Supply Chain Twin Demo - Companion Deck

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Neo4j, Inc. All rights reserved 2022
1
Supply Chain Twin
Demo Companion Deck
Nicolas Rouyer
Sales Engineer, Neo4j

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Graphs in Supply Chain
Management & BOM

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Worldwide supply chain problems

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Organizations Facing Challenges Across Supply Chains
Worsened by the Pandemic
Planning Sourcing Production Sales
Warehouse &
Distribution
69% 74% 68% 69% 67%
Difficulty planning
due to lack of
information on
impacted
suppliers
Shortages of
critical parts /
materials
Difficulties in
balancing stock
between
warehouses
Difficulties
reconfiguring
product lines
Lost sales due to
stock outs
Percentage of organizations that faced significant challenges in each area
Source: Capgemini Research Institute, Supply Chain Survey, August-September 2020, N=1,000 organizations

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Worldwide Supply Chain Problems are Data Problems...
75%
938
of Fortune 1000 had a T1-2 supplier
impacted by the pandemic
Good forecasts are just
less bad forecasts
of executives aren’t confident in
their data quality

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Supply Chain is a Graph

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Supply Chain is a Graph
● Customers
● Orders
● Employee
● Suppliers
● Materials
● Products
● Plants
● Distribution Centers
● Shipments
● Global News
● Etc…

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Supply Chain is a Graph
Because of its connected
nature, supply chain is a
massive data challenge

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Demand Products Manufacturing
Suppliers Order
Fulfillment
Transportation Weather Geospatial Third
Party
Leveraging Cross-Silo Connections
The property Graph Model

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Google + Neo4j Supply Chain Twin Architecture
BigQuery
Supply Chain
Twin L2 Data
Dataflow Graph Database
Bloom
Graph Data Science
Keymaker Framework
Supply Chain
Twin Application
Analysts
Supply Chain Service
Source data is present in Google supply chain twin L2
canonical and GDELT public datastore.
Google Dataflow is used for one time bulk and batch data
processing for consuming Google supply chain twin L2
canonical data by executing Google BigQuery and loading
that data into Neo4j Database after transformation using
Neo4j Spark Connector integration.
Neo4j database enables you to store, query, analyze, and
manage highly connected data. Neo4j supports Apache
Spark, Kafka Streams, BI and database drivers integration to
load/read data into/from Neo4j database.
Neo4j Graph Data Science (GDS) is a connected data
analytics and machine learning platform that helps you
understand the connections in big data to answer critical
questions and improve predictions.
Neo4j Bloom data visualization tool allows analytics team to
freely explore graph data to validate findings and improve
explainability. Here web application also embeds its UI for an
easier accessibility of contextual data.
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Neo4j Keymaker is a data model agnostic tool designed to
help organizations operationalize their graph based analytical
queries. Here it executes product risk score, product parts
shortfall and other queries to serve data insights to web
application through its GraphQL endpoints.
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Application Service is implemented using Neo4j GraphQL
Library to access Neo4j database data using low code
approach.
Supply Chain Application allows customer to find additional
insights of their supply chain data which gets enriched with
highly connected data insights using Neo4j Graph database
and Data Science services.
GDELT Data
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6 Google Applications
Google Applications can integrate to consume this connected
data insights by invoking Keymaker or Supply Chain
Application service GraphQL endpoints.
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Products Solution
Accelerators
Flex
Templates
Solutions Workbench
A
Iterative Graph Modeling. (Design time).
A

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Google Supply Chain Twin L2 Data Model

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Neo4j Supply Chain Twin - Graph Data Model

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Orders, Fulfillment and Product Shortfall
• Total orders
• Orders unable to be fulfilled
• Products and Parts shortfall
against the orders

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Best Selling Products, Common Parts
• Current Best Sellers
• Common Parts used by best
sellers
• Low inventory common parts
• Which of these common
parts can be made more of
based on inventory levels
multiple levels deep
• Legal Entities for low
inventory parts and their
location

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Part Shortages per Order (multi-level + tree)
• Part Shortages per order for
each level of the part
• Multi-level
◦ Shows how many more
parts can be made using
low level parts
• Tree
◦ Shows suppliers from
top-level products ordered
by shortfall

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Part Shortages per Order
• Graph traversals make it easy to
traverse the supply chain - looking
for parts made of other parts
• Inventory information can be looked
up as part of the traversal to
calculate shortfalls
Part graph
traversals
Parts
Inventory

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Part Shortages per Order (Sankey)
• Sankey diagram looks where
the part is used further up
the supply chain

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Risk Scoring based on Orders, Inventory, Part Centrality
and Location

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Evaluate Supply Chain Risk
• Analyze the most critical parts and
calculate the part centrality risk score
using betweenness centrality
• Calculate location risk score based on the
coordinates of the manufacturer’s address
• Calculate order risk score based on
current inventory levels and order
quantities
Provide actionable insights by simulating more
inventory and order and modifying risk
weightage on the fly.
High
Centrality
Product
News Events
Order Fulfillment
Risk

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Additional Use Cases
Demand Sensing
• Forecasting across Products,
seasons/time periods, Location, etc.
Channel Shaping
• Analyzing orders by customer /
partner, frequency, trends, etc.
Supply Management
• Inventory forecasts
• Demand based Inventory
replenishment
• Alternate Supplier
Dynamic Risk Alerting
• News monitoring and event based risk
calculations and alerting
• Multi factor risk calculations
Business Planning
• Scenario based planning and analysis
• Balance demand, supply, inventory
and financial targets