Supply Chain

Supply Chain is an interactive map of a company’s real economic relationships: who it buys from (suppliers), who it sells to (customers), who it competes with (competitors), and who it partners with (joint ventures, R&D, licensing, distribution, manufacturing, marketing, and equity stakes). For each relationship it surfaces the revenue exposure, the program or product that ties the two companies together, when the relationship started, whether it is still active, and which side disclosed it. Use it to trace a company’s true second-order dependencies and concentration risk — not just its sector label. Find it under Equities → Supply Chain for any company. Users also call it the “Supply chain map,” “Relationship network,” “Customers & suppliers,” or the “Economic dependency map.”

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What it is

Supply Chain answers one question: who are this company’s real counterparties, and how concentrated is the exposure to each one. Instead of a sector or industry label, it shows the named companies on the other side of the money flow — the suppliers it depends on for inputs, the customers that drive its revenue, the rivals it competes with, and the strategic partners it works with. It loads on a Network view by default — the company in the center, suppliers fanning out to the left (money out) and customers to the right (money in), each sorted by its share of revenue, with partners and competitors in sections below. From there you can drill into a full, sortable table for any role, open a detail card on any counterparty, and push the result into the AI assistant or export it. The relationship graph is built on FactSet’s Revere supply-chain dataset — roughly 968,000 relationships across about 457,000 public, private, and government entities — so the picture reflects disclosed economic ties rather than a classification model.

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When to use it

Reach for Supply Chain when you need to understand who a company actually depends on, and how much:

• Size customer- or supplier-concentration risk — find the names that drive an outsized share of revenue (for example, a single customer at 25%+ of revenue).
• Find read-through plays — if a key customer cuts orders or a key supplier stumbles, trace who else is hit.
• Map a competitive set from disclosed market overlap — build a peer/counterparty list grounded in real relationships, not just a sector bucket.
• Discover partnership and licensing exposure — joint ventures, R&D collaborations, inbound/outbound licensing, distribution, and equity stakes.
• Surface names you would not get from sector classification alone — the second-order suppliers and customers behind a company’s reported numbers.

It is the go-to surface for second-order exposure analysis and for building a peer or counterparty list before deeper work.

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When to use something else instead

Supply Chain answers “who are this company’s real counterparties and how concentrated is the exposure” — not “what are its reported numbers.”

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How to use it

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What you get

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Views and tabs

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What the detail card shows

Hover any counterparty to open a card with:

• The counterparty’s name, ticker, country, and ISIN (and, when the matched name differs, the name the entity was matched as).
• A plain-language statement of the relationship from this company’s perspective — for example “Supplier to X,” “Customer of X,” “Joint venture partner of X,” “Contract manufacturer for X,” “Equity investor in X,” “Distributor of X’s products,” or “Licensor to X.”
• The specific programs or products involved (the context keywords — for example distinct US Government programs).
• Start date and, if the relationship is closed, the end date, plus Active / Closed status.
• The revenue concentration figure with an explicit statement of whose revenue it represents, and whether it was disclosed in filings or is a FactSet estimate.
• Which side disclosed the relationship.

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Partner subtypes

The Partners tab’s Subtype dropdown isolates: All Partners, Joint Venture, Research/Collaboration, Distribution, Licensee (inbound IP), Licensor (outbound IP), Manufacturing, Marketing, Equity Investee, or Equity Investor.

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Key capabilities

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What you can take away

• Interactive supplier/customer network graph with revenue share per counterparty.
• Sortable, searchable relationship tables for Suppliers, Customers, Competitors, and Partners.
• Per-relationship detail card — relationship type in plain language, programs/products, dates, status, revenue concentration, and disclosure source.
• Counts of distinct suppliers, customers, competitors, and partners.
• Export to CSV, Excel, JSON, or clipboard (tab-separated).
• Formatted relationship context pushed into AI Chat for Q&A and workflows.

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Data & sources

• The relationship graph — relationship types, revenue-exposure percentages, disclosure direction, start/end dates, context keywords, and active/closed status — is sourced from FactSet’s Revere supply-chain dataset. Coverage is roughly 968,000 relationships across about 457,000 public, private, and government entities. The FactSet Revere name appears on the no-data screen and in the reference that accompanies a hand-off to the AI assistant; the network legend itself shows only the relationship and entity counts.
• Entity identifiers (ticker, ISIN, and country) from FactSet Revere are used to identify counterparties; the ISIN together with the ticker resolves the correct entity for the company you are viewing.

