Case Study
Global manufacturer reduces machine stoppages by 35%
THE CASE
A leading automobile spare parts manufacturer needed help with production loss, inventory management, and material wastage that decreased throughput and efficiency across the factory floor.
CHALLENGE
Our client reported a series of inefficiencies including, unscheduled machine stoppage that caused recurring shortfalls and failure to meet daily production targets, misplaced incorrectly stored raw material, disruptions in changeover processes, and machine downtime.
SOLUTION
UST’s AI experts explored the nature of the client’s issues and designed a framework of Vision AI-based solutions to help the client overcome their factory floor challenges. UST VisionBox used the factory’s existing CCTV camera network and enabled it with AI-powered vision intelligence (AIVI) to enable real-time analysis of the video output and identify key problem areas.
IMPACT
UST VisionBox detected machine stoppages preemptively and optimized the component changeover process time thanks to machine status and predictive data provided to the supervisors and plant managers. In addition, the UST team identified a misalignment of manufacturing equipment by viewing highly sensitive sensors in machines and video feed from AI-enabled CCTV cameras. As a result, continuous monitoring for material misalignment and immediate flagging of the anomaly now enables factory managers to fix issues on a priority basis.
To fix the misplaced raw materials issue, UST implemented smart tagging to place smart chips on raw materials. This helped with object tracking and object detection in real-time, minimizing material loss and stopping shop employees from loading the wrong material onto a machine.
Real-time alerts reduced the frequency and duration of machine stoppages by nearly 35%, leading to a 16% increase in productivity, a 26% decrease in losses due to unmet production targets, and an 80% increase in material movement and handling compliance.
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