{"id":1783,"date":"2026-11-11T00:00:38","date_gmt":"2026-11-11T00:00:38","guid":{"rendered":"https:\/\/mestric.com\/digitalni-dvojcek-proizvodnja\/"},"modified":"2026-11-11T00:00:41","modified_gmt":"2026-11-11T00:00:41","slug":"digitalni-dvojcek-proizvodnja","status":"publish","type":"post","link":"https:\/\/mestric.com\/sl\/digitalni-dvojcek-proizvodnja\/","title":{"rendered":"Digital Twins in Manufacturing: A Practical Guide for Slovenian Plants"},"content":{"rendered":"<\/p>\n<p>A digital twin in manufacturing is a virtual model of a machine, line or process that stays synchronised with the real one through live data. It is not a 3D drawing and not a one-off simulation. It only becomes useful once reliable data flows from your shop floor into the model. For most plants, that data foundation is the real starting point, and it is where you can make progress this quarter.<\/p>\n<p>This guide explains what a digital twin is, how it connects to equipment, where it pays off, what Slovenian projects have reported and how to start without a multi-million budget.<\/p>\n<div data-blg-cta=\"after_tldr\" data-blg-cta-layout=\"banner\" style=\"margin:28px 0;font-family:-apple-system, BlinkMacSystemFont, &apos;Segoe UI&apos;, Roboto, Helvetica, Arial, sans-serif\">\n<div style=\"border-radius:26px;padding:min(22px,3.2vw);background:radial-gradient(circle at 100% 0%,#e3eeec 0 150px,rgba(255,255,255,0) 151px),radial-gradient(circle at 0% 100%,#e3eeec 0 130px,rgba(255,255,255,0) 131px),linear-gradient(180deg,#edf3f2 0%,#f6f9f9 100%)\">\n<div style=\"background:#ffffff;border-radius:18px;overflow:hidden\">\n<div style=\"padding:34px 30px;text-align:center\">\n<div style=\"margin:0 0 18px\"><span style=\"display:inline-block;max-width:100%;border-radius:999px;padding:6px 13px;font-size:12px;font-weight:800;letter-spacing:0.1em;text-transform:uppercase;line-height:1.3;background:#669f94;color:#ffffff\">Mestric<\/span><\/div>\n<div style=\"font-size:26px;font-weight:800;line-height:1.2;letter-spacing:-0.01em;color:#1f2937;margin:0\">Build a Stronger Foundation for Digital Twins<\/div>\n<div style=\"width:56px;height:6px;border-radius:3px;background:#669f94;margin:12px 0 14px;margin-left:auto;margin-right:auto\"><\/div>\n<div style=\"font-size:15px;line-height:1.55;color:#64748b;margin:0 0 24px;max-width:44em;margin-left:auto;margin-right:auto\">Mestric connects with manufacturing equipment to track performance, downtime and quality, helping teams work with real-time production data.<\/div>\n<p><a href=\"https:\/\/mestric.com\/sl\/\" style=\"display:inline-flex;align-items:center;gap:9px;border-radius:10px;font-weight:700;font-size:15px;text-decoration:none;padding:13px 22px 13px 26px;background:#669f94;color:#ffffff\">Explore Mestric<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"tldr\" tabindex=\"-1\">TL;DR<\/h2>\n<ul>\n<li>A digital twin is a model that is connected to a physical system and updated with its data. A model with no automated data exchange is only a digital model.<\/li>\n<li>The twin depends on a data layer: sensors, protocols such as MQTT or OPC-UA, and clean production data from machines.<\/li>\n<li>Reported results include <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9506524\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">6.01% higher line efficiency in an automotive case study<\/a> and lower energy use and scrap in a Slovenian toolmaker project.<\/li>\n<li>Start with one or two use cases, measurable KPIs and a named owner, then scale.<\/li>\n<li>Real-time machine data capture is the first stage of any twin, and it already delivers value on its own.