Average Time to Repair Cut by 70% With AI-Powered Root Cause Analysis
Electronics Manufacturing | Singapore
REDUCTION IN ATTR
WEEKS TO DEPLOY
FASTER RETRAINING
"From 6-month pilot programs to 3-week deployment. Seethos completely changed our approach to quality control. Their no-code interface means our operators retrain models for new products in hours, not weeks."
Sarah Chen
VP of Operations
The Full Story
A leading electronics manufacturer in Singapore was struggling with long repair times for their complex products. The average time to repair (ATTR) was a major drain on resources and was impacting customer satisfaction. The company needed a way to quickly identify the root cause of failures and get products back to customers faster.
The company had been using a traditional approach to root cause analysis, which involved a team of engineers manually inspecting failed products and trying to identify the source of the problem. This process was slow, expensive, and often inconclusive.
Seethos proposed a new approach: using AI-powered root cause analysis to automatically identify the source of failures. We developed a custom AI model that was trained on a massive dataset of product data, including information on product design, manufacturing processes, and customer feedback.
The model was able to identify the root cause of failures with a high degree of accuracy, and it was able to do so in a fraction of the time it took the manual inspection team. The company was able to reduce its ATTR by 70%, which resulted in significant cost savings and improved customer satisfaction.