The AI system sends instant notifications when a vehicle is improperly loaded, significantly improving response times to loading violations. This real-time detection ensures immediate action to prevent unsafe transport practices.
Cost Savings
By automating manual inspection processes previously handled by human inspectors, the system has saved approximately £15 per hour, reducing operational costs associated with material transportation compliance.
Detection Accuracy
The system achieved a 98% accuracy rate in detecting properly and improperly loaded vehicles, surpassing the target of 95%. It continues to improve with ongoing use, ensuring a high level of compliance monitoring.
Alert Response Time
The average response time for alerts generated after detecting improperly loaded vehicles is 1.8 seconds, surpassing the target of under 2 seconds.
Reduction in Illegal Loads
Since system deployment, the AI solution has achieved a 62% reduction in illegal vehicle loads within the first few months, progressing toward the target of a 70% reduction within 6 months.
Environmental Impact
By ensuring that only properly loaded vehicles pass through, the AI system optimizes vehicle trips, reducing unnecessary movements and lowering carbon emissions from material transport.
Key Performance Indicators
Detection Accuracy
Formula
Detection Accuracy = Number of correctly identified vehicles (loaded/empty and authorized) / Total number of vehicles monitored × 100
Goal
Maintain a detection accuracy rate of over 95%, ensuring correct identification of vehicles.
Cost Savings
Goal
Continue reducing operational costs by saving £15 per hour through automated AI inspections, replacing manual human inspections.
Reduction in Illegal Loads
Goal
Achieve a 70% reduction in illegal vehicle loads within the first 6 months post-implementation.
Current Performance
62% reduction in illegal loads, with progress being tracked monthly to reach the goal.
Environmental Impact
Goal
Reduce unnecessary vehicle trips by optimizing loading compliance, contributing to lower carbon emissions from material transport activities.