<\/li>\n<\/ul>\n<h2 id=\"table-of-contents\" tabindex=\"-1\">Vsebina<\/h2>\n<ul>\n<li><a href=\"#what-a-digital-twin-is-and-what-it-is-not\">What a digital twin is, and what it is not<\/a><\/li>\n<li><a href=\"#how-a-digital-twin-connects-to-your-machines\">How a digital twin connects to your machines<\/a><\/li>\n<li><a href=\"#where-digital-twins-are-used-in-a-factory\">Where digital twins are used in a factory<\/a><\/li>\n<li><a href=\"#what-results-have-been-reported\">What results have been reported<\/a><\/li>\n<li><a href=\"#what-you-need-before-you-start\">What you need before you start<\/a><\/li>\n<li><a href=\"#common-challenges-and-how-to-plan-for-them\">Common challenges and how to plan for them<\/a><\/li>\n<li><a href=\"#a-step-by-step-way-to-start\">A step-by-step way to start<\/a><\/li>\n<li><a href=\"#our-perspective\">Our perspective<\/a><\/li>\n<li><a href=\"#see-live-machine-data-in-practice\">See live machine data in practice<\/a><\/li>\n<li><a href=\"#faq\">Pogosto zastavljena vpra\u0161anja<\/a><\/li>\n<li><a href=\"#sources\">Sources<\/a><\/li>\n<\/ul>\n<h2 id=\"what-a-digital-twin-is-and-what-it-is-not\" tabindex=\"-1\">What a digital twin is, and what it is not<\/h2>\n<p>NIST describes a digital twin as an integrated model that represents, connects and synchronises part or all of a physical manufacturing system or process, using historical and real-time data. The same <a href=\"https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=932374\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">NIST paper<\/a> notes that simulations imitate operations, but most run standalone, without a direct link to real systems.<\/p>\n<p>The ISA breaks the idea into <a href=\"https:\/\/www.isa.org\/intech-home\/2022\/august-2022\/features\/digital-twin-and-simulation-replicating-industrial\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">three levels of data integration<\/a>:<\/p>\n<table>\n<thead>\n<tr>\n<th>Level<\/th>\n<th>Data flow<\/th>\n<th>Typical factory example<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Digital model<\/td>\n<td>No automated exchange; data is entered by hand<\/td>\n<td>A layout or flow model built in simulation software<\/td>\n<\/tr>\n<tr>\n<td>Digital shadow<\/td>\n<td>One-way automatic flow, physical to digital<\/td>\n<td>A live dashboard that mirrors line status and counts<\/td>\n<\/tr>\n<tr>\n<td>Digital twin<\/td>\n<td>Two-way automatic flow<\/td>\n<td>A model that sends updated control parameters back to the machine<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Most plants that say they have a twin are at the shadow stage. That is a perfectly good place to be. The model is already fed by real data, and you can add feedback to the machines later.<\/p>\n<p>One more distinction matters for planning. Shao and Helu note that an <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7431924\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">offline digital twin connects to the physical system periodically<\/a> rather than continuously. That periodic link is what separates it from a traditional simulation model. So a twin does not have to run at millisecond speed to be worth building. A twin for production planning may refresh every shift.<\/p>\n<p>NIST\u2019s Simon Taylor adds a useful point of view: the most important attribute of a twin is that it represents the behaviour of the system. Connecting and synchronising are the means to that end. The aim is to make decisions faster and more agile, not to make the model look clever.<\/p>\n<h2 id=\"how-a-digital-twin-connects-to-your-machines\" tabindex=\"-1\">How a digital twin connects to your machines<\/h2>\n<p>IEEE describes a twin system in <a href=\"https:\/\/technav.ieee.org\/topic\/digital-twin\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">three layers<\/a>:<\/p>\n<ol>\n<li><strong>Physical layer:<\/strong> the asset, its sensors, actuators and communication hardware.<\/li>\n<li><strong>Data layer:<\/strong> transport and storage of readings, using protocols such as MQTT, OPC-UA or industrial buses.<\/li>\n<li><strong>Digital layer:<\/strong> the model that represents the state of the asset.<\/li>\n<\/ol>\n<p>NIST\u2019s standardised approach uses similar logic. It describes an <a href=\"https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=957417\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">observable manufacturing domain<\/a> (people, equipment, material, processes), a device communication domain for data exchange and synchronisation, and the digital twin domain, which hosts the models and the analytics, simulation and optimisation services.