Continuous industrial material-handling systems are operationally and energy-intensive technological structures in which downtime affecting one equipment group can reduce the availability of the entire production chain. This study develops a probabilistic framework for assessing downtime impacts when detailed historical event-level downtime records are available, but complete technical and economic equipment parameters are missing. The analysis is based on 6605 downtime records for conveyors, excavators and stackers observed between 2017 and 2025. Historical downtime records were combined with interval-based assumptions for power demand, load factor, handling capacity, electricity price and commodity value, and were propagated through a Monte Carlo simulation with 10,000 iterations. The results revealed a strong concentration of downtime burden. The combination of P–Conveyor–Material Collapse accounted for 32.58% of total downtime, while the top five equipment–fault combinations explained 67.86% of cumulative downtime. At the system level, the median modelled energy-service unavailability reached approximately 4339 MWh, the median production-loss equivalent reached approximately 9279 kt, and the median total economic loss was approximately EUR 209.5 million. The proposed Energy–Economic Impact Index integrated event frequency, downtime severity, energy-service unavailability and economic loss into a single maintenance-prioritisation indicator. The highest-ranked maintenance target was P–Conveyor–Material Collapse, confirming that maintenance priorities should be determined by combined operational, energy-related and economic consequences rather than by event frequency alone. The study demonstrates that historical downtime records can be transformed into a probabilistic decision-support tool for risk-based maintenance planning in industrial systems with incomplete technical and economic data.

Operational failures in continuous material-handling systems are usually evaluated through failure counts; however, failure frequency alone may underestimate the true operational burden when downtime severity is unevenly distributed across devices and fault mechanisms. This study develops an integrated statistical framework for analysing operational failures and downtime in a continuous material-handling and technological transport process. The empirical dataset consists of 6605 anonymised failure events recorded between 2017 and 2025, covering 108 monthly observations, three technological device categories, and 42 classified fault types. The methodology combines frequency–severity analysis, inferential testing, time-series forecasting, and cluster-based identification of monthly operating regimes. The results show a strong disproportionality between failure frequency and downtime burden. Conveyor belts accounted for 51.40% of all failures but generated 83.22% of total downtime, confirming their dominant role in system-level operational losses. Several fault types, including Belt Slip, Off-Track Belt, Tear, Motor Failure, and Transfer Chute, also exhibited high downtime severity despite lower occurrence frequency. Inferential testing confirmed statistically significant and operationally meaningful differences in downtime severity across machine categories, whereas the calendar month was not a significant determinant of monthly failure counts or total downtime. Among the candidate forecasting models, Seasonal and Trend decomposition using Loess combined with exponential smoothing (STL-ETS) achieved the best holdout performance for both failure counts and total downtime. Cluster analysis further identified six interpretable monthly operating regimes differing in failure intensity, downtime burden, equipment involvement, fault-type composition, and temporal growth dynamics. The study contributes to downtime-oriented maintenance analytics by demonstrating that operational risk should be assessed through combined frequency–severity and regime-based perspectives rather than through aggregate failure counts alone.

The article presents a simulation model of a conventional and pipe conveyors created in the Tecno-matix Plant Simulation environment. The main objective was to analyse the normal forces, through-put, and energy consumption of both types of conveyors. The model allows for variable input param-eters and experimental comparisons. Four experiments were conducted as part of the research, chang-ing the belt filling coefficient and the design characteristics of the idlers. The results showed that a conventional conveyor transports more material than a pipe conveyor with the same belt width. How-ever, the higher productivity of the conventional conveyor is also associated with higher electricity consumption. Conversely, for the same amount of transported material, the conventional conveyor has slightly lower energy consumption compared to the pipe conveyor. The analysis also confirmed the stability of both systems during long-term simulation. The most significant impact on performance was the filling coefficient, which directly affected the amount of transported material. The results provide practical insights for optimising the design and operation of belt conveyors.