वैश्विक खनन ऑपरेटर। कैटरपिलर 777 फ्लीट प्रदर्शन के लिए डेटा साइंस ऑप्टिमाइजेशन।

हमने GPS टेलीमेट्री, निष्क्रिय घटनाओं और ईंधन पैटर्न का विश्लेषण करके कैटरपिलर 777 हॉल-ट्रक बेड़े के प्रदर्शन को अनुकूलित किया। ऑपरेशन को ईंधन भरने वाले स्टेशनों पर अत्यधिक निष्क्रिय समय और गैर-इष्टतम रूटिंग का सामना करना पड़ा। ML-संचालित भविष्यवाणियों, अनुकूलित गति गलियारों और आंशिक ईंधन भरने में बदलाव का उपयोग करके, हमने प्रभावी हॉल चक्रों को 3-7% तक बढ़ाया और प्रति ईंधन भरने की घटना में 15-25 मिनट तक डाउनटाइम कम किया - शून्य पूंजीगत व्यय के साथ।

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