Industry 4.0, characterized by the integration of advanced technologies, has allowed industries to optimise operations, reduce waste, and minimize environmental impact. Digitalization has become a cornerstone for achieving sustainability goals, and one of the key enablers of Industry 4.0 is the digital twin. The digital twin provides real-time insights and simulations to optimise processes. Organizations can thus optimise not only their operations but also contribute to a more resilient global supply chain. The aim of the paper is to present a comprehensive methodology for implementing the digital twin principle in the Tecnomatix environment. The Tecnomatix model is connected to Siemens PLC SIMATIC S7-1200 utilizing the OPC UA (Unified Architecture) method and through KEPServerEX, V6.14.263.0. The described approach enables continuous monitoring of critical indicators to optimise production efficiency and address downtime issues.

AGV-based intra-company transport systems are indispensable in the manufacturing industry of Industry 4.0. Designing the systems involves determining AGV movement paths that are predefined dynamically or adjusted based on real-time events. This study focuses on the simulation and optimization of an intelligent transport system. The aim is to create a system model with freely moving AGVs controlled based on the requirements of production facilities. The simulation model was designed in the Tecnomatix Plant Simulation environment. A fictional case study with a flexible manufacturing system was used. Specific methods have been developed for AGV operation, control, and dynamic product handling. The initial simulation model served as the basis for optimization. Model optimization, performed using a genetic algorithm, aimed to maximize production volume while minimizing the number of AGVs. Simulation results showed that AGV movements were dynamically adjusted based on real-time machine requests, and the optimal configuration of AGVs achieved a production volume that was significantly higher than the initial setup. This study demonstrates a new approach to modeling AGV traffic systems emphasizing real-time dynamic adjustments of AGV paths. The findings contribute to integrating intelligent transport systems into production processes, and this study provides valuable insights for future investigation in this area.

Digital twin (DT) technology is a key element of Industry 4.0. Despite its rapid development, current research is mainly focused on industrial optimisation and machine-level monitoring. However, its implementation in the educational process lags significantly behind practice. Moreover, existing DT implementations in education often emphasise visualisation or simulation, while neglecting synchronisation and verification of functional equivalence between the physical and virtual systems. This study presents the design, development and experimental verification of a digital twin of a laboratory material handling system. The virtual model created in Tecnomatix Plant Simulation is connected to the physical system controlled by a Siemens PLC SIMATIC S7-1200 and equipped with industrial sensors and an HMI interface. Real-time bidirectional communication is established via the OPC UA protocol using KEPServerEX, ensuring synchronisation between the physical and virtual systems. Experiments confirmed the functional synchronisation of both systems. Additionally, the study presents that DT technology can be adapted for educational purposes and implemented in engineering education.

This paper addresses the optimization of material supply and handling using simulations. The primary objective is to reduce loader downtime by improving raw material procurement and revising the work schedule for material delivery and loader operations. Through the adjustments in the scheduling of raw material deliveries, excessive idle times of handling equipment are minimized. A simulation model is designed to compare the current state with the proposed optimized scenario. The reorganization reduced loader downtime by improving raw material procurement. The simulation results indicate a decrease in loader downtime, from 33% to 13% of total available time. The analysis of storage box occupancy reveals a notable increase in supply efficiency at the outdoor landfill. Adjustments enable the delivery of larger quantities of gypsum within shorter timeframes, maximizing stored material without exceeding storage capacity constraints.

Background: Digital twins (DTs) are a key technology of Industry 4.0 and play a crucial role in the digital transformation of industry. The education of experts for the needs of Industry 4.0 and the emerging 5.0 must align with the requirements of practice. However, the availability of these technologies for educational institutions is problematic due to their complexity and limited resources. Methods: This study develops and implements DT and a virtual monitoring system for a specialised 6-DOF laboratory robotic manipulator. The architecture uses a multi-layered communication via middleware KEPServerEX. AI-assisted “Vibe Coding” supports development of a custom Python control application and integration with multiple APIs. Next, the application of a virtual monitoring system enables tracking of selected metrics via a web interface. Results: Our work provides three primary contributions: (1) consolidation of multiple devices through middleware (KEPServerEX) to link robots and PLCs; (2) accelerated development through AI-assisted “Vibe Coding”; and (3) remote accessibility through a custom web-based monitoring platform. Conclusions: The study demonstrates that a fully functional digital twin, integrated with a virtual monitoring system, can be implemented in a university laboratory environment using a combination of cost-effective technologies.