Enhancing Vehicle Tracking Accuracy for Drive-Thru Restaurant

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THE CHALLENGE

PAR, a drive-thru restaurant, faced significant challenges maintaining accurate data due to outdated hardware. One of the critical issues was the difficulty in tracking car orders accurately. This problem was exacerbated by environmental challenges, particularly heavy snowfall, which made vehicle identification difficult.

CLIENT REQUIREMENT

PAR’s primary requirement was to obtain a live vehicle tracking report. Specifically, they needed to monitor the time a vehicle spent in the drive-thru, from entry to exit. This data was essential for improving operational efficiency and customer service.

THE SOLUTION

After thoroughly understanding PAR’s requirements, we proposed the implementation of a Drive Thru Timer system. This system leverages an AIML (Artificial Intelligence and Machine Learning) sensor integrated into the camera to provide real-time car tracking insights.

Tech Stack Use:- Node Js, React, and Python

 Key Features of the Solution:

  • Real-Time Vehicle Tracking

    The AIML sensor in the camera detects and records the vehicle’s in and out time, providing precise tracking information.

  • Accurate Vehicle Identification

    The camera sensor identifies vehicles based on unique features such as car and model numbers. This helps in avoiding duplicate entries for the same vehicle.

  • Handling Environmental Challenges

    As a solution to the difficulty in identifying vehicles during heavy snowfall, we conducted various car model trend checks. This approach ensured that the system could accurately identify vehicles despite adverse weather conditions.

THE OUTCOME

The implementation of the Drive Thru Timer system with AIML sensors yielded numerous benefits for PAR:

  • Increased Sales and Throughput 

    Locations deploying the PAR Drive-Thru Timer typically see at least a ten-second reduction in service times, translating to approximately 5 additional cars processed per hour. This results in more revenue with the same crew in the same amount of time.

  • Near-Real-Time Reporting

    Managers gain valuable insights to resolve bottlenecks, improve process steps for faster service, and coach or train crews or individuals more effectively.

  • Key Metrics Measurement and Reporting

    The system enables measurement and reporting of crucial metrics such as average total service times, total number of cars, window times, and drive-offs.

  • Proactive Remote Monitoring

    The system’s proactive remote monitoring capability allows for quick identification and resolution of issues without dispatching a technician, avoiding the cost of a truck roll.

  • Crew-Facing Screen

    A screen that gamifies the speed of service displays service time rankings, car positions, and more, further enhancing team motivation and performance.