Digital twins – the virtual model of your enterprise

A digital twin is a virtual model of a physical object, process, or system that dynamically mirrors its real-world performance. The model is continuously fed with data from Internet of Things (IoT) sensors, as well as SCADA, ERP, or MES systems, presenting up-to-date information on equipment status, energy consumption, temperature, flow rates, loads, and many other operational parameters.

Unlike traditional simulations, a digital twin is a “living” system that learns alongside the physical asset, responds to changes, and can predict future events. Thanks to integration with data analytics and artificial intelligence tools, it allows forecasting equipment failures, testing optimization scenarios, and making decisions based on real operational data rather than theoretical design assumptions.

In industrial environments, digital twins can be used at every stage of an asset’s life cycle - from design and commissioning to operation, modernization, and decommissioning. Their implementation can bring measurable results, especially in energy-intensive industries such as chemicals, metallurgy, paper, and food production.

Design and commissioning of installations

During the design phase, a digital twin enables virtual testing of concepts and equipment configurations. Engineers can simulate media flow, heat losses, and energy consumption before the physical installation is built. This shortens implementation time and reduces the risk of design errors.

Monitoring and diagnostics

During operation, the digital twin integrates data from sensors installed on machines, enabling continuous monitoring of their technical condition. The system can detect deviations from normal performance, identify causes of reduced efficiency, and predict potential failures. This allows maintenance work to be scheduled in a way that minimizes downtime and production losses.

Support for production management

Digital twins also enable simulation of process changes - such as introducing a new raw material, adjusting operating temperatures, or changing production modes. This allows plant management to make data-driven decisions and minimize the risk of inefficiencies.

Implementing a digital twin brings a range of operational and strategic benefits for industrial plants. The most significant include:

• improved energy efficiency – detailed data analysis enables better management of electricity and other utilities. The system identifies areas of high energy losses and suggests ways to minimize them,
• reduced operational costs – through predictive maintenance, plants can avoid costly breakdowns, production stoppages, and unnecessary preventive replacements of machines based solely on operating time,
• higher data quality and process transparency – the digital twin consolidates data from various systems, creating a single, reliable source of information about the plant’s condition,
• safe testing of changes – the virtual environment allows simulation of various scenarios without disrupting actual plant operations,
• support for investment decisions – analyses based on the digital model help assess the profitability of planned modernizations and investments in new technologies.

By integrating data from energy management systems (EMS) and building automation, a digital twin enables analysis of real energy flows within a plant. This makes it possible to identify inefficient processes and evaluate the impact of modernization measures - such as motor replacements, inverter installations, or cogeneration implementation.

Digital twins can also create energy maps of a plant, visualizing the flow of energy between its sources and production lines. This helps determine which processes generate the greatest losses and which can be optimized by adjusting equipment schedules or improving waste heat recovery. Importantly, a digital twin can be connected to artificial intelligence algorithms that recommend optimal operating settings.

For a digital twin to function effectively, it must be integrated with the company’s existing IT systems - from sensors and PLC controllers to MES, ERP, and SCADA platforms. This integration enables a comprehensive representation of the enterprise’s processes, supporting report generation and analysis that assist in daily operational decision-making. Managers can continuously track energy consumption indicators, equipment efficiency, and production results across shifts, and respond immediately to any deviations.

Digital twins are one of the cornerstones of the Industry 4.0 concept, which merges automation, data analytics, and process digitization. Combined with IoT, artificial intelligence, and cloud computing, they create an environment where data becomes a key strategic resource. This marks a shift from reactive management to real-time, predictive control based on analytics and process modeling.

When integrated with machine learning algorithms, digital twins can predict how changes in operating parameters will affect energy consumption and production efficiency. Artificial intelligence analyzes historical data and suggests the most effective settings to minimize operational costs and CO₂ emissions.

Digital twins represent a shift in how industrial facilities are managed - from reacting to anomalies after they occur to predicting and preventing them. By precisely replicating processes and providing continuous data access, companies can make decisions based on knowledge rather than intuition. Consequently, the digital twin becomes not only an engineering tool but also a strategic component of organizational development - integrating production, maintenance, and energy management into a single, cohesive information ecosystem.

As industrial digitalization progresses, the importance of such models will continue to grow. In the future, digital twins may become the foundation of autonomous factories, where systems independently analyze data, make decisions, and optimize processes in real time.