The optimization of business processes is essential for enhancing efficiency and competitiveness in today’s dynamic industrial environment. This study presents a novel approach that integrates Artificial Intelligence (AI) with Failure Mode and Effects Analysis (FMEA) to automate risk assessment and enable predictive maintenance. Using real-world data from a glass manufacturing company (SK), we implemented machine learning models—Random Forest and XGBoost—to predict Risk Priority Number (RPN) values and classify failure risks. The Random Forest Regressor achieved an Coeficient of Determination (R²) of 0.999, Mean Absolute Error (MAE) of 2.18, and Root Mean Square Error (RMSE) of 2.65, indicating high accuracy in risk prediction. The Random Forest Classifier reached a classification accuracy of 82.9%, with strong recall (92.4%) for high-risk cases, making it practical for industrial deployment. The proposed AI-enhanced FMEA system supports dynamic risk management, reduces downtime, and improves maintenance planning. Our findings demonstrate that AI can significantly strengthen traditional FMEA practices and serve as a foundation for intelligent, data-driven decision-making in industrial environments.

This study investigates predictive modeling of failure states in manufacturing systems using artificial intelligence in the context of sustainable maintenance and Industry 4.0. The proposed methodological framework is based on historical operational and maintenance data from a single manufacturing device, encompassing multiple process and operational signals such as vibrations, temperature, electric current, and operational logs. The aim is to predict failure within a short-term horizon to support maintenance planning. The article compares Random Forest and XGBoost algorithms at different prediction horizons (8 h and 16 h) to identify the trade-off between classification accuracy and lead time for maintenance planning. Model outputs are analyzed using explainable artificial intelligence and transformed into a risk index compatible with the FMEA methodology. The practical contribution of the proposed approach is illustrated through a scenario-based what-if assessment of potential sustainability impacts, particularly in terms of estimated reductions in unplanned downtime, material waste, and energy consumption. The results point to the potential of integrating AI-supported maintenance as a tool for increasing the reliability and sustainability of manufacturing systems. The novelty of the proposed framework lies in the integration of predictive maintenance, explainable artificial intelligence, replay-based maintenance assessment, dynamic FMEA risk assessment, and sustainability impact quantification into a unified decision-support framework.

From the perspective of the main goal, the real use of agile project methodology was analyzed, and then the levels of knowledge and use of the mentioned methodology were evaluated. Areas for potential improvement were identified and proposals for measures were created to increase the efficiency of future project implementation. Most companies today focus on delivering quality and gaining customer satisfaction and in order to accomplish this, the challenge lies in choosing between traditional development methodologies and agile development methodologies. However, both these approaches have positives and negatives, making the right choice plays a crucial role while starting a new project.

The main objective of the contribution is to improve the selected production process in an electrical engineering company. In order to achieve the main objective, it was necessary to gradually develop the specified partial objectives. The Yamazumi method, which is subsequently used to balance the V03 line or explain the bottleneck in the production process, which in the case of the analysed line is a hydraulic tester. After balancing the line and distributing the workstations among the workers, it was possible to plan and carry out a test of adding a worker to the line.

This paper deals with the selection and comparison of programs that students could use when preparing semester papers in the subject of Operational and Systems Analysis. The analysis consists of a comparison of work in selected software based on selected criteria, such as the availability of programs for students, the language in which the programs are written, the time spent on preparing individual assignments and the correctness of their results. The final part evaluates the benefits of the paper and the selection of the most suitable programs for individual types of tasks.