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Manufacturing Quality Control with Graph Analytics

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Speakers: Eric Wespi, Data Scientist, Boston Scientific Ryan McGowan, Data Scientist, Boston Scientific

Technology
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Manufacturing Quality Control with Graph Analytics
Presenters Eric Wespi Boston Scientific eric.wespi@bsci.com Ryan McGowan Boston Scientific ryan.mcgowan@bsci.com
• Introduction and manufacturing context • Methods for extracting insights from the graph • Use case evolution • Platform maturity • Questions Outline
Boston Scientific
What caused a failure? • Vertically integrated • Batch processing • Multiple teams • Nonstandard analysis methods • Multiple data sources Business Problem & Use Case +
Supply Chain Mapping Product Qty: 100 Part A Qty: 100 Failure Qty: 1 Part A Qty: 100 Results In Part Part Part Part B
Analogy
•Simple •Explainable •Whiteboard friendly Interpretable Data Models
Links between failures Visualizing Relationships
Looking for patterns can lead to scary graphs…
Date Score • Every batch (node) gets a “score” • Scores can be analyzed in a number of ways Extract and Analyze Graph Data Score Process Data Product Qty: 100 Part A Part A Failure
Adding new nodes/relationships • Products • Processes • In process testing • Clinical feedback – Initially built from single database What next? Evolving the data model
Connecting Products • Apply findings from existing products to new ones • Alert other users of suspicious batches/materials more quickly • Improve sensitivity to weak signals
• Inline manufacturing feedback – Sometimes, upstream testing can be a critical input to finished device yield – Predictions can lead to prescriptions Expanding arenas Input param: 70% 50% Yield Input param: 60% 95% 90% Yield 60% 97% 95% Yield 70% Random Batch Matching
Platform maturity “Our Journey” CSV files Desktop Cloud instance Streaming Data Enterprise License Managed ServicesLEGACY SCALED
Questions? Thank You

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