Bernhard & Varsha Venugopal
Solving the puzzle: Leveraging machine learning for effective root cause analysis
#1about 3 minutes
The challenge of root cause analysis in complex manufacturing
High-precision manufacturing, like semiconductor lithography, generates complex data that makes finding the root cause of failures extremely difficult.
#2about 3 minutes
Why traditional data analysis methods fall short
Standard statistical methods like linear regression fail with real-world manufacturing data due to non-linearity, missing values, and high class imbalance.
#3about 2 minutes
Using explainable AI to understand black box models
Explainable AI (XAI) provides methods to understand how a machine learning model makes predictions, which builds trust and helps discover hidden patterns in data.
#4about 9 minutes
A three-step approach using LightGBM and SHAP
A practical workflow for root cause analysis involves training a LightGBM model, explaining it with SHAP to find feature importance, and forming hypotheses from the results.
#5about 2 minutes
Building a self-service tool for domain experts
To scale the impact of data science, an internal self-service tool was built to empower engineers to perform their own root cause analysis without data science expertise.
#6about 3 minutes
Key principles for successful tool adoption by engineers
The tool's success relied on being human-centric, transparent about limitations like correlation vs causation, robust for real-world data, and simple to use.
#7about 1 minute
Empowering engineers with accessible machine learning tools
By providing simple, transparent, and powerful tools, engineers can leverage machine learning to solve complex problems much faster than before.
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