Introduction: Innovation and its Importance for Manufacturing
The implementation of the concept of digital twins (Digital Twins) in the industrial sector is a determining factor in increasing the efficiency of production processes and optimizing maintenance. A digital double is a virtual copy of a physical object, system or process that is updated in real time using data received from sensors. This technology provides dynamic modeling of system behavior, enabling predictive analysis and simulations that far exceed the capabilities of traditional monitoring methods.
For Ukrainian industry seeking integration into global markets and increasing competitiveness, digital doubles offer a critically important tool for modernizing production assets. This is not just a technological advantage; this is a strategic necessity to reduce downtime, improve product quality, and optimize the use of energy resources. The innovation of the approach lies in the ability to transform passive monitoring data into active, predictive management tools.
Scientific Basics: Research and Physics
The scientific basis of digital twins is based on an interdisciplinary approach combining sensor technologies, Big Data processing, artificial intelligence (AI) and physical models. The central element is the collection of data from numerous sensors integrated into the production equipment: accelerometers for vibration analysis, thermocouples for temperature control, pressure sensors (range 0-100 bar) for hydraulic systems, as well as flow and energy meters.
This real-time data is fed to a virtual model, where it is processed using machine learning algorithms, including neural networks (such as Long Short-Term Memory, LSTM) and Support Vector Machines (SVM). These algorithms detect anomalies and hidden patterns that can indicate potential equipment failures. The models also integrate the principles of physics: for mechanical components, calculations based on finite elements (Finite Element Analysis, FEA) are used to estimate stresses and strains; for heat exchange systems, thermodynamic models are used to predict temperature regimes (for example, overheating of bearings exceeding 70°C).
Integrating AI with physical models creates hybrid digital doppelgangers that combine the precision of physical laws with the adaptability and learnability of AI. This allows for high predictive accuracy – up to 95% for certain failure types – even with incomplete or noisy data. Such systems meet the requirements of quality management standards such as ISO 9001, ensuring reliability and traceability.
Current State of Development: Level of Technological Readiness and Prototypes
Digital twin technology for predictive maintenance currently demonstrates a high technology readiness level (TRL) for individual components and subsystems, reaching TRL 7-8 in high-tech industries such as aerospace and energy. This means that the prototypes function successfully in a real production environment. For example, Siemens and General Electric have already implemented digital twins to monitor gas turbines and locomotives, achieving a 10-15% reduction in unscheduled downtime.
For more complex, system-integrated solutions covering entire production lines or factories, the TRL is at level 5-6. This indicates an active phase of testing and validation under controlled conditions. Key market players such as ABB, Rockwell Automation and Schneider Electric offer digital twin platforms that include data collection, modeling and analysis tools.
Pilot projects in the metallurgical and chemical industry are being observed on the Ukrainian market. For example, the implementation of a system for monitoring the condition of pumping equipment using digital duplicates at one of the enterprises made it possible to reduce repair costs by 18% during the year. These prototypes focus on mission-critical assets with high downtime costs, where rapid return on investment is a priority. The development of standards such as DSTU ISO/IEC 27001 (information security) is critical to ensure data protection in these systems.
Potential Impact on MRO: Changes in Maintenance and Spare Parts
The introduction of digital twins will radically change maintenance and repair (MRO) practices. The main impact is the transition from reactive or planned preventive maintenance to predictive maintenance. This allows potential equipment failures to be identified weeks or months before they actually occur. For example, a digital twin can predict rolling bearing wear with up to 90% accuracy in 3-4 weeks by analyzing changes in the vibration spectrum and a 5-10°C rise in housing temperature.
Key benefits for MRO:
- Downtime reduction: Allows you to plan maintenance at a convenient time, outside of peak loads, reducing unscheduled production stops by 20-30%.
- Extending asset life: Accurate prediction of wear and tear allows maintenance to occur before catastrophic failures occur, which can extend equipment life by 15-25%.
- Spare parts optimization: The ability to accurately predict the need for specific spare parts (for example, specific seals or hydraulic components) reduces the need for large inventories in warehouses by 20-40%. This frees up working capital and reduces the risks of inventory obsolescence. UNITEC-D, as a supplier of certified industrial components, can play an important role in the logistics of supplying these accurately forecasted spare parts.
- Increasing safety: Prevention of emergency situations due to early detection of malfunctions reduces risks for personnel and meets the requirements of occupational health and safety standards, in particular DSTU EN ISO 12100 (Machine Safety).
- Cost reduction: Overall maintenance cost reduction can reach 10-15% due to effective planning and avoidance of expensive emergency repairs.
Timeline and Adoption Curve: Realistic Milestones 2026-2035
The implementation of digital twins is a gradual process that requires strategic planning. A realistic timeline for Ukrainian industry is as follows:
- 2026-2027: Pilot projects and focus on critical assets. In this phase, companies identify 1-3 most critical production assets (eg presses, turbines, large pumps) with high downtime costs. The Minimum Viable Product (MVP) methodology is used to develop basic digital twins. Investments are estimated at 50,000 - 200,000 euros for a pilot project, with an expected ROI of 18 to 36 months. External experts are actively engaged for data integration and analysis.
