How Graph Technology
Drives Modern Supply
Chain Strategy
Graph Database & Analytics Powers Agile,
Resilient Networks
Table of Contents
Title
Introduction: A New Paradigm in Supply Chain Management
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SECTION 1
The Modern Supply Chain Faces Unprecedented Challenges
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Scale and complexity introduce vulnerability
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Consumers demand flexibility and transparency
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New Priorities for Supply Chain Leaders: Resilience, Agility, and Sustainability
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Why Supply Chains Can’t Be Modernized on Legacy Systems
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Key drawbacks of legacy supply chain management systems
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SECTION 2
Visualize & Understand Your Entire Supply Chain With Graph Technology
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How graph databases handle supply chain complexity
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Graph Data Science offers deep insights into supply chain operations
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C A S E S T U DY
Using Graphs to Optimize Vehicle Testing, Logistics, and Procurement
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Getting cars to market faster
7
Transforming parts procurement and logistics
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Making supply chain experts smarter
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The Economic Impact of Neo4j Graph Technology
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Modernizing Your Supply Chain With Neo4j
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Introduction
A New Paradigm in Supply Chain Management
The modern supply chain has a lot to contend with: costly, hard-to-predict disruptions, rapidly changing customer demands, increasing calls for transparency and ethical sourcing, and more.
As supply chain leaders seek to build more flexible, resilient supply chain systems, they’re discovering that graph database technology is an indispensable tool. Graph databases are
designed to reveal patterns in vast, deeply interconnected datasets that originate in disparate systems — exactly what supply chain modernization requires.
In this white paper, we take an in-depth look at the capabilities of graph technology and its potential to transform supply chain management.
SECTION 1
The Modern Supply Chain Faces
Unprecedented Challenges
Scale and complexity introduce vulnerability
With the shift towards an increasingly globalized economy, supply chains have evolved
from regional or national systems into transnational networks spanning diverse
climates, political regimes, and modes of transport.
That increase in scope and complexity has introduced new vulnerabilities.
Modern supply chains are vulnerable to any local disruption occurring within their vast
networks — and the menu of potential disruptions is itself vast, encompassing
infrastructure failures, border disputes, epidemics, natural disasters, and retaliatory
trade policies. A regional conflict can affect the availability of energy supplies,
agricultural commodities, and manufacturing components across the globe.
When supply chains become this large and complex, disruptions tend to be frequent
and expensive.
Over a decade, the financial fallout from these disruptions is likely to equal 30 percent
of one year’s EBITDA in sectors like consumer goods.1 In the near term, this kind of volatility might get worse before it gets better. Leading
geopolitical analysts like Peter Zeihan expect supply chain disruptions to increase
over the next few decades, as the global order evolves and regional powers look to
reshape trade protocols and practices.
Consumers demand flexibility and transparency
Aside from the difficulties caused by disruptions, supply chain leaders must also
contend with the growing demand for transparency and traceability. Concerns about
sustainability and ethical sourcing drive this demand, pushing companies to invest in
technologies that provide real-time tracking and advanced analytics for
predictive modeling.
Consumer needs and expectations have also shifted, requiring the capacity to predict,
prepare for, and respond to a rapidly-changing market.
Companies can expect a monthlong supply
chain disruption every
3.7 years
1 McKinsey Global Institute, “Risk, Resilience, and Rebalancing in Global Value Chains,” August 6, 2020
2 Zeihan, Peter, The End of the World Is Just the Beginning: Mapping the Collapse of Globalization, Harper Business, June 14, 2022
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New Priorities for Supply Chain Leaders:
Resilience, Agility, and Sustainability
50% of companies lack visibility into In this increasingly complex environment, supply chain leaders have begun rethinking
their supply chains4
— and redesigning — their supply chains. They’re moving beyond the traditional model,
which prioritizes cost control and stability, to create digital supply networks that are
more resilient, agile, and sustainable.
Resilience involves anticipating and quickly adapting to new types of supply chain
disruptions; agility allows companies to meet fast-evolving, hard-to-predict customer
and consumer needs; and sustainability means modernizing with an eye towards
Key drawbacks of legacy supply chain management systems
While supply chains have evolved into complex digital supply networks, supply chain
management systems have not evolved with them. Many companies still rely on legacy
relational databases, which fail in several critical areas:
creating more environmentally responsible systems. Without the right technological solutions, it’s impossible to achieve these goals — let
alone build a strong supply chain. As a result, supply chain leaders are turning to
Lack of adaptability
technology specifically geared to managing interconnected data and systems. They’re
Business needs change quickly in the supply chain industry. Relational databases,
which have rigid, predefined schemas, cannot adapt at the speed of the market.
also hiring more supply chain managers to facilitate modernization and address
Poor performance with large connected datasets
systemic bottlenecks.3
As data size and complexity grow, the performance of relational databases degrades.
This is particularly true for operations that involve multiple JOINs and degrees of
separation. For example, tracing a product's journey across a global supply chain or
Why Supply Chains Can’t Be Modernized on
Legacy Systems
identifying the ripple effects of a disruption require traversing multiple connections.
Relational databases struggle with such operations, leading to slow query responses
and overall poor performance.
