How Predictive Quality Analytics Reduced Defects on an SMT Line
For many electronics manufacturers, defects are not caused by a single failure or a broken machine. They are the result of small, compounding process variations that go unnoticed until yield drops and scrap increases. Traditional quality systems detect defects after they occur. Predictive quality analytics…
AI Visual Inspection for SMT Lines: From Detection to Root Cause
Visual inspection is a critical control point in SMT manufacturing. AOI systems can detect thousands of defects per shift, but for process engineers, detection alone is no longer enough. The real challenge starts after a defect is detected. Why did it occur? Is it a…
SMT Analytics: How Electronics Manufacturers Use Real-Time Data to Improve Yield
In modern electronics manufacturing, SMT lines generate enormous amounts of data. Placement machines, SPI, AOI, reflow ovens, test stations — all produce valuable signals every second. Yet in many factories, this data is still underutilized. Engineers often rely on static dashboards, post-shift reports, manual investigations,…
Smart Factory Analytics Platform: Connecting Data, AI, and ROI
Manufacturers are transforming their operations from reactive monitoring to proactive intelligence. At the center of this shift is the Smart Factory Analytics Platform — a data ecosystem that connects machines, test stations, and quality systems into one intelligent layer. With real-time analytics, predictive insights, and…
Why aren’t “PASS/FAIL” criteria sufficient for controlling manufacturing yield and quality?
Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before your products are ready to be shipped to your customers. At every stage of the production flow—from incoming raw materials to the final stage before delivery—we implement…