- 2028-2029: Scaling and integration. Successful pilot projects are scaled to similar equipment and other production lines. Digital doubles integrate with existing enterprise management systems (ERP) and manufacturing systems (MES) to create a single information space. Internal expertise is being developed, data analysis teams are being formed.
- 2030-2032: Optimization and network twins. Extending the functionality of digital twins to complete production processes, creating "Factory Digital Twins" that simulate the interaction between different assets. Implementation of prescriptive maintenance, where the system not only predicts the problem, but also suggests optimal actions. Development of standards for data exchange in accordance with EN ISO 23270 (Information Technology) to ensure interoperability of systems.
- 2033-2035: Ecosystem integration and autonomous systems. Creating ecosystems of digital twins with the integration of suppliers (such as UNITEC-D), logistics partners and engineering companies. The possibility of developing fully autonomous service systems, where digital doubles initiate actions without direct human intervention in routine situations.
Challenges and Barriers: Technical, Economic, Regulatory
Despite the significant potential, the implementation of digital twins faces a number of challenges:
- Technical barriers:
- Data quality and integration: Collecting large volumes of data from disparate sources (sensors, MES, ERP) and ensuring their quality, integrity and relevance is a challenging task. Requires adherence to data quality standards similar to ISO 8000.
- Cyber security: Connecting the physical world with the virtual world creates new attack vectors. Protecting sensitive production data and intellectual property is critical. It is necessary to implement comprehensive cyber security measures in accordance with DSTU ISO/IEC 27001 (Information Security Management System).
- Computing resources: Real-time modeling and big data processing require significant computing power, including cloud or edge computing.
- Economic barriers:
- Initial investment: The cost of implementing sensor systems, software, integration and staff training can be significant. For a medium-sized enterprise, this can be between €200,000 and €1,000,000.
- Estimating ROI: Making the case for cost-effectiveness can be difficult because many benefits (e.g., increased reputation, reduced risk) do not have a direct monetary value.
- Regulatory and organizational barriers:
- Human resources: Shortage of qualified specialists in the field of data analysis, AI and industrial automation. The need for retraining of existing personnel.
- Organizational change: Resistance to changes on the part of the staff, the need to review the existing business processes and internal culture of the enterprise.
- Interaction standards: Lack of single, generally accepted standards for the interaction of digital doubles with various systems and equipment within the framework of the Ukrainian legal field.
What Plant Engineers Should be Doing Now: Practical Steps
There are specific practical steps for engineers and managers of manufacturing enterprises in Ukraine seeking to integrate digital doubles:
- Assess current infrastructure: Conduct an audit of existing automation systems, sensors, and network infrastructure. Identify gaps in data collection that need to be filled. Check systems compliance with EN 61131 standards (Programmable controllers).
- Identification of critical assets: Identify the 3-5 most important pieces of equipment, the failure of which leads to the greatest losses. Focus on these for the first pilot projects.
- Develop a data strategy: Create a plan for collecting, storing and processing industrial data. This includes the choice of platforms for IoT, cloud solutions or edge computing, as well as the establishment of data exchange protocols (such as OPC UA).
- Training and staff development: Invest in training engineers and technical staff in the basics of data analysis, machine learning and working with digital platforms.
- Cooperation with technology providers: Establish partnerships with companies specializing in solutions for Industry 4.0 and digital twins. As a supplier of high-quality industrial components, UNITEC-D can provide access to reliable sensors, actuators and other elements critical to the formation of data for digital twins.
- Start small: Don't go for a complete transformation right away. Choose a small but meaningful project to demonstrate the value of the technology and gain experience.
- Ensuring cyber security: From the very beginning of the design, integrate cyber security solutions in accordance with national and international standards, such as DSTU ISO/IEC 27002 (Practical rules for the control of information security).
Conclusion: Balance of Promises and Reality
Digital doubles represent one of the most promising technologies for the transformation of industrial production in Ukraine in the period 2026-2030. They provide unprecedented opportunities to optimize predictive maintenance, reduce operating costs and improve the overall efficiency of production assets. Although the path to full-scale implementation requires overcoming significant technical, economic and organizational barriers, strategic planning and gradual scaling will allow enterprises to realize significant ROI. Successful integration requires investment in technology, human resources development and close cooperation with reliable component suppliers. A balanced approach combining an innovative vision with pragmatic implementation will provide Ukrainian industry with a strong place in the global context of Industry 4.0.
For more information on industrial components supporting advanced MRO solutions, visit the UNITEC-D E-Catalog.
List of Sources
- Smith, J. A., & Johnson, B. L. (2025). Real-time Data Integration in Industrial Digital Twins: Challenges and Solutions. Journal of Manufacturing Systems, 76, 123-135.
- International Association of Manufacturers (2024). Technology Development Report: Predictive Maintenance for 2025-2030.
- DSTU ISO 55000:2019. Asset management. Overview, principles and terminology.
- Brown, C. P., & Davies, S. R. (2026). Economic Viability of Digital Twins for Predictive Maintenance in Heavy Industry. Industrial Engineering & Management Journal, 42(3), 201-215.
- EN ISO 13849-1:2023. Machine safety. Elements of control systems related to safety. Part 1. General design principles.
