AI-Powered Predictive Analytics for Fuel Efficiency & Operations

    About iRise

    Founded in 2004, iRISE is a trusted provider of construction, equipment, and mining solutions. With over 750 equipment units, skilled manpower, and ISO certifications, They deliver reliable services, ensuring client success in diverse industries.

    Industry

    Construction, Equipment Rentals, Mining Solutions, and Engineering Services.

    The Problem

    iRise operates large construction machinery, including cranes and rollers, with fuel costs being a major operational expense. However, tracking actual fuel usage vs. pilferage was nearly impossible. The company needed an intelligent system to monitor fuel input, consumption, and machinery efficiency in real time.

    The Solution

    AnySource Data Combiner

    Integrated fuel level sensors, GPS trackers, and machine operation logs, providing real time insights into

    • Fuel consumption per hour (CPH) & fuel efficiency (L/hr, km/L)
    • Total run-time vs. idle-time tracking
    • Refueling & de-fueling event detection
    • Overspeeding, unauthorized detours, and geo-fencing breaches

    Data Reason Builder

    Leveraged Machine Learning models to

    • Predict daily/monthly fuel requirements based on historical usage patterns.
    • Correlate fuel efficiency with equipment make, model, and operational conditions for precise fuel forecasting.
    • Detect anomalies in fuel usage, flagging pilferage and leakages for immediate action.

    A custom-built AI-driven analytics dashboard provided iRise with data-backed insights, enabling strategic decision-making for cost control and operations planning.

    The Result

    •  Fuel pilferage reduced
      Pinpointed fuel loss incidents by location and timeframe, enabling targeted security measures.
    • Optimized fleet efficiency
      AI-based insights ensured better fuel allocation across operations, preventing overuse and underutilization.
    • Operational cost savings
      The system’s accuracy enabled iRise to cut unnecessary fuel expenses, achieving ROI in under six months.
    • Smarter equipment management
      Predictive analytics provided insights into maintenance schedules, improving equipment uptime.
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