Manufacturing Quality Control with Graph Analytics

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Neo4j

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

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

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

•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

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