Frequency–Severity Asymmetry and Regime-Based Forecasting of Operational Downtime in Continuous Material-Handling Systems

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.