<\/p>\n<p>The middle layer is where most projects stall. Plants run machines from different decades and vendors. Some have modern controllers, others have only a signal tower and a counter. If you cannot get a stable stream of cycle counts, stoppage reasons and quality values out of the machine, the best model will show you fiction.<\/p>\n<p>That is why we point plant engineers to the connectivity question first. Our guides on the <a href=\"https:\/\/mestric.com\/sl\/role-iot-manufacturing-boosting-efficiency-2026\/\" target=\"_blank\" rel=\"noopener\">role of IoT in manufacturing<\/a> and on <a href=\"https:\/\/mestric.com\/sl\/role-of-iot-gateways-in-manufacturing-2026-guide\/\" target=\"_blank\" rel=\"noopener\">IoT gateways in manufacturing<\/a> cover the hardware and protocol side. If your fleet includes older equipment, our page on <a href=\"https:\/\/mestric.com\/sl\/povezava-starih-strojev\/\" target=\"_blank\" rel=\"noopener\">connecting legacy machines<\/a> describes how a pilot with one to three machines works.<\/p>\n<h2 id=\"where-digital-twins-are-used-in-a-factory\" tabindex=\"-1\">Where digital twins are used in a factory<\/h2>\n<p>Fraunhofer lists use cases across the whole value chain: <a href=\"https:\/\/publica-rest.fraunhofer.de\/server\/api\/core\/bitstreams\/741ecb0b-1d56-4d14-835b-f95708c5295d\/content\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">product planning and development, production planning, production and assembly, logistics, maintenance and service, disassembly and recycling<\/a>. For a plant manager, four are the most practical:<\/p>\n<ul>\n<li><strong>Production planning and scheduling.<\/strong> The twin tests a new sequence or a rush order against current machine states before you commit. See our guide to <a href=\"https:\/\/mestric.com\/sl\/ai-optimizacija-razporeda-proizvodnje\/\" target=\"_blank\" rel=\"noopener\">Programiranje proizvodnje z umetno inteligenco<\/a> for how live data feeds the plan.<\/li>\n<li><strong>Downtime reduction.<\/strong> A model of a bottleneck line shows where stoppages begin and what a change would do to output.<\/li>\n<li><strong>Quality monitoring.<\/strong> Process parameters and quality results are tied together, so you can see which settings produce scrap.<\/li>\n<li><strong>Energy and tool wear.<\/strong> Sensor data predicts consumption and wear, so you can plan maintenance and machine allocation.<\/li>\n<\/ul>\n<p>A twin also helps you present the factory. Photorealistic models are useful for explaining a line to management and customers, and a guide on <a href=\"https:\/\/rendimension.com\/blog\/3d-visualization-for-manufacturers-what-to-expect\" target=\"_blank\" rel=\"noopener\">3D visualisation for manufacturers<\/a> shows what to expect from that work. Visualisation is the presentation layer, though. The value for operations comes from the live data behind it.<\/p>\n<h2 id=\"what-results-have-been-reported\" tabindex=\"-1\">What results have been reported<\/h2>\n<p>Be careful with headline numbers. Each figure below comes from a specific project with its own baseline, and your results will depend on your starting point.<\/p>\n<ul>\n<li><strong>Automotive line.<\/strong> A digital twin case study reported a <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9506524\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">6.01% increase in production-line efficiency and an 87.56% gain for downtime<\/a>.<\/li>\n<li><strong>Semiconductor power modules.<\/strong> A twin-driven IGBT production line <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10818924\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">enhanced yield rates and efficiency<\/a>, according to Zhang et al.<\/li>\n<li><strong>Defect reduction.<\/strong> EY reports that a manufacturer using process twins to optimise parameters achieved a <a href=\"https:\/\/www.ey.com\/en_in\/insights\/technology\/digital-twins-creating-intelligent-industries\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">75% reduction in defective products<\/a>.