_topicConnectionEdge",[67],{"id":68,"name":63,"uri":69,"__typename":64},"dGVybTo4MDE0","\u002Ftopics\u002Fmanufacturing\u002F","PostToTaxonomy_topicConnection",{"nodes":72,"__typename":73},[],"PostToTaxonomy_tagConnection",{"edges":75,"nodes":82,"__typename":84},[76],{"isPrimary":60,"node":77,"__typename":81},{"slug":78,"name":79,"__typename":80},"delmia","DELMIA","Taxonomy_brand","PostToTaxonomy_brandConnectionEdge",[83],{"name":79,"slug":78,"__typename":80},"PostToTaxonomy_brandConnection",{"nodes":86,"__typename":94},[87,90,92],{"name":88,"__typename":89},"Metrics","Taxonomy_keyword",{"name":91,"__typename":89},"OEE",{"name":93,"__typename":89},"Operational Intelligence","PostToTaxonomy_keywordConnection",{"title":96,"metaDesc":97,"opengraphAuthor":98,"opengraphDescription":97,"opengraphTitle":20,"opengraphUrl":99,"opengraphSiteName":100,"opengraphPublishedTime":101,"opengraphModifiedTime":102,"twitterTitle":98,"twitterDescription":98,"readingTime":103,"metaRobotsNoindex":104,"__typename":105},"From OEE Dashboards to Operational Intelligence: Optimizing Manufacturing Metrics - Dassault Systèmes blog","Learn how to move past raw metrics to eliminate bottlenecks, boost throughput, and drive continuous improvement across your manufacturing operations.","","https:\u002F\u002Fblog.3ds.com\u002Fbrands\u002Fdelmia\u002Ffrom-oee-dashboards-to-operational-intelligence-optimizing-manufacturing-metrics\u002F","Dassault Systèmes blog","2026-10-23T01:56:00+00:00","2026-10-23T01:56:01+00:00",7,"index","PostTypeSEO","Post","RootQueryToPostConnection",{},{},1792739593373]