Graph Data Science Streamlines Complex Medical Supply Chain Analysis

Challenge

Boston Scientific is highly vertically integrated in the development, design, manufacturing and
sales of its medical products. This means that the company manufactures its complex devices,
starting with raw materials such as resin and metal. Starting with raw materials requires a
significant amount of batch processing in addition to discrete manufacturing of finished
products.

With its complex manufacturing supply chains, Boston Scientific has numerous engineering
teams aligned based on the process technology they’re using.

Many devices use multiple technologies, so multiple teams are involved. With multiple teams,
often in different countries, working on the same problems in parallel, engineers resort to
analyzing their data in spreadsheets.

Decentralized ad-hoc analytics led to inconsistencies and, most importantly, an inability to find
root causes of defects.

Boston Scientific needed a far more effective method for analyzing, coordinating and improving
their manufacturing processes across all its locations.

Solution

Eric Wespi, Data Scientist at Boston Scientific, and his team understood the complexity of
their business problem and their inability to address it using traditional tools. Wespi attended
GraphConnect, where he met Eric Spiegelberg, a Senior Consultant at GraphAware, and
decided to work with him on Boston Scientific’s supply chain problems.

Boston Scientific moved from considering graphs to vetting the technology. “This is where the
quantum leap happens, where an organization goes from merely being a passive observer of
the graph community to being an active participant,” said GraphAware’s Eric Spiegelberg. “It
is typically embodied by an organization getting their actual data into Neo4j, which is a critical
step because it forces the organization to evaluate the design decisions and build out their
model.”

The heart of Boston Scientific’s graph data model consists of three nodes representing a
finished product, a part and a failure, with relationships that trace failures to parts and connect
those to finished products. This simple model effectively represents a complex medical device
composed of dozens of parts, manufactured from raw materials.

The data model is simple, but Boston Scientific’s graph is massive. Using Neo4j graph
algorithms from the Graph Data Science Library, the Boston Scientific team analyzes its graph
and computes scores that rank nodes based on their proximity to failures, enriching its models
with insights derived from the graph.

“We’re using Neo4j not as just a data store, but as a place to analyze data and store those new
characteristics of the data back in the graph and then extract it for traditional analysis,” said
Wespi.