## 5 Discussion
### Proposal for Integration of ML Models into Industrial DSS  
Decision support systems (DSS) are computer systems designed to help managers and users make more informed decisions. They do this by collecting, analyzing, and visualizing complex data, and also enable the simulation of various scenarios, thereby improving the quality and speed of decision-making processes. The practical application of the proposed ML models offers wide possibilities for implementation in the area of monitoring and management of conveyor systems, while specific use depends on the needs of the given enterprise. The created models for predicting the impact resistance of conveyor belts open up wide possibilities for practical application. They can be easily integrated into existing intelligent monitoring and decision support systems in an industrial DSS environment. These systems are crucial for effective predictive maintenance and full utilization of the potential of Industry 4.0 [71; 72; 73; 74; 75; 76]. The integration of ML models into industrial control structures would enable continuous collection and processing of sensor data in real-time. This includes, for example, measurements of impact forces, belt strength, or various operating parameters [74; 75; 77]. Data would typically be collected using standard industrial communication protocols, such as Open-Platform Communications Unified Architecture (OPC UA) or Message-Queuing Telemetry Transport (MQTT), which are designed for robust and efficient information transfer in Industrial Internet of Things (IIoT) environments [78]. Subsequently, data would be pre-processed directly on-site, for example, by noise filtering, normalization, or aggregation, to be ready for trained models. For fast processing and latency minimization, models with low computational demands could run directly on edge devices, such as industrial computers or programmable logic controllers with extended capacity, located near the belts. This is critical for dynamic industrial applications [74; 79]. For more complex analyses or retraining of models, or for advanced functions such as a digital twin of a conveyor belt [28; 77], aggregated data would be transferred to centralized cloud platforms or local data centers. Model outputs--for example, Remaining Useful Life (RUL), risk level of failure, or specific recommendations for maintenance--would be processed by the DSS module. Subsequently, they would serve for automated generation of recommendations for service interventions, early warnings before critical values are exceeded, or optimized maintenance planning [68; 69; 78; 80; 81]. Key to effective management is a clear and immediate presentation of these data. It would be realized through intuitive dashboards, which are part of existing HMI (Human-Machine Interface) or SCADA (Supervisory Control and Data Acquisition) systems, or as specialized web applications. These interfaces, supplemented by alarm systems (e.g., SMS notifications, email alerts, or visual/audible signals), would allow operators to quickly identify risky situations and make informed decisions [72; 74]. Overall, integrating ML solutions into DSS provides operators and managers with objective real-time decision-making bases, leading to more efficient resource utilization and reduced operating costs. Early risk identification thus allows for more precise maintenance planning and a reduction in unplanned downtimes, which fundamentally increases the reliability and effectiveness of conveyor system operation [68; 69; 80]. However, it is important to emphasize that the practical implementation of these advanced systems requires a detailed analysis of specific operating conditions and close cooperation with experts in automation, IT (Information Technology) infrastructure, and maintenance.