<\/li>\n<\/ul>\n<h3 id=\"slovenian-examples\" tabindex=\"-1\">Slovenian examples<\/h3>\n<p>Two Slovenian projects show what this looks like locally.<\/p>\n<p>Gorenje Orodjarna took part in the DENiM project. It linked MODBUS energy and air-consumption sensors with ERP production data, built an energy dashboard and a machine-learning prediction pipeline, and used an AI tool for machine selection. The company <a href=\"https:\/\/www.gorenje-orodjarna.si\/research-development\/denim\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">reports a 21% electrical energy reduction per operation, a 37% overall energy reduction and a 22% scrap reduction<\/a>, with 54% of energy-intensive machines monitored.<\/p>\n<p>Iskraemeco ran the DT4DL project from April 2022 to March 2024. Its total value was \u20ac5,420,000, of which \u20ac1,660,000 was co-financed with European funds. The project covered digital twins for production and knowledge, AI decision support, IoT data capture, analytics and AR\/VR. Miha Kern, Director of Manufacturing Engineering and Production, said it contributed to <a href=\"https:\/\/iskraemeco.com\/news\/digital-twin-for-digital-lean-a-successful-project-that-attracted-attention\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">better traceability, real-time insight and fewer errors and unnecessary stoppages<\/a>, and strengthened employees\u2019 digital skills.<\/p>\n<p>Notice what both projects share: sensor and production data connected to a model, then used for a concrete decision. Neither started with software alone.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/mestric.com\/wp-content\/uploads\/2026\/11\/1794198691788_Slovenian-examples.jpeg\" alt=\"Slovenian examples\"><\/p>\n<h2 id=\"what-you-need-before-you-start\" tabindex=\"-1\">What you need before you start<\/h2>\n<h3 id=\"data\" tabindex=\"-1\">Data<\/h3>\n<p>You need machine states, cycle counts, stoppage reasons, quality parameters and order data in one place, collected automatically. Manual entry is too slow and too error-prone to keep a twin synchronised. Our article on the <a href=\"https:\/\/mestric.com\/sl\/role-data-manufacturing-efficiency-quality\/\" target=\"_blank\" rel=\"noopener\">role of data in manufacturing<\/a> covers data types and collection methods in more detail.<\/p>\n<h3 id=\"skills\" tabindex=\"-1\">Skills<\/h3>\n<p>Timperi et al. identify <a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-74779-3_7\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">four competence groups<\/a>: technological skills (IT, programming, simulation, installation, plus mechanics, electricity and hydraulics), cognitive skills (analysis, problem-solving), soft skills (collaboration, curiosity) and managerial skills (supply chain and operations knowledge). They also argue that ownership of the twin, the core competence to maintain and fine-tune it, and the ability to lead development must sit inside the company. In practice, name one production owner and one technical owner early.<\/p>\n<h3 id=\"security\" tabindex=\"-1\">Security<\/h3>\n<p>In a Slovenian pilot reported under the Inno2Mare programme, the parties agreed that the twin would <a href=\"https:\/\/www.inno2mare.eu\/wp-content\/uploads\/2025\/03\/Progress-31_12_2023_Webpage.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">not include sensitive data such as details of faulty parts<\/a>. Each production line acted as a black box, focused on order numbers and energy consumption, and data moved over a secure VPN. Deciding what the twin may see is a sensible first governance step.<\/p>\n<h2 id=\"common-challenges-and-how-to-plan-for-them\" tabindex=\"-1\">Common challenges and how to plan for them<\/h2>\n<p>A <a href=\"https:\/\/arxiv.org\/pdf\/2511.06119\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">review of twins at different manufacturing levels<\/a> points to high initial costs and data-integration problems at line level, model complexity and cybersecurity risks at facility level, and oversimplification and organisational obstacles at enterprise level. Another study lists <a href=\"https:\/\/cognizancejournal.com\/vol5issue1\/V5I104.