Truck Monitoring and Compliance System at Yunus Textile Mills Limited

Yunus Textile Mills Limited (YTML), a leader in textile manufacturing, faced challenges in truck monitoring, maintaining compliance with Standard Operating Procedures (SOPs)

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About YTML

Yunus Textile Mills Limited (YTML), a leader in textile manufacturing, faced challenges in truck monitoring, maintaining compliance with Standard Operating Procedures (SOPs), and enhancing safety within their facilities. Manual tracking methods often led to inefficiencies and potential safety risks.

To tackle these issues, Yunus Textile Mills Limited collaborated with The Disrupt Labs to implement an AI-powered Truck Monitoring System. This solution provided real-time tracking, validated vehicle operations at the weighbridge, and monitored restricted zones for unauthorized activities, revolutionizing their operational efficiency and safety standards.

The Challenge

YTML encountered several critical challenges in their truck monitoring processes:

01.

Weighbridge Compliance

Ensuring all vehicles pass through the weighbridge is essential for both entry and exit, guaranteeing accurate tracking and compliance at every stage.

02.

Vehicle Entrance Monitoring

Keeping track of vehicles entering the facility to ensure they complied with weighing procedures.

03.

Unauthorized Exit Detection

Monitoring vehicle movements during restricted hours, including after-hours, weekends, and holidays.

04.

No-Go Area Monitoring

Detecting unauthorized personnel or vehicles in restricted zones, such as the scrap area.

05.

Real-Time Vehicle Validation

Verifying truck details and number plates at both entry and exit points to maintain compliance.

06.

Database Integration

Synchronizing data from the weighbridge and gate pass records to ensure seamless validation and data accuracy

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“The AI-based PPE Monitoring System from The Disrupt Labs has transformed our approach to workplace safety. Automating PPE monitoring has minimized human error, brought compliance levels to over 95%, and saved us countless hours previously spent on manual tracking. The system’s intuitive dashboard and automated reporting allow our management team to make informed, data-driven decisions, significantly boosting both safety and operational efficiency. It has truly set a new benchmark for safety at our facility.”
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M/S Axens
Representative

The Solution

The Disrupt Labs developed an AI-powered Truck Monitoring System specifically designed to solve YTML’s challenges. This system combined advanced surveillance technology with smart data management to streamline operations.

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Weighbridge Monitoring

Cameras track vehicle movements at the weighbridge and clearway areas. The system generates alerts if a vehicle bypasses the weighbridge, ensuring strict adherence to SOPs.

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Vehicle Entrance Monitoring

Surveillance cameras monitor vehicles as they approach the weighbridge. The system validates number plates and triggers alerts if a vehicle skips the weighing process.

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Vehicle Exit Monitoring

Cameras track vehicle movements during entry and exit, generating alerts for unauthorized exits during restricted hours.

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Gate 2 Monitoring

Cameras monitor the scrap area to detect unauthorized personnel. The system validates the number plates of vehicles at Gate 2 in real-time to prevent unauthorized access.

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Database Integration

Centralized data storage using SQL and Oracle databases ensures real-time access to truck weight records and gate pass details. The system integrates data for accurate validation and compliance monitoring.

The Outcome

The implementation of the AI-powered Truck Monitoring System brought significant improvements to YTML’s operations:

The system ensured strict adherence to weighbridge SOPs, reducing violations and improving overall compliance.

Unauthorized vehicle and personnel movements in restricted zones were quickly identified and addressed, enhancing safety across the facility.

Automated monitoring and validation processes minimized manual intervention, streamlining truck operations and improving efficiency.

Centralized databases provided real-time access to truck records and gate pass information, ensuring transparency, accountability, and easy retrieval for audits.

Conclusion

The AI-based truck monitoring and compliance system at YTML has established a new standard for operational efficiency and safety in the textile manufacturing industry. By effectively tackling challenges like weighbridge compliance, unauthorized exits, and restricted zone monitoring, the system has significantly improved safety, compliance, and data accuracy. YTML’s forward-thinking adoption of AI-driven solutions reflects its dedication to continuous improvement and innovation. This case study demonstrates how technology serves as a powerful example to create safer, more efficient industrial environments. Through advanced monitoring systems, YTML has emerged as a leader in smart factory management, setting the stage for future advancements in industrial safety and operational excellence.