Any new supply-chain management solution will need to solve a fundamental problem:
Difficulty in handling complex relationships
harnessing a sea of data from myriad sources and identifying patterns in that data, so
Relational databases are poor at modeling intricate supply chain networks. The
supply-chain professionals can make critical decisions in real time.
relational model introduces unnecessary complexity with rows, columns, indexes, and
Many companies, however, still rely on legacy systems to handle modern supply chain
JOINs. You get low fidelity in a highly complex model, when you want a low-complexity
demands. Those systems are ill-equipped to model end-to-end supply chains with high
big picture with high-fidelity data processing — a bird’s eye view of your facilities,
people, products, inventory, customers, and suppliers.
fidelity, rapidly answer queries involving rich data from multiple sources, uncover
hidden relationships and patterns, or make accurate predictions under
changing circumstances.
3 Financial Times, “Supply chain managers in demand as businesses hit by shortages,” May 14, 2023
4
Tive, “The State of Visibility 2023,” April 21, 2023
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SECTION 2
Visualize & Understand Your Entire Supply
Chain With Graph Technology
Graph technology has emerged as a powerful solution to the challenges facing
modern supply chains. The technology has two main pillars: Neo4j Graph Database
and Neo4j Graph Data Science.
Graph databases help in key areas, including:
Digital twin
Graph databases allow you to create a high-fidelity digital map of assets and collect
associated data. Suitable for modeling complex networks, graph databases represent
the physical supply chain in a digital format, which enables real-time tracking and
analysis. This aligns with Gartner's trend of Smart Operations, which extends the
concept of smart manufacturing to encompass all core operational capabilities.
Logistics
Best-practice management means tracking the movement of products and vehicles
How graph databases handle supply chain complexity
through the entire supply chain in real time. Graph databases handle the high volume
Graph databases excel at modeling complex supply chains with high fidelity. They
insights. This aligns with Gartner's trend of Mobile Asset Optimization, which involves
make it easy to model recursive relationships, for example, so they’re ideal for
improving coordination across supply chains by increasing visibility into shipping and
managing suppliers that extend beyond tier 1 into tier 2 and even tier 3. Unburdened by JOINs and indexes across rows and columns, graph databases rapidly
query rich, interrelated data, no matter how large the datasets grow. You can query
where to source alternative parts or find customer orders — and get answers in
real time.
Flexible by design, graph databases adapt as supply chains evolve. Managers can
easily add data classes and entities when they introduce products or product
categories. Accommodating new suppliers and channel partners becomes simple — no
need to refactor applications or the data platform.
and velocity of data generated by modern logistics and provide real-time visibility and
supplier operations.
Sustainability
In advanced economies, customers increasingly expect visibility into the social and
environmental impacts of their spending. Graph databases excel at storing and
correlating metadata with products and inventories, enabling organizations to track
their carbon footprint over time or audit the facilities involved in delivering products to
customers.
Predictive analytics
Modern supply chain professionals need to identify root causes of problems or events
and anticipate business outcomes. By capturing and analyzing the complex
relationships within supply chain data, graph databases uncover previously hidden
Graph databases support the top supply chain technology trends identified by Gartner
bottlenecks and predict the ripple effects of potential disruptions, enabling more
for 2023: Actionable AI, Smart Operations, Mobile Asset Optimization, and Supply
proactive decision-making.
Chain Integration Services.5
Visualization
Unlike relational databases, which store data in rows and columns, graph technology
stores data as nodes and relationships, allowing for a more intuitive visualization of
data. Visualization makes it easy for non-technical stakeholders to digest data.
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Gartner, “Top Strategic Supply Chain Technology Trends for 2023,” May 10, 2023
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In its 2023 supply chain strategy report, Gartner encourages industry leaders to
adopt a more holistic approach to supply chain management. Graph databases, with
their ability to handle complexity, large datasets, and intricate relationships, allow
organizations to understand and optimize the entire supply chain in an integrated,
comprehensive way.
Graph Data Science offers deep insights into supply chain operations
The capabilities of graph technology extend beyond just data storage and retrieval.
The technology also provides a powerful platform for executing graph algorithms,
which are designed to analyze and interpret the relationships between data points.
These algorithms uncover patterns and structures within your data that are not
immediately apparent, providing deeper insights and enabling more informed
decision-making. Graph Data Science focuses on the relationships between data points, which are
often more important than the data points themselves. In the context of supply chain
management, those relationships will reveal the most critical nodes in your supply
chain, potential bottlenecks, and the ripple effects of a disruption.
In our experience, the most popular graph algorithms used in supply chain
optimization fall into four categories:
Pathfinding and search algorithms
Identify the types of paths or routes between nodes — the shortest or most
robust path, all possible paths, etc.
Centrality algorithms
Find the most important nodes in your supply chain — highly connected nodes,
centrally located nodes, or nodes that bridge key parts of the supply chain.
When paired with graph visualization capabilities, centrality algorithms make it
easy to pinpoint potential bottlenecks.
Community detection algorithms
Understand how entities in your supply chain cluster together. Identifying
densely connected communities of nodes allows you to anticipate the bullwhip
effect — you’ll be able to predict how a delay from one supplier might impact
all closely connected suppliers.
