Optimizing air conditioning maintenance with QR codes, AI/ML, and efficient ticketing for Bengaluru International Airport.

Technologies Used:

bootstrap

Description:

Bluestar, a prominent player in air conditioning and refrigeration solutions, manages preventive and breakdown maintenance for their equipment at Bengaluru International Airport (BIAL). Face5 was implemented to optimize these maintenance operations, utilizing QR code technology, a robust ticketing system, and AI/ML enhancements.

Users:

  • Technicians
  • Supervisors
  • Branch Level Admin
  • BIAL Team
  • BIAL Admin

Key Solutions Implemented:

  1. QR Code-Based System:
    • Equipment Scanning: Technicians use QR codes to scan equipment, ensuring accurate identification and tracking of maintenance tasks.
    • Activity Logging: QR codes facilitate real-time logging of maintenance activities, ensuring all tasks are recorded accurately.
  3. Preventive and Breakdown Maintenance:
    • Preventive Maintenance: Scheduled regular maintenance tasks to ensure equipment remains in optimal condition, supported by AI-driven predictive maintenance to anticipate potential issues before they arise.
    • Breakdown Maintenance: Efficiently managed and tracked breakdown incidents, minimizing repair time and equipment downtime, with ML algorithms analyzing patterns to prevent future breakdowns.
  4. Ticket Raising and Management:
    • Complaint Resolution: The Airport team, BIAL, raises tickets for any issues or complaints regarding air conditioners, which are promptly handled and resolved by the Bluestar team.
    • Activity Module: Technicians enter daily area temperature readings from all areas of Terminal 2, ensuring optimal climate control.
  5. Comprehensive Reporting:
    • Generated detailed reports for preventive maintenance, breakdown maintenance, and complaints, providing valuable insights into maintenance performance and efficiency.

Outcome:

  • Increased Accuracy: QR code scanning ensured precise tracking and logging of maintenance activities.
  • Enhanced Responsiveness: The ticketing system allowed for quick identification and resolution of maintenance issues.
  • Data-Driven Insights: Detailed reports provided insights into maintenance trends and performance, helping to identify areas for improvement.
  • Predictive Maintenance: AI-driven predictive maintenance has reduced unexpected breakdowns and improved overall equipment reliability.
  • Operational Efficiency: ML algorithms have optimized resource allocation and maintenance scheduling, enhancing overall efficiency.