Advanced Fleet Management

This case study explains how a leading construction enterprise adopted advanced fleet management to gain centralized visibility over cranes, trucks, and tower cranes across multiple Indian sites. The client needed a digital solution to replace fragmented logs and manual tracking with real-time telemetry, enabling better utilization, reduced idle time, and a foundation for predictive maintenance.
Client
Manufacturer
Platform
Android, Web
Industry
Manufacturing
Technology
Angular

90%

Improvement Fleet Management

100%

Data Accuracy Improved

90%

Operational Efficiency Improved

100%

Process Transparency Enhanced

About

A multi-site construction firm operating across India with a high-value fleet of heavy equipment (cranes, haul trucks, tower cranes). The organization required an advanced fleet management approach to standardize monitoring, consolidate data across projects, and protect capital investments through smarter operations.

Business Challenges

  • Scattered asset monitoring: Site-level records prevented a consolidated view necessary for effective advanced fleet management.
  • Under-utilization & idle time: High-value equipment remained idle because planners lacked visibility across sites for redeployment under an advanced fleet management strategy.
  • Manual tracking & errors: Paper logbooks and delayed reports undermined the accuracy needed for advanced fleet management decisions.
  • Maintenance blind spots: No real-time performance telemetry led to unplanned downtime and higher maintenance costs—a gap advanced fleet management must close.
  • Operational inefficiency: Without utilization analytics, allocation and scheduling decisions could not be optimized through advanced fleet management practices.

    • Solution

    To enable advanced fleet management, we implemented a layered digital solution:

    • IoT integration with equipment: Fitted telematics and IoT sensors to cranes, trucks, and tower cranes to capture engine runtime, load, idle periods, and movement.
    • Azure IoT Hub for data collection: Securely ingested and processed telemetry from distributed sites to support an enterprise-scale advanced fleet management platform.
    • Centralized dashboard: Built an interactive web dashboard showing site-wise utilization, idle vs active time, and actionable fleet summaries to support advanced fleet management decisions.
    • Analytics & reports: Created utilization, movement, and downtime reports to reveal patterns critical for advanced fleet management.
    • Alerts & notifications: Configured real-time alerts for prolonged idle times, abnormal usage, and maintenance thresholds to enable timely action under advanced fleet management.

    Our Approach

    1
    Discovery & Strategy
    Conduct in-depth analysis and identified key inefficiencies.
    2
    Tech Implementation
    Integrated AI-powered tools to steer development activities.
    3
    Deployment & Support
    Launched the solution and provided continuous support.

    Our Steps

    1
    Assess
    Assess asset inventory, telemetry readiness, and connectivity at each site. Map key KPIs for advanced fleet management (utilization, idle time, runtime, maintenance thresholds) and prioritize devices (cranes, trucks, tower cranes) for pilot instrumentation.
    2
    Deploy
    Deploy IoT sensors and telematics, onboard devices to Azure IoT Hub, and roll out the centralized dashboard. Configure telemetry schemas, alerts, and basic analytics to realize immediate gains from advanced fleet management.
    3
    Optimize
    Use analytics and operational feedback to refine thresholds, build utilization reports, and enable periodic redeployment plans. Expand toward predictive maintenance models and automation to continuously mature advanced fleet management capabilities.

    Outcome

    The deployment of advanced fleet management delivered clear operational benefits:

    • Improved asset utilization — data-driven redeployment reduced idle windows and increased effective uptime.
    • Centralized visibility — leadership gained a single pane of glass for fleet performance across all sites.
    • Reduced operating costs — better scheduling and proactive maintenance lowered operating and unplanned repair costs.
    • Enhanced decision-making — dashboards and reports enabled scheduling, capacity planning, and cross-site allocation.
    • Foundation for predictive maintenance — collected IoT telemetry establishes the dataset required to evolve advanced fleet management into AI-driven predictive analytics.
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