How Boston Scientific Improves Manufacturing Quality Using Graph Analytics
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Tracking end of line manufacturing issues to their source can be a daunting task. Boston Scientific, in partnership with GraphAware, has used the Neo4j platform to build a manufacturing quality tool that offers dramatic improvements to the time, quality, and quantity of investigations. In this talk we will review a manufacturing value stream in a graph and discuss the analysis methods available, which result in striking increases in business efficiencies, for this unique application. We will also present how the system was implemented within the existing data architecture and then scaled from a laptop investigational tool to an enterprise-grade solution with Neo4j Server. *Talk at GraphConnect NYC 2018*
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How Boston Scientific Improves Manufacturing Quality Using Graph Analytics
- 1. GraphAware® How Boston Scientific Improves Manufacturing Quality Using Graph Analytics
- 2. Presenters GraphAware® Eric Wespi Boston Scientific eric.wespi@bsci.com Eric Spiegelberg GraphAware eric@graphaware.com
- 3. ‣ Introduction ‣ Project Themes ‣ Business Problem ‣ Graphs Add Business Value ‣ Graph Model Evolution ‣ Extracting Insights From Graphs ‣ Additional Use Cases ‣ Questions Outline GraphAware®
- 4. ‣ Data science team lead ‣ Engineering background ‣ High-volume manufacturing & development Boston Scientific GraphAware® Eric Wespi eric.wespi@bsci.com
- 6. GraphAware GraphAware® Eric Spiegelberg eric@graphaware.com ‣ Senior Consultant at GraphAware ‣ Java architect and developer ‣ Spring Portfolio ‣ Back end technologist
- 7. ‣ Consultancy ‣ Training ‣ Software Development ‣ Neo4j Certified Professionals ‣ Neo4j Solution Partner GraphAware GraphAware®
- 8. THEME 1 FOCUS ON THE BUSINESS PROBLEM
- 9. THEME 2 USE THE RIGHT TOOLS
- 10. THEME 3 KEEP IT SIMPLE
- 11. ‣ Business problem ‣ Discover graphs ‣ Exploration, vetting of technology ‣ Proof of concept (PoC) / Minimum Viable Product (MVP) ‣ Success & business value demonstrated ‣ Evaluate additional business opportunities ‣ Repeat Boston Scientific: A Case Study in Graph Adoption GraphAware®
- 12. What caused a failure? ‣ Vertically integrated ‣ Batch processing ‣ Multiple teams ‣ Nonstandard analysis methods ‣ Lots of spreadsheet manipulation Business Problem & Use Case GraphAware® +
- 13. Supply Chain Mapping GraphAware® Product Qty: 100 Part A Qty: 100 Failure Qty: 1 Part B Qty: 100 75 Issued 25 Issued Results In Part Part Part Part C
- 15. ‣ Query times decreased from ~ 2+ minutes to ~ 10-55 seconds ‣ Streamlines the process ‣ Enhances overall efficiency Graphs Add Business Value: Performance GraphAware®
- 16. Largest value added from non-functional areas: ‣ Simplicity ‣ Explainability ‣ Whiteboard friendly ‣ Data Accessibility Graphs Add Business Value GraphAware®
- 17. Estimates: ‣ 2.5 quintillion bytes of data created daily, accelerating ‣ 90% of the world’s data generated in the last 2 years Graphs Add Business Value: Data Accessibility GraphAware®
- 18. Everyone has data The competitive advantage is going from data to wisdom to action Graphs Add Business Value: Data Accessibility GraphAware®
- 19. ‣ Shortest path ‣ Variable length queries Graphs Add Business Value: New Capabilities GraphAware®
- 21. Links between failures Boston Scientific: Shortest Path GraphAware®
- 22. GraphAware® Looking for patterns can lead to scary graphs…
- 23. ‣ Every batch (node) gets a “score” ‣ Scores can be analyzed in a number of way Extract and Analyze Graph Data GraphAware® Date Score Score Process Data
- 24. ‣ Prepare data Python ‣ Build and test database py2neo/cypher ‣ Augment properties Weights and scores Extract Insights From the Graph GraphAware® Product Qty: 100 Part A Part B Weight: 0.75 Issued: 75 Weight: 0.25 Issued: 25 Failure …then develop a production-worthy pipeline through Hadoop:
- 25. BOSTON SCIENTIFIC NEXT STEPS Data Model Evolution
- 26. Many start by transfer existing data model to the graph 1:1 ‣ This approach is ok when getting started ‣ Follows crawl, walk, run ‣ May not take advantage of graph strengths Graphs are highly flexible, easy to change ‣ Many accustomed to high barriers and costs of changes in non-graphs ‣ Leads to the “Mentality of initial perfection” ‣ Avoid this mentality and revise, evolve model easily, as needed Graph Adoption Lessons GraphAware®
- 27. ‣ TopAssembly (TA) has a “product” text property ‣ Next: extract “product” to dedicated node, relationship Boston Scientific: Graph Model Evolution GraphAware®
- 28. match (ta:TA) with ta create (:Product {name: ta.product})-[:HAS_PRODUCT]->(ta) remove ta.product Graph Model Evolution GraphAware®
- 29. Data analysis expansion add supplier and supplier facility data Boston Scientific: Graph Model Evolution GraphAware®
- 30. ‣ Apply findings from existing products to new ones ‣ Alert other internal users of suspicious raw material batches more quickly ‣ Improve sensitivity to weak signals Connecting Different Products GraphAware® Part Part Product Failure Raw Material New Product Failure
- 31. • Import raw text describing inspection failures • Extract and correlate topics for better root cause investigation Future NLP Use Cases GraphAware® Event 1 Pertains to Topic 2 Topic 1 Event 2 Pertains to Pertains to Part Date/Time Topic 1 Frequency
- 32. GraphAware®