مشغّل تعدين عالمي. تحسين أداء أسطول Caterpillar 777 باستخدام علم البيانات

قمنا بتحسين أداء أسطول من شاحنات النقل من طراز Caterpillar 777 من خلال تحليل بيانات القياس عن بُعد عبر نظام الـ GPS، وحالات التوقف عن العمل، وأنماط استهلاك الوقود. حيث كانت العمليات تعاني من فترات توقف مفرطة في محطات التزود بالوقود ومسارات نقل غير مثالية. وبتطبيق التنبؤات القائمة على التعلم الآلي، وممرات السرعة المحسّنة، والتحول إلى التزود الجزئي بالوقود، تمكنا من زيادة دورات النقل الفعلية بنسبة 3–7% وتقليل أوقات التوقف بمقدار 15–25 دقيقة لكل عملية تزود بالوقود — ودون أي نفقات رأسمالية.

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