Similarity algorithms
Increase the resiliency of your supply chain by quickly identifying alternative
suppliers for similar parts.
Applying these algorithms will deepen your understanding of supply chain structure
and improve decision-making in multiple areas, from strategic planning to day-today operations.
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Gartner, “Top Strategic Supply Chain Technology Trends for 2023,” May 10, 2023
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CASE STUDY
Using Graphs to Optimize Vehicle Testing,
Logistics, and Procurement
Many organizations, including national militaries, car manufacturers, and maritime
logistics companies, have used Neo4j graph technology to optimize their supply
chains. These case studies illustrate the tangible benefits of Neo4j on supply
chain operations.
Getting cars to market faster
Vehicle testing is an essential but time-consuming process for car makers. To identify
and fix defects before full-scale production begins, companies must collect and
organize large volumes of test data. A major Japanese auto manufacturer was
struggling to make its critical test information useful for long-term product validation
management (PVM). The manufacturer’s product validation lifecycle wasn’t working
because non-standardized vehicle test data couldn’t effectively identify the root cause
of problems, much less fix them.
The carmaker leveraged Neo4j to build a knowledge graph to standardize metadata
and expedite the product validation lifecycle. The enterprise knowledge graph unifies
testing data for faster, more accurate product decisions. It has also significantly
reduced time to market for both new and existing vehicles.
Read the full case study
Transforming parts procurement and logistics
The U.S. Army procures millions of spare parts every year, sometimes ordering millions
of components at a time. Tracking costs is critical, because the maintenance and
support costs of equipment account for 80 percent of total lifecycle cost. Until
recently, however, it struggled to track costs effectively — data volumes were
increasing, data sources were changing, and it relied on an aging, mainframe-based
tracking system.
The Army turned to Neo4j, which deployed a 3TB graph database with over 5.2 billion
nodes and 14.1 billion relationships. Neo4j graph technology now provides critical
visibility into total equipment costs, and the military rapidly stores, explores and
visualizes its wealth of logistical data. It used to take 60 person-hours to load the data
required to understand which parts needed replacing — now, with Neo4j, it only takes seven.
Read the full case study
Making supply chain experts smarter
Global procurement services firm Scoutbee offers in-depth insights into supplier
networks and supply chains for some of the world’s largest businesses. Their
customers manage enormous data volumes — some use 10 ERP systems that pull data
from a bewildering array of sources.
Scoutbee realized it needed a knowledge graph to connect the dots within the data
and bridge gaps between the data and its customers’ business vocabularies. Neo4j
graph technology allowed Scoutbee to create graphs that everyone could use —
business users and other non-technical folks, as well as data scientists and engineers.
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Scoutbee cut supplier discovery time by
75% — from 180 hours to 12
Scoutbee’s supplier discovery is now 75% faster — processes that used to take 100–
180 working hours over 24 weeks now take 8–12 hours over six weeks — and the
company provides intelligence on suppliers as they exist in the real world, with
semantic relationships to data from various domains. Scoutbee can leverage all
available data on a given supplier, create visualizations of complex supplier
interdependencies, and select the optimum supply chain partner at any moment
in time.
Read the full case study
Consider the impact of a 20% improvement on applications targeting revenues of $5
million — this translates to a benefit exceeding $2.2 million over three years for the
composite organization studied.
Neo4j also accelerated time to value. The study found that the average development
time was cut from a year to just four months. For a development team of six, this time
saving equates to a financial saving of over $1.1 million across three years.
Neo4j’s return on investment (ROI) is equally impressive. The study found that a
composite organization could expect benefits of $5.19 million over three years versus
costs of $1 million, resulting in a net present value of $4.18 million and an ROI of 417%.
These findings underscore the significant economic advantages of implementing
graph technology in supply chain management. By choosing Neo4j, organizations not
only address today’s most difficult supply chain challenges; they also secure a high
return on their investment.
The Economic Impact of Neo4j
Graph Technology
Optimizing your supply chain can deliver substantial economic benefits. A
commissioned study conducted by Forrester Consulting, “The Total Economic
Impact™ of the Neo4j Graph Data Platform,” provides compelling insights into the
economic benefits that Neo4j delivers to its customers.
The study found that organizations using Neo4j experienced notable business
improvements, including
Faster data querie
Enhanced collaboration with business stakeholder
Increased ability to source additional data
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CONCLUSION
Modernizing Your Supply Chain With Neo4j
For supply chain leaders, graph technology is less an option than a competitive
necessity. It transforms supply chains into resilient, efficient and transparent networks
— exactly what success in a global economy requires, and what consumers and
investors increasingly expect.
As the industry leader in graph database and analytics, Neo4j offers business agility,
risk reduction, optimized costs, and accelerated time to value, while ensuring
compliance with industry standards.
Neo4j graph experts are available to answer any questions you might have about
unlocking the value of your supply chain data or supply chain modernization generally.
They’ll also work with you to model your own optimization initiative, so you can gauge
the potential benefits for your specific use case and organization.
Consult With a Graph Expert
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