AI System Optimisation

Within just 2 days, SAPHI was able to optimise the throughput of QUBE Logistic’s assets by 7 times without adding any additional infrastructure.

What Was The Problem?

QUBE Logistics was facing significant operational inefficiencies with their large-scale AI system, which was designed to detect defects in vehicles exiting transport ships. The existing system was taking an average of 45 seconds to process each car, resulting in substantial throughput delays and major time inefficiencies. This bottleneck was significantly impacting their ability to maintain a smooth and efficient workflow.

What Was The Solution?

To address these inefficiencies, our team at SAPHI implemented a solution without adding any new infrastructure. This included:

 

  • Asynchronous Processing: Redesigned the system to process images asynchronously, allowing cars to move continuously without stopping.
  • System Integration: Ensured that the new asynchronous processing integrated seamlessly with the existing infrastructure, avoiding any need for additional hardware.
  • Processing Speed: Focused on optimizing existing AI models and processing workflows to achieve significant speed improvements without additional computational resources. The AI algorithms were enhanced to process images 7 times faster.

What Was The Outcome?

The redesigned AI imaging system for Qube delivered exceptional results including:

 

  • Elimination of Downtime: Cars no longer needed to stop for imaging, significantly increasing throughput and operational efficiency.
  • Increased Processing Speed: Processing time was reduced 7-fold within just two days, leading to substantial time savings.
  • Revenue Impact: The efficiency gains generated millions in additional revenue for Qube by speeding up the assessment process and reducing delays.

 

This case demonstrates SAPHI’s ability to dramatically improve operational efficiency and performance using innovative AI solutions without the need for additional infrastructure investments.

 

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