{"id":3507,"date":"2026-04-03T15:03:05","date_gmt":"2026-04-03T12:03:05","guid":{"rendered":"https:\/\/edilo.com.ua\/blog\/digital-twins-in-business-what-they-are-types-and-how-to-create-them\/"},"modified":"2026-06-22T12:50:41","modified_gmt":"2026-06-22T09:50:41","slug":"digital-twins-in-business-what-they-are-types-and-how-to-create-them","status":"publish","type":"post","link":"https:\/\/edilo.com.ua\/en\/blog\/digital-twins-in-business-what-they-are-types-and-how-to-create-them\/","title":{"rendered":"Digital Twins in Business: What They Are, Types, and How to Create Them"},"content":{"rendered":"\n

The global digital twin market is estimated at $36.19 billion<\/a> in 2025, and by 2030 it will exceed $180 billion. Three-quarters of large companies are already using this technology\u2014and not just industrial giants. Cloud solutions and the DTaaS (digital twin as a service) model have made digital twins accessible to medium-sized businesses as well. A virtual replica of a production line, warehouse, or supply chain allows companies to test changes without real-world risks or costs. In this article, we\u2019ll explore how digital twins work, the different types available, and what businesses need to create their first prototype. <\/p>\n\n

What Is a Digital Twin and How Does It Work?<\/h2>\n\n

Let\u2019s assume that your warehouse, production line, or delivery network exists simultaneously in two dimensions\u2014the physical and the virtual. The physical dimension operates as usual, while the virtual dimension collects data from dozens of sensors, analyzes it, and shows exactly where the process is slowing down, where equipment is wearing out, and where you\u2019re losing money. <\/p>\n\n

In other words, a digital twin is a virtual copy of a real-world object or process that is synchronized with the original in real time. Unlike a conventional 3D model or computer simulation, a digital twin does more than just replicate the object\u2019s appearance\u2014it reacts to changes. It receives a stream of data from IoT sensors, processes it using machine learning algorithms, detects anomalies, and provides forecasts and recommendations even before a problem becomes noticeable to the business. <\/p>\n\n

The mechanism of this model consists of three levels:<\/p>\n\n

    \n
  1. Physical \u2014 a real-world object equipped with sensors (temperature, pressure, vibration, geolocation). The sensors transmit data to the cloud every second or at another specified frequency. <\/li>\n\n\n\n
  2. Analytics \u2014 a cloud-based infrastructure where AI and ML algorithms process data streams. The system detects anomalies, generates wear-and-tear forecasts, and models \u201cwhat-if\u201d scenarios. <\/li>\n\n\n\n
  3. The decision-making layer is the interface through which managers view the status of a facility, receive risk alerts, and test changes before implementing them in practice.<\/li>\n<\/ol>\n\n

    Let\u2019s consider a specific scenario. A manufacturing company produces home appliances and supplies them to retail chains. The main requirement from customers is reliable and prompt delivery without delays. The company creates a digital twin of its supply chain: from the production line to the warehouse and delivery routes. The twin shows in real time where inventory is building up, where delays occur between stages, and predicts shortages a week before they affect customers. Instead of manually monitoring dozens of metrics, managers see a unified overview and make decisions based on data rather than intuition. <\/p>\n\n

    Another example is the energy sector. Wind farm operators install hundreds of sensors on each turbine. The digital twin analyzes the load on the blades, predicts bearing wear, and automatically adjusts the pitch angle\u2014even before a component fails. The result: fewer unplanned outages and lower costs for emergency repairs. <\/p>\n\n

    To better understand where this technology fits in among related solutions, let\u2019s compare a digital twin to a 3D model and a simulation:<\/p>\n\n

    Specifications<\/td>3D model<\/td>Simulation<\/td>Digital Twin<\/td><\/tr>
    Data<\/td>Static cross-section<\/td>Historical or hypothetical<\/td>Real-time, two-way<\/td><\/tr>
    Connection to the object<\/td>Not available<\/td>Unilateral or absent<\/td>Continuous exchange<\/td><\/tr>
    Main objective<\/td>Show view<\/td>To predict in theory<\/td>Manage, prevent failures, optimize<\/td><\/tr><\/tbody><\/table><\/figure>\n\n

    See also: <\/em><\/strong>Profit Margin: What It Is and How to Calculate It<\/em><\/strong><\/a><\/p>\n\n

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    Types of Digital Twins: Examples for Business<\/h2>\n\n

    There is no one-size-fits-all solution. That is why it is important to consider examples of the types of digital twins that businesses choose, depending on the scale of the task\u2014ranging from a copy of a single part to a model of an entire enterprise. <\/p>\n\n

    Component duplicates replicate a specific piece of equipment: a pump, a valve, or a bearing. Their main value lies in the continuous monitoring of wear on critical parts. When a sensor detects a deviation from the norm, the system alerts technicians in advance, rather than after a breakdown occurs. <\/p>\n\n

    Product twins combine several components into a single, integrated object\u2014a machine tool, a turbine, or a truck. They show how individual parts interact with each other and help identify the weak link before it causes the entire mechanism to fail. One notable example: the consulting firm Challenge Advisory created a digital twin of a Boeing 737-800 and demonstrated that the maximum allowable cargo weight could be safely increased by 23%. For airlines, this means thousands of dollars in additional profit on every flight. <\/p>\n\n

    System twins simulate the interaction of multiple assets: an assembly line, a building\u2019s power supply system, or a server cluster in a data center. For example, a factory combines the stages of raw material supply, production, quality control, and packaging into a single system twin. A manager notices that a delay at the quality control stage is causing a 40-minute bottleneck due to manual sorting\u2014and tests the automation of this process in a virtual environment before purchasing the equipment. <\/p>\n\n

    Process twins cover the broadest scope\u2014the entire business operations model, from raw material procurement to delivery to the customer. IKEA, for example, created process twins for 37 facilities with a total area of 42 million square feet. Over the course of nine months, the company analyzed 6,000 units of HVAC equipment and reduced the energy consumption of its ventilation system by 30%. <\/p>\n\n

    For medium-sized businesses in Ukraine, where operations are smaller than IKEA\u2019s, the most practical way to get started is with component- or product-based digital twins. They require a minimal number of sensors, deliver results quickly, and do not require a complete overhaul of the IT infrastructure. And with the emergence of cloud services such as DTaaS (digital twin as a service), even a small business doesn\u2019t need to maintain its own server infrastructure\u2014all it needs is a subscription and a set of sensors. <\/p>\n\t

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