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Success Stories

Enhancing Shopfloor Analysis with
Machine Learning and SPC Solutions

Achieving Early Anomaly Detection and Improved Process Control

In today’s fast-paced manufacturing environment, early anomaly detection and real-time process monitoring are crucial for maintaining production efficiency and quality.

At camLine forum 2024, Julie Ravel, Front End Manufacturing Process Control Program Manager, and Hélène Isselé, SPC Process Control Manager from STMicroelectronics, shared their insights on leveraging Machine Learning (ML) and Statistical Process Control (SPC) solutions to enhance shopfloor analysis and optimize first-level decision-making.

Driving Process Improvement Through Machine Learning

One of the most compelling discussions at camLine forum 2024 focused on Machine Learning’s role in detecting production anomalies early. Julie and Hélène highlighted the transformative potential of ML in analyzing raw data, allowing manufacturers to identify process deviations before they impact production quality.

"We see great potential for applying machine learning at STMicroelectronics to make better use of raw data, leading to more effective anomaly detection in process profiles."

— Julie Ravel, STMicroelectronics

The Role of LineWorks SPACE in Enhancing Shopfloor Analysis

Before adopting LineWorks SPACE, STMicroelectronics faced challenges in first-level shopfloor analysis after out-of-control (OOC) events. The existing methods often produced irrelevant insights, requiring continuous improvements in how real-time process data was delivered to shop floor operators.

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With LineWorks SPACE, along with its powerful plug-ins LineWorks SPACE Charts Device Maps Plugin (DMP) and LineWorks Statistical Defect Control (SDC), STMicroelectronics has successfully streamlined first-level analysis. The solution now provides relevant, real-time data, improving out-of-control event handling and enabling faster, more accurate decision-making.

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