This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises.

Modelling and simulation of production and logistics concepts and processes makes sense primarily in terms of the possibilities of variability in terms of parameter settings and environment, which allows you to fine-tune and optimize prototypes in the virtual world before their physical implementation and will be reflected in increasing the quality of the system. Although there are many positive aspects to adopting digitalization in the sense of Industry 4.0 and 5.0, it is necessary to consider several technological, software and other challenges that companies have to face during implementation. The article deals with the issue of using modern software tools that help companies optimize production and logistics flows in the Testbed 4.0 experimental workplace environment. Testbed 4.0 was designed as a trial test line, or rather. As a complex set of equipment, equipped with the most modern equipment and technology. Thanks to these conditions, it is possible to design, verify, evaluate products, processes and technologies of digitalization.

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber-physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications.

This study presents a comprehensive simulation-driven approach to identify and mitigate bottlenecks in the supply flows of an automotive manufacturing system. The supply process relies on a Kanban-based assembly line replenishment, supported by multiple material transports per shift using tugger trains. To analyze flow constraints, the research combines time–motion studies with data from a Real-Time Locating System (RTLS), enabling spatial-temporal mapping of material movements. Based on this data, digital-twin simulation models of the supply process were developed and validated. These models allowed testing of improvements aimed at increasing throughput and efficiency. The proposed methodology demonstrates effective bottleneck resolution and provides a structured framework for data collection and simulation integration. Unlike traditional observation-based approaches, the use of RTLS enables precise detection of routing deviations and operator behaviors. The simulation model was tested under real operating conditions and validated against ground-truth data. Results showed a reduction in collisions and delivery delays, as well as measurable productivity and financial gains. This approach offers a practical and transferable method for optimizing intralogistics in modern production environments.

This paper addresses the design of production expansion by using simulation to optimize production and storage capacities. The primary objective is to apply the Siemens Tecnomatix Plant Simulation software version 2404 to analyze the current production process, identify potential bottlenecks, and propose improvements. The methodology involves the use of simulation to create digital models of the production process, allowing for the detection of issues and evaluation of alternative solutions without interfering with ongoing production. The novelty of this approach lies in its ability to test and optimize the production flow digitally, ensuring a smoother transition to expanded capacities. Several alternative scenarios for production expansion were developed using Siemens Tecnomatix Plant Simulation, with a focus on verifying production volumes, identifying bottlenecks, and proposing the necessary machines and technological equipment. The results of the simulations are used to assess the proposed improvements, while the layout of the process is optimized for efficient material flow. The paper also presents 3D models as part of the solution proposal. The findings demonstrate the effectiveness of simulation-based design for expanding production systems, offering a methodology that can be applied to similar manufacturing processes.

The main goal of this article is to identify the relationship between designing a layout and developing an efficient material flow for a new production facility. The article begins with a brief overview of process development and optimization in industrial and logistical environments. It then highlights the significant impact that an effective layout and material flow can have on overall production efficiency. The following sections offer a detailed review of existing literature and theoretical models that support the connection between facility layout design and material flow effectiveness. The article stresses the importance of incorporating modern technologies and methods during the planning phase to boost productivity and minimize waste. In the practical section, the article thoroughly examines the development of the layout solution and provides an in-depth analysis of the material flow, focusing on a specific product. This includes a detailed evaluation of various layout designs and their effects on material handling, storage, and transportation within the facility. The proposed layout and material flow were validated using TX Plant Simulation software, which generated statistical reports and outputs. This software allowed for the modeling and simulation of different scenarios, offering insights into potential bottlenecks and areas for improvement. The simulation results are discussed in detail, highlighting key findings and their implications for the production facility.

If an organization implements lean manufacturing without concurrently considering ergonomic requirements, the anticipated outcome, such as increased production productivity, may not be realized. Several authors have emphasized the significant potential for synergies resultingromf the successful integration of lean manufacturing and ergonomics. The objective of this paper is to exemplify the application of lean manufacturing tools in enhancinghetproductivity of casting aluminium alloys while incorporating ergonomic considerations. The results of the assessmentf oworking positions through the CERAA application before intervention indicate a potential risk of increased physical strain on operator. Utilizing a hybrid research design, we conducted a singular case study that delves into the industrial implementation of a robotic cell in the productioline for manufacturing battery covers for electric/hybrid vehicles. The results of the assessment of working positions after he implementation of robotic technology do not indcate a potential risk of increased physical load on the operator. isThcase study demonstrates the integration of ergonomics and lean manufacturing principles in practice.

The article focuses on the issue of streamlining logistics processes by applying selected VSM and kanban lean tools, which are also of great importance in the current digital age, especially from the point of view of finding the potential for eliminating processes and activities that do not add value. Unlike traditional supply systems, the new dynamic approach to inventory management is that it considers unique procurement methods, unique demand, and product flows through the manufacturing process. The mentioned approach aims to define the optimal amount of stock, which can ensure the required level of supply service and, at the same time the efficiency of material flows in production and assembly and reflect on fluctuations in demand In the analysis and verification of outputs, methods of lean production, VSM, OEE monitoring, and kanban were used. An object-oriented approach to business process modelling using ARIS software was used for process algorithmizing.

