Opérateur minier mondial. Optimisation par la science des données de la performance de la flotte Caterpillar 777

Nous avons optimisé la performance d'une flotte de camions de transport Caterpillar 777 en analysant la télémétrie GPS, les événements de ralenti et les profils de consommation de carburant. L'opération souffrait d'un temps de ralenti excessif aux stations de ravitaillement et d'un routage non optimal. Grâce à des prédictions basées sur l'apprentissage automatique, des couloirs de vitesse optimisés et un passage au ravitaillement partiel, nous avons augmenté les cycles de transport effectifs de 3 à 7 % et réduit les temps d'arrêt de 15 à 25 minutes par événement de ravitaillement, sans aucune dépense d'investissement.

Fleet OptimizationTelematicsML & Big Data Analytics

Client

Confidential (UK Mining Group)

Industry

Management Consulting

Date

2025-12-03

Timeline

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01 —The Impact

The results, up front.

Increase in Effective Haul Cycles

Leading to efficacy and efficiency increase

Downtime Reduction per Fueling

25min

With at least 15 minutes reduction per one fueling operation

Annual Financial Impact

$6.2M

The number was within a range of $2M–$6.2M

3–7% increase in effective haul cycles 15–25 minutes less downtime per fueling event Annual savings and productivity impact of $2M–$6.2M Achieved entirely via process and analytics — no CAPEX

02 — The Challenge

Where they started.

The operator’s fleet encountered bottlenecks at refueling points and inefficient routing, leading to long idle times and fewer productive hauling cycles. Telemetry data existed but was not utilized to drive operational optimization. We conducted a full analysis of GPS tracks, speed curves, idle events, fuel levels, and historical cycle times. ML models were used to recommend optimal speed corridors, predict cycle time windows, and forecast fuel demand precisely enough to shift away from full-tank fueling. We also introduced high-pressure fueling complexes and developed a DS Advisor tool to assist dispatchers with real-time routing and fueling decisions. The project delivered a step-change in efficiency, all through process excellence and data-backed decisioning — requiring no new CAPEX.

04 — Approach & Methodology

How we got there.

The data revealed that full-tank fueling drastically increased refueling downtime and that trucks often queued unnecessarily. Predictive fuel-demand modeling enabled partial fueling at optimal times, which significantly decreased wait time and increased cycle availability.

Routing adjustments based on recommended speed corridors further improved predictability and reduced cycle-time variance.

05 —In Practice