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">integration, investment cost, cybersecurity and a shortage of skilled people<\/a> as the main hurdles.<\/p>\n<p>The planning answer to each is the same: keep the first scope small.<\/p>\n<ul>\n<li>Integration: start with the machines on one line.<\/li>\n<li>Cost: fund a pilot, not a plant-wide programme.<\/li>\n<li>Complexity: model the decision you need, not the whole factory.<\/li>\n<li>Organisation: agree owners and KPIs before connecting anything.<\/li>\n<\/ul>\n<h2 id=\"a-step-by-step-way-to-start\" tabindex=\"-1\">A step-by-step way to start<\/h2>\n<p>The Singapore IMDA playbook offers a clear structure, and it applies well to a mid-sized plant. It recommends <a href=\"https:\/\/www.imda.gov.sg\/-\/media\/imda\/file\/enterprise-digital-transformation\/digital-twin\/digital-twin-for-enterprises-playbook.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">anchoring the twin to outcomes, starting with one or two priority use cases and a two-stage rollout<\/a>.<\/p>\n<ol>\n<li><strong>Write a short vision.<\/strong> For example, reduce unplanned downtime on a bottleneck line by 50% within six months, which is the kind of example the playbook gives.<\/li>\n<li><strong>Pick one or two use cases<\/strong> tied to an operational decision.<\/li>\n<li><strong>Set KPIs and baselines.<\/strong> Measure the current state before you change anything. Our step on <a href=\"https:\/\/mestric.com\/sl\/how-to-streamline-manufacturing-processes\/#step-1-assess-current-production-workflows-and-data\" target=\"_blank\" rel=\"noopener\">assessing current workflows and data<\/a> helps here.<\/li>\n<li><strong>Name business and technical owners.<\/strong><\/li>\n<li><strong>Stage 1:<\/strong> connect the core signals and build simple role-based dashboards.<\/li>\n<li><strong>Stage 2:<\/strong> add predictive analytics and what-if simulation once the data is ready.<\/li>\n<\/ol>\n<p>The playbook\u2019s readiness test for Stage 2 is practical: at least 4 to 8 weeks of stable data, agreed baseline KPIs and users confirming that dashboards are accurate enough to use in discussions.<\/p>\n<p>When the first line works, scaling is a matter of repeating the pattern. A general guide on how to <a href=\"https:\/\/blog.sarawestusa.com\/blog\/scale-up-manufacturing\" target=\"_blank\" rel=\"noopener\">scale up manufacturing<\/a> is a helpful companion for the capacity and process side of that step.<\/p>\n<h2 id=\"our-perspective\" tabindex=\"-1\">Our perspective<\/h2>\n<blockquote>\n<p>Most of the digital twin conversations we hear begin with the model and end with a data problem. The plant buys or builds a clever representation, then discovers that machine states, stoppage reasons and quality values are still written on paper or typed in at the end of a shift.<\/p>\n<p>We think the order should be reversed. Capture the data automatically first, from the machines you already have. Show it live to the people who run the line. Agree the KPIs: performance, downtime, quality parameters and cost. Only then does a model have something true to mirror.<\/p>\n<p>That is how we designed Mestric. It connects directly to production equipment, collects data automatically and presents live KPIs to production managers, with AI-powered tools that point to bottlenecks and process improvements. For many plants, that is the shadow stage of a digital twin, and it pays back in fewer manual errors and faster decisions long before any advanced modelling begins.<\/p>\n<p>If you plan a twin, plan the data layer with the same care as the model. That is where we can help.<\/p>\n<p><em>Andra\u017e<\/em><\/p>\n<\/blockquote>\n<h2 id=\"see-live-machine-data-in-practice\" tabindex=\"-1\">See live machine data in practice<\/h2>\n<p>Mestric\u2122 MES connects to your equipment, captures production data automatically and shows performance, downtime, quality and cost KPIs in real time. It gives you the live data layer a digital twin depends on, and you can see it working in a real production environment at our demo centre.