IQR-based statistical validation of material transport cycles in open-pit mining

Fleet Management Systems (FMS) record material transport cycles in open-pit mines through event-log entries combining manual and automated state transitions. These data support key performance indicator (KPIs) and fleet-sizing decisions, but contain outliers arising from logging errors, state-recognition failures, incomplete records, shift changes and operational inconsistencies. This article proposes a weighted interquartile range–based validation framework with differentiated Tukey multipliers ( k = 1.5; 2.0; 2.5) per cycle stage and a four-layer hierarchical scoring system. The method was applied to 580,224 cycles recorded in 2025 at an open-pit mine, covering 12 months of FMS data. The framework validated 93.0% of the cycles and rejected 7.0% as outliers, producing a corrected mean-cycle-time estimate of 22.82 min instead of the raw 24.21 min (−5.7% relative to the raw mean). In the validated set, travel, fixed operations and waiting represented 73.8%, 15.1% and 11.1% of the cycle, respectively. Mean haul distance showed a strong linear relationship with mean cycle time across mining fronts ( R 2 = 0.81). The framework provides a reproducible procedure for cleaning FMS data before KPI reporting and artificial intelligence (AI)-based optimization.