Case Studies - opensupplyhub/supplychaindata.exchange GitHub Wiki

Case Studies

This page highlights real-world examples of how organizations have implemented the SC-DEX standard. These case studies showcase different use cases, challenges, and outcomes to help others understand the practical value of using an open, interoperable supply chain data model.

Whether you're exploring SC-DEX for the first time or looking for proven approaches to implementation, these stories provide insight into how SC-DEX can support transparency, compliance, and collaboration across tiers and sectors.

Case Study: Multi-Tier Supplier Mapping with Matrix Footwear Using SC-DEX

How one brand used an open data standard to see the tiers of its supply chain

In early 2025, Matrix Footwear became our first brand to pilot the Supply Chain Data Exchange (SC-DEX) Standard. Their goal? To map their supply chain beyond their Tier 1 suppliers and build a repeatable process for transparency that could eventually trace their end-to-end supply chain.

At the start, Matrix had their Tier 1 suppliers listed on Open Supply Hub, as well as a spreadsheet. They hadn't yet mapped their Tier 2 network, which made it difficult to assess sourcing risks, have visibility into Tier 3 and beyond, or prepare for upcoming regulatory changes, such as the EU's Omnibus.

With a commitment to staying ahead of compliance requirements and aligning with best practices in ethical sourcing, Matrix worked with the SC-DEX team to create a repeatable process for tracing their supply chain with a lean, simple data schema. The result was a map of more than 30 Tier 2 suppliers, introduced a practical framework for continued supplier tracing, and highlighted the importance of standardized data in real-world supply chains.

Starting Point: Tier 1 and a Spreadsheet

Matrix’s internal records consisted of a list of four Tier 1 manufacturing partners. These partners are direct suppliers to the brand and typically represent the portion of the supply chain most brands are familiar with. Beyond Tier 1, however, visibility sharply declined. Matrix had no formal system for tracing or managing Tier 2 suppliers, let alone Tier 3.

The team’s objectives:

  • Trace the network of Tier 2 suppliers connected to its Tier 1 partners
  • Create a repeatable process to reach even deeper tiers in the future
  • Prepare for anticipated due diligence requirements

The SC-DEX Approach: Simple, Open, Interoperable

Matrix used the SC-DEX core schema to structure and exchange supplier data. The schema captures three key data types:

  • Locations (e.g., factory addresses)
  • Organizations (e.g., company names, identifiers)
  • Affiliations (e.g., "is supplier of" relationships)

The team began by distributing a simple data template to each Tier 1 supplier. Each was asked to provide details about their own suppliers (Tier 2), including names, addresses, and—if known—any organizational identifiers.

We also asked for contact information (email or phone number) to be able to then repeat the ask for Tier 3 and beyond.

One supplier declined to share this information at the time. This was completely acceptable within the SC-DEX model, which is designed to also work with closed or permissioned data. Even privately held data may be formatted in SC-DEX so that, if made open in the future, it’s immediately interoperable without requiring reformatting.

From Spreadsheet to Schema: A Seamless Conversion

Once supplier data was collected, it was easily converted from spreadsheet format into SC-DEX’s machine-readable JSON format using a CSV-to-JSON converter tool developed by Vasiliki Gkatziaki. This minimized the manual work typically associated with formatting data and ensured accuracy and consistency.

CSVtoJSONconverter

The output was a clean, extensible dataset that mapped over 30 Tier 2 supplier locations.

Exponential Mapping: Four Becomes Thirty-Four (and Growing)

Though Matrix began with only four Tier 1 suppliers, those suppliers revealed at least 34 distinct Tier 2 suppliers. In many cases, Tier 2 suppliers themselves had even larger upstream networks, meaning the mapping effort has the potential to expand exponentially with each new tier traced.

Because the SC-DEX template also encouraged Tier 1 suppliers to provide contact information for Tier 2s, the process is easily repeatable, in order to extend mapping further upstream, eventually resulting in full end-to-end mapping.

Identifier Bridging in Practice: From USCC to OSID

One key learning from the pilot was around organizational identifiers. While SC-DEX supports global identifiers like the