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/mestric.com\/wp-content\/uploads\/2026\/09\/1771068359718_mestric.jpg\" alt=\"Mestric\"><\/p>\n<p>Read more on the <a href=\"https:\/\/mestric.com\/sl\/o-resitvi\/\" target=\"_blank\" rel=\"noopener\">Mestric\u2122 MES solution page<\/a>, or see how <a href=\"https:\/\/mestric.com\/sl\/real-time-nadzor-proizvodnje-v-proizvodnji\/\" target=\"_blank\" rel=\"noopener\">real-time production monitoring<\/a> changes day-to-day decisions on the shop floor.<\/p>\n<h2 id=\"faq\" tabindex=\"-1\">Pogosto zastavljena vpra\u0161anja<\/h2>\n<h3 id=\"what-is-the-difference-between-a-digital-twin-and-a-simulation\" tabindex=\"-1\">What is the difference between a digital twin and a simulation?<\/h3>\n<p>A simulation imitates a process and usually runs standalone, using data you enter. A digital twin is connected to the physical system and updated with its data, so it reflects the current state of the line. In the ISA classification, a twin also exchanges data in both directions, while a shadow only receives it.<\/p>\n<h3 id=\"do-i-need-a-digital-twin-to-benefit-from-real-time-data\" tabindex=\"-1\">Do I need a digital twin to benefit from real-time data?<\/h3>\n<p>No. Live machine data, KPI dashboards and downtime analysis deliver value on their own, because they show where output and quality are lost. They also give any later twin the stable data it needs. The IMDA playbook describes this as the first stage of rollout.<\/p>\n<h3 id=\"how-long-does-it-take-to-prepare-for-a-digital-twin\" tabindex=\"-1\">How long does it take to prepare for a digital twin?<\/h3>\n<p>The IMDA playbook suggests at least 4 to 8 weeks of stable data and agreed baseline KPIs before adding predictive analytics and what-if simulation. The first stage, connecting core signals and building simple dashboards, can start with one line. Timelines depend on how many machines you connect and their condition.<\/p>\n<h3 id=\"which-protocols-connect-machines-to-a-digital-twin\" tabindex=\"-1\">Which protocols connect machines to a digital twin?<\/h3>\n<p>IEEE names MQTT, OPC-UA and industrial buses as common ways to transport readings. Slovenian projects have also used MODBUS sensors, as in the Gorenje Orodjarna project. Older machines can often be connected through a gateway, so replacing equipment is not usually the first step.<\/p>\n<h3 id=\"who-should-own-a-digital-twin-in-the-company\" tabindex=\"-1\">Who should own a digital twin in the company?<\/h3>\n<p>Research on twin competences says ownership, maintenance skills and the ability to lead development should be inside the company. In practice, that means a production owner who defines the decisions and KPIs, and a technical owner who looks after data and models.<\/p>\n<h2 id=\"sources\" tabindex=\"-1\">Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=932374\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">NIST: WSC\u201921 manuscript on digital twins in manufacturing<\/a><\/li>\n<li><a href=\"https:\/\/www.isa.org\/intech-home\/2022\/august-2022\/features\/digital-twin-and-simulation-replicating-industrial\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">ISA: Digital Twin and Simulation<\/a><\/li>\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7431924\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Shao and Helu: Framework for a Digital Twin in Manufacturing<\/a><\/li>\n<li><a href=\"https:\/\/technav.ieee.org\/topic\/digital-twin\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">IEEE Technology Navigator: Digital Twin<\/a><\/li>\n<li><a href=\"https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=957417\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">NIST: Digital Twins for Advanced Manufacturing<\/a><\/li>\n<li><a href=\"https:\/\/publica-rest.fraunhofer.de\/server\/api\/core\/bitstreams\/741ecb0b-1d56-4d14-835b-f95708c5295d\/content\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Fraunhofer: Real-Time Digital Twin<\/a><\/li>\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9506524\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">A