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Tips & best practices

• Read concentration on the Customers tab. Sort by % of revenue (high to low): a customer around 10%+ is worth noting, and 25%+ reads as concentration risk. The network view emphasizes these heavy dependencies visually.
• Use the Source filter to know whose number you are reading. “Company” shows what this company named in its own filings; “Counterparty” shows what others named about it — and that changes whose revenue the % of Rev represents.
• Don’t be surprised when an entity appears twice. Distinct contracts or programs under one counterparty are kept as separate rows (shown as x N) so you can see the program-level breakdown, and a single company can legitimately be both a supplier and a customer.
• Expand before you conclude. The network shows only the top 10 per side by default; expand to 80, or open the full role tab, before judging how diversified a company’s customer or supplier base really is.
• Watch the asterisk. A starred figure is a FactSet estimate, not an officially disclosed number — weight it accordingly.
• Use the partner subtypes for risk type, not just count. Isolating inbound licensing or joint ventures tells you where strategic and IP exposure sits, separate from straight trading relationships.
• Hand the map to the AI assistant for the “so what.” Push the network or a table into AI Chat and ask which customers drive the most revenue, or who is exposed if a key counterparty pulls back.

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Limits & things to know

• The workspace loads currently-active relationships (up to about 2,000 per direction). In practice Status reads “Active” for the rows shown; the Active only toggle, the closed markers, the dashed closed-relationship lines, and end (“Until”) dates are part of the interface but typically have no closed rows to act on with the default load.
• The network shows the top 10 suppliers and top 10 customers per side by default; Expand raises this to 80 per side, and a “View all” jump to the full tab only appears when a side has more than 80.
• Competitor relationships do not carry a revenue-exposure percentage.
• % of Rev is the disclosing party’s revenue share. For company-disclosed rows it is this company’s revenue tied to the counterparty; for counterparty-disclosed rows it is the counterparty’s revenue tied to this company — not this company’s cost of goods. It is shown only where FactSet has a figure; otherwise it is blank.
• Some revenue figures are FactSet estimates (marked with an asterisk) rather than officially disclosed.
• Coverage is finite (about 457,000 entities). Smaller or recently-listed issuers may have no data, and some regional ticker suffixes may not be mapped — in which case the workspace shows a “no data” message.
• Entities without a real exchange listing (governments, central banks, privately-held subsidiaries) still appear, but their placeholder symbols are suppressed so they don’t show as misleading tickers — they simply have no tradeable ticker.
• Multiple distinct contracts or programs under one counterparty are kept as separate rows (shown as x N) rather than merged, so a single entity can appear several times with different programs.
• The header tally matches the tab counts. Both group relationships strictly by type. Inside the graph, however, a directional partner (licensing, manufacturing, distribution, or an equity stake) is drawn within the supplier or customer flow, so the per-section column counts there are grouped by money flow rather than by type.

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Availability

There is no feature-specific gating beyond having access to the Equities (Data Viewer). Availability depends on the company being covered in the FactSet Revere dataset; when it is not, the workspace shows a “no data” state.

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Works with other features

This surface has no add-to-watchlist, bookmark, report-generation, or note-taking control of its own; cross-feature workflows from here run through Add to AI Chat and Export. The selected sub-tab is reflected in the page link, so a specific view can be bookmarked or shared.

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Example workflow

Goal: assess a company’s customer-concentration risk and trace the read-through, then package the work.

1. Open the company in the Data Viewer and go to Supply Chain. On the Network view, read the money-in side to see which customers carry the largest share of revenue.
2. Switch to the Customers tab and sort by % of revenue (high to low). Flag any customer around 10%+ (a concentration note) and 25%+ (concentration risk). Hover the top names to confirm the program/product, the start date, the Active status, and whether the figure is disclosed or a FactSet estimate.
3. Set the Source filter to Company to see only what this company disclosed itself, then to Counterparty to see what others disclosed about it — noting how the revenue basis of the % changes.
4. Press Add to AI Chat and ask the assistant, “Which customers drive the most revenue, and who is exposed if the largest one cuts orders?” — answered against the exact table.
5. Take the customer list to a Grid to ask the same exposure question across the company’s peers, and use Document Search to pull the filings behind the largest disclosed relationships into a Data Room.
6. Generate a Report on the company grounded in the relationship map, and Export the Customers table to Excel for your model.

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FAQ

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Getting help

For help using Supply Chain or to talk through a counterparty-exposure workflow, reach AllMind support through the in-app support option or your account team. For a guided walkthrough, book a session from the Help link in the top navigation.