ArcGIS Knowledge

ArcGIS Knowledge: Supply Chain Visualization & Analysis

ArcGIS Knowledge adds an ArcGIS Enterprise Graph Store and new graph analytics capabilities to ArcGIS. With ArcGIS Pro as its first client, users can explore and analyze spatial and non-spatial data together in one context, whether the data is structured or unstructured, to accelerate decision-making. Developed to seamlessly connect analysts to the authoritative data they need and the analytical tools they trust, ArcGIS Knowledge empowers collaborative investigations.


A supply chain can be a complex network of people, processes, and systems that work together to deliver a high-quality service or goods to the end consumer at low operational costs to the business. As businesses attempt manage effectively through the current era of supply disruptions and bottlenecks – from plant shutdowns, natural disasters, and global conflicts – managers need greater visibility into their supply chain networks to help mitigate their risks and costs.

One way businesses can proactively manage their supply chain networks is to apply graph analytics. With Esri’s new graph analysis capabilities in ArcGIS Knowledge, users can connect many sources of information about their supply chain operations into a single context, even if their data is coming from different systems or in disparate formats. Analysts can explore a supply chain as a network of entities, such multiple tiers of suppliers, plants, parts, products, managers, and customers, and the relationships that connect them, like “produces” and “supplies” and “purchases,” all within a geospatial context. Now ArcGIS users can work with non-spatial and unstructured data alongside spatial and structured data. By applying sophisticated graph and geospatial analysis tools together, users can discover and better understand previously unknown relationships and dependencies within the supply chain network and gain new insights into their operations.

To highlight some of the key features and functionalities noted here and illustrate how knowledge graphs in ArcGIS can provide a holistic perspective on a supply chain network, let us consider a hypothetical scenario: A manufacturer has a Supplier in San Antonio, Texas (“Supplier 88”) experiencing a quality control issue that is delaying their deliveries of steel Parts to the manufacturer’s Plants. The manufacturer would like to better understand how this disruption will impact the rest of their supply chain network.

Visualize and Explore

If we were to approach this problem from a purely geospatial perspective, we would use a map to visualize the locations of all the Plants as well their Suppliers (Tier 1), their Suppliers (Tier 2), and their raw materials suppliers (Tier 3). Then we could use a series of joins or even draw origin-destination links on the map between each feature (each Plant or Supplier) to its immediate supplier.

Image of supply chain locations with the origin destination links.
Image of supply chain locations with the origin destination links.

We need to understand the dependencies implicit in our supply chain: a complicated, many-to-many array of parts and materials. Lines drawn on the map do not adequately capture these dependencies, nor do they allow us to connect more than two points into a multi-tiered network of suppliers. Instead, we can use a knowledge graph to create a digital representation of this complex supply chain network. We can add the Plant and Supplier locations into a knowledge graph, along with the non-spatial parts and relationships that connect them.

With all the data connected in the knowledge graph, we can start with Supplier 88 and expand our view of Supplier 88’s immediate relationships, one degree (or “hop”) at a time, to identify the other Suppliers and Plants that will be affected by the disruption. As we see from this link chart view, Supplier 88 supplies Suppliers 28 and 75, who each produce a variety of Parts, which are purchased by Plants 24, 25, and 26. By traversing the relationships, we were able to quickly go from an initial starting entity (Supplier 88) and isolate the subset of the supply chain network that will be affected (Suppliers 28 and 75, Plants 24, 25, and 26). In this way, the knowledge graph has made it easier to recognize the upstream and downstream flow of resources among the entities in this network to quickly determine the full impact of the disruption, multiple degrees removed from the origin of the disruption, Supplier 88.

Image of a link chart showing the other Suppliers and Plants that are directly related to Supplier 88 and will be impacted by its manufacturing delay.
Image of a link chart showing the other Suppliers and Plants that are directly related to Supplier 88 and will be impacted by its manufacturing delay.

Graph Query

Our investigation with the ArcGIS Knowledge Graph does not end there. Supplier 88 informed us that their quality issue for the steel parts was caused by and regional environmental contamination incident affecting their primary distributor of steel, Supplier 226. Since this could end up being a regional event affecting more than just the steel from this one supplier, we need to determine if our supply chain network includes any other suppliers in the same region of the world. The map reveals that Supplier 226 is located in the middle of Japan, and that the manufacturer has several other suppliers in the same region. However, the actionable insight we are really looking for is more complex: we need to know what the combined impact on our supply chain would be if these nearby suppliers are also contaminated.

Image of environmental contamination location for Supplier 226 and graph query.
Image of environmental contamination location for Supplier 226 and graph query.

Instead of manually expanding the view for each of these possibly contaminated suppliers in the same way we did for Supplier 88, we can write a graph query to do it automatically. In graph analytics, a “path” is a collection of entities and the relationships that connect them, so that a straight line can be drawn between two entities, multiple degrees apart. The graph query we wrote looks for a path that runs through all the raw materials, the network of suppliers, the parts, and the plants that they serve.

The graph query is written in openCypher, which is a query language similar to SQL, and Esri has also incorporated spatial analytics into the graph query. This allows us to also specify that at least one of the suppliers in the path must be located in the contaminated region. The query is executed quickly in ArcGIS Enterprise, where the knowledge graph resides, and the results can be visualized on a new link chart view. The graph query revealed that 9 more plants are going to be affected by this environmental contamination, along with several more Suppliers. The knowledge graph enabled us to skip the link chart visualization approach to exploring relationships and automatically get to the answer we needed, more efficiently and at a much larger scale.

Graph Analytics

Let’s learn more about two other graph analysis capabilities in ArcGIS Knowledge: the Search and Filter tool, and the Centrality tool. The Search and Filter pane can help us explore the results from the query above, or provide a summary of any part of the knowledge graph that you’d like to focus on. The Search and Filter pane summarizes the attributes and connections that the entities have in common, offering a histogram-like view of the counts for easy comparison in order to discover commonalities, outliers, and patterns in the data. Now that we know what Suppliers, Plants and Parts are going to be affected by this contamination, we can immediately take steps to minimize disruption to the supply chain network.

Image of spatially enabled graph query and resulting paths for all Suppliers and Plants potentially affected by the environmental contamination.
Image of spatially enabled graph query and resulting paths for all Suppliers and Plants potentially affected by the environmental contamination.

Another new graph analysis capability is the Centrality tool. Centrality applies several different graph algorithms that each measure how important or influential an entity is relative to other entities. In our example, the Centrality scores are calculated for the subset of entities that are in the link chart view. The Centrality View is a table showing the results of a variety of different centrality algorithms, including Degree, Eigenvector, PageRank, Betweenness, Closeness and others. We will get into more detail on what each one of these mean and how they measure relationships amongst entities in a follow-up blog post. For now, know that Degree, Eigenvector, and PageRank scores are calculated based on the number of relationships in which an entity participates. And Betweenness, Closeness, and Harmonic scores are calculated based on the length of the path from an entity to all the other entities in the link chart.

For this scenario, to see which of these Suppliers we depend on the most, we can sort by Page Rank, which yields the result of Supplier 10. Since we are looking at just the subgraph affected by the environmental contamination, this means that Supplier 10 being down will have the biggest impact on our operations, compared to the other affected suppliers.

Image of centrality tool with the Centrality View table of the environmental contamination.
Image of centrality tool with the Centrality View table of the environmental contamination.

In this example, we explored a few of the new tools and analytic methods you can use in ArcGIS Knowledge. Knowledge graphs in ArcGIS empower you to uncover patterns and anomalies in your connected data through spatial and graph analysis in one context, and graph analytics like centrality and paths can help you discover critical or influential entities in your supply chain network. By combining spatial and graph analytics to supply chain networks, we can achieve a more agile and holistic understanding of the impact of disruptions, make more nuanced predictions about where shortages will occur, and proactively build variety and diversity into our supply chain network to mitigate risk.

Get Started

To learn more about ArcGIS Knowledge, sign up for product announcements. To add ArcGIS Knowledge to your organization’s ArcGIS Enterprise deployment, contact an Esri representative.

About the authors

Christie is an Account Executive at Esri with over 6 years of experience in enabling organizations to utilize GIS as a business intelligence system that transforms their operations. She holds a master’s degree in Heritage Preservation with a Graduate Certificate in GIS from Georgia State University. In her spare time, Christie enjoys exploring the outdoors, hanging with family, and trying a new recipe in the kitchen.


Amy Clarke

Amy Clarke is a Senior Solutions Engineer at Esri with a decade of experience using knowledge graphs to connect data and analyze relationships in a wide variety of industry sectors. She focuses on bringing together nonspatial and spatial data, including unstructured data, in order to apply graph and spatial analysis in the same context.

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