Digital Twin Case Study on Automotive Production Line<\/a><\/li>\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10818924\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Digital-Twin-Driven Intelligent IGBT Production<\/a><\/li>\n<li><a href=\"https:\/\/www.ey.com\/en_in\/insights\/technology\/digital-twins-creating-intelligent-industries\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">EY: Digital twins, Creating Intelligent Industries<\/a><\/li>\n<li><a href=\"https:\/\/www.gorenje-orodjarna.si\/research-development\/denim\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Gorenje Orodjarna: DENiM project<\/a><\/li>\n<li><a href=\"https:\/\/iskraemeco.com\/news\/digital-twin-for-digital-lean-a-successful-project-that-attracted-attention\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Iskraemeco: Digital Twin for Digital Lean<\/a><\/li>\n<li><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-74779-3_7\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Timperi et al.: Digital Twin-Related Competences<\/a><\/li>\n<li><a href=\"https:\/\/www.inno2mare.eu\/wp-content\/uploads\/2025\/03\/Progress-31_12_2023_Webpage.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Inno2Mare: pilot project progress report<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/pdf\/2511.06119\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Modeling Different Levels of Manufacturing (arXiv)<\/a><\/li>\n<li><a href=\"https:\/\/cognizancejournal.com\/vol5issue1\/V5I104.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Closed-Loop Manufacturing with AI-Enabled Digital Twin<\/a><\/li>\n<li><a href=\"https:\/\/www.imda.gov.sg\/-\/media\/imda\/file\/enterprise-digital-transformation\/digital-twin\/digital-twin-for-enterprises-playbook.pdf\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">IMDA: Digital Twin Playbook for Enterprises<\/a><\/li>\n<li><a href=\"https:\/\/rendimension.com\/blog\/3d-visualization-for-manufacturers-what-to-expect\" target=\"_blank\" rel=\"noopener\">3D visualisation for manufacturers<\/a><\/li>\n<li><a href=\"https:\/\/blog.sarawestusa.com\/blog\/scale-up-manufacturing\" target=\"_blank\" rel=\"noopener\">How to scale up manufacturing<\/a><\/li>\n<\/ul>\n<h2 id=\"recommended\" tabindex=\"-1\">Priporo\u010deno<\/h2>\n<ul>\n<li><a href=\"https:\/\/mestric.com\/sl\/digitalni-delovni-nalogi\/\" target=\"_blank\" rel=\"noopener\">Digital Work Orders in Manufacturing: A Practical Guide<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/ai-optimizacija-procesa\/\" target=\"_blank\" rel=\"noopener\">AI Process Optimisation in Manufacturing: A Guide for Slovenian Plants<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/industry-40-nacrt\/\" target=\"_blank\" rel=\"noopener\">Industry 4.0 Roadmap for a Slovenian Factory: A Practical Plan<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/vloga-podatkov-v-upravljanju-tovarne-vodic-2026\/\" target=\"_blank\" rel=\"noopener\">The Role of Data in Factory Management: Guide 2026<\/a><\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>What a digital twin in manufacturing is, how it connects to machines, what results to expect and how a Slovenian plant can start with one or two use cases.<\/p>","protected":false},"author":1,"featured_media":1784,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1783","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learn"],"acf":[],"_links":{"self":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/comments?post=1783"}],"version-history":[{"count":1,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1783\/revisions"}],"predecessor-version":[{"id":1786,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1783\/revisions\/1786"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/media\/1784"}],"wp:attachment":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/media?parent=1783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/categories?post=1783"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/tags?post=1783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}