This paper discusses the use of RTLS (Real-Time Location System) technology to track and optimize material flows in the Prototyping and Innovation Center. The objective was to develop and implement a digital tracking sheet that allows accurate real-time monitoring of material movement. For this purpose, an RTLS network was experimentally built in the centre to provide continuous monitoring of selected production processes from the input of the semi-finished product to the completion of the final product. As part of the testing, various elements of production were monitored, including material movement, logistics operations and pallet truck handling. The RTLS software enabled detailed analysis of material flow using spaghetti maps, heatmaps and zone maps, which provided valuable data on movement trajectories and identified bottlenecks in production. The analysis revealed the problem of long delays in the manual grinding area after machining on the mill, indicating the need to optimize this process. The results confirm that the implementation of an RTLS system contributes to a more efficient management of production operations, minimizing downtime and improving material flow continuity. In addition, the system enables better production planning and provides data for further optimization of logistics processes within industrial production.

Within the framework of Industry 4.0, the transition from stand-alone systems to an interconnected digital enterprise plays a crucial role. Increasing demands for flexibility, quality, and shorter product development cycles drive the adoption of advanced digital solutions such as product data management (PDM) systems, manufacturing execution systems (MES), advanced planning and scheduling (APS) tools, and the digital twin (DT) concept. At the same time, digitalisation trends (e.g., Smart Factory) reveal critical weaknesses of manufacturing companies, with insufficient planning and scheduling capabilities being one of the most common issues. While medium- and long-term planning is typically handled within ERP systems, operational scheduling is often still managed through inflexible spreadsheet-based solutions. APS systems are therefore gaining importance as effective tools with relatively fast return on investment, although their deployment requires alignment between ERP and MES layers as well as a thorough understanding of business and production processes. The combined implementation of PDM, APS, MES, and DT enhances decision-making, enables rapid adaptability to change, and supports the development of learning organisations. The article focuses on key principles and technologies of digital transformation with emphasis on data integration using selected tools.

The presented article deals with the application of tools to calculate the Master Production Schedule (MPS) in the production of beverages (consumer packed goods) and its impact on the inventory profile in finite or infinite capacity. The calculation of MPS can be performed at the level of an ERP solution or with dedicated standalone planning software. The first part of the manuscript defines MPS calculations, its position in the planning hierarchy and defines reason of importance having MPS calculations in the consumer product industry. The second part of the article analyses sample data of beverage production, calculates MPS for each item and compares the results of finite/infinite capacity utilization. In the last part of the manuscript, authors discuss if the challenges described in introduction part of the article can be addressed with specific planning solution to calculate MPS.

The innovative pressure device, developed to address contamination issues on Essity Slovakia’s carton production line, was successfully implemented using 3D printing technology. This approach resulted in a precise prototype that significantly reduces contamination, simplifies packaging procedures, and lessens the need for manual labor. The project entailed a comprehensive review of the current system, 3D scanning, creation of a model using SolidWorks software, and fabrication with a Trilab DeltiQ 2 printer. The outcomes demonstrate a staggering 96% decrease in contamination, elimination of downtime, and a boost in overall line efficiency. This research underscores the transformative capabilities of additive manufacturing in industrial modernization and accentuates the significance of technological advancements in enhancing efficiency, sustainability, and quality within the manufacturing industry.

Ergonomics is a crucial element in industrial engineering, contributing to the optimization of production processes while ensuring safe, sustainable, and efficient working conditions. This study focuses on simulation-supported ergonomic improvements within the production system of a selected manufacturing company. The main objective was to identify ergonomic deficiencies and propose solutions to reduce workers' physical strain. A digital model of the existing workstation was created using Tecnomatix Process Simulate software, enabling detailed analysis of worker movements and postures in a virtual environment. The RULA (Rapid Upper Limb Assessment) method was applied to assess postural load before and after implementing proposed improvements. The key intervention involved introducing spring-loaded carts to reduce frequent bending and lifting during material handling. Simulation results showed a significant decrease in the risk of musculoskeletal disorders and improvement in worker posture. The study demonstrates that integrating simulation technologies with ergonomic analysis is an effective approach to enhancing workplace conditions. This method emphasizes the value of incorporating ergonomic design early in workstation planning to improve both safety and production efficiency.

Effective warehouse management plays a key role in optimising supply chain operations and ensuring on-time delivery of goods. As logistics systems become increasingly complex, the need for data-driven approaches and advanced planning tools becomes essential. This work focuses on integrating input data processing and simulation-based modelling for warehouse logistics planning using TX Plant Simulation, a powerful tool for modelling, simulating, and optimising discrete-event logistics systems. The presented article aims to show how accurate input data processing combined with simulation support can contribute to more efficient warehouse layout, improved material flow, and resource optimisation. In our case, this involves the creation and testing of a simulation model of a new warehouse. The first phase involves the collection and analysis of real or realistically generated input data related to warehouse operations, such as inbound and outbound flows, order picking strategies, storage methods, transport routes, and resource utilisation. The data required for the analysis were processed based on data available from previous periods from warehouse management within the original warehouse. This data thus formed a relevant input base for creating a simulation model that replicates real warehouse movements. This data is then cleaned, structured, and prepared for use in a simulation environment. Using TX Plant Simulation, a digital twin of the warehouse system is created to test different planning scenarios. Simulation experiments provide valuable insights into the impact of layout configurations, planning strategies, and process improvements. In addition, simulation allows for the safe testing of optimisation strategies without disrupting real operations. The results highlight the importance of high-quality input data and proper model calibration for reliable simulation results. The simulation findings support decision-making processes in warehouse planning and help identify areas for cost reduction, capacity improvement, and increased operational flexibility. The integration of data analysis and simulation tools proves to be an effective approach to solving real-world challenges in warehouse management. This study confirms the potential of TX Plant Simulation as a decision-support tool in logistics engineering and highlights the value of data-driven planning in the context of modern warehouse systems. The proposed methodology can be applied for academic research and industrial practice for strategic and operational planning of warehouse processes.

Nowadays, when the variability of markets and the complexity of products are growing rapidly, their life cycles are shortening, and the influence of global supply chains is increasing, companies must reflect on these changes and, therefore, strive to be more flexible, cheaper and faster. Industry 4.0 provides solutions and ways for companies to cope with these challenges. Digital connectivity will improve efficiency and accelerate innovation, introducing new business models that can be implemented much faster. The paper focuses on the ​​applicability of simulation software as an effective Industry 4.0 tool in the area of ​​real conditions of existing production. The company under study is planning a significant expansion of production. Still, before physically interfering with the existing production, it would like to verify the effects of the selected type of innovation. To create a reliable simulation model, it is necessary to map the current production process and collect relevant data. Subsequently, it is necessary to create a digital model of current production and gradually expand it with new factors that the company plans to implement in the future.

This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. Didactic robotic platforms, such as the Wlkata Mirobot, offer students hands-on opportunities to develop these abilities in a practical and interdisciplinary context. When combined with technologies like digital twins, the Internet of Things, and simulation tools, educational robotics fosters both technical proficiency and adaptability to evolving industrial demands. The presented case study demonstrates the design, construction, and experimental setup of a functional laboratory mini-line using the Wlkata Mirobot. The focus is placed on layout design, robot programming, and simulation-based process optimization to reflect real industrial processes. This study also presents student feedback and performance indicators from repeated trials to illustrate the educational and operational potential of the solution.

The article deals with the issue of creating aninformation system through an object-oriented approach, which is suitable for ensuring an efficient and properlyfunctioning logistics network. An object-orientedapproach to the creation of a business information system pays attentionatoset of cooperating objects and reacts more flexibly to events in the environment. The main effort within the object-orientd approach is the reuse of created objects fornewa system, which significantly contributes to shortening the development of new systems. For this fact and based onpractical experience, the mentioned approach was chosen by the authorstheof article. The overall design and functionalityofthe information system were influenced by the strategic directionftheocompany, which was also necessary to considerwhen preparing the article. In the end, a comparison of specificndaobject-oriented approaches to the creation ofinformation systems for the company is processed.

The implementation of lean manufacturing principles into both the manufacturing and non-manufacturing sectors makes it possible to achieve a high level of efficiency in many indicators. The presented article deals with the influence of lean production on the methodology of measuring the performance of enterprises in the non-production sphere. Part of the methodology is the analysis of key performance indicators, which serve to calculate the main quantities related to the efficiency of the operation and its optimization. In connection with the definition of different types of losses that have been identified and are successfully reviewed in the production sphere, the most common causes of losses and waste that can occur in the non-production sphere have been defined. Even though the principles of lean production were primarily developed for the production sphere, in the presented article it is possible to see the positive influence of lean principles in the non-production sphere. Customers expect a quick transformation of their requirements into products in the form of a product or service with a certain degree of personalization. Businesses focused on the non-manufacturing sector meet potential customers who are largely active and personalize products for which they are willing to pay. The path of implementing innovative solutions is one of those paths that contributes to positive change and creates prerequisites for moving towards the so-called smart business also in the non-production sphere.

The presented article deals with the issue of inventory management optimization through modelling and simulation in the software Tecnomatix Plant Simulation. Based on an in-depth analysis of the supply chain with an emphasis on inventory management, a simulation model was developed. It was optimized using the inventory theory methodology in combination with a discrete-event simulation approach. Dynamic simulation made it possible to perform experiments and optimize the existing state. The solution to the problem is elaborated on a case study to achieve the efficiency of the supply process of a specific operation of a clothing retail chain, which is struggling with the problems of excessive and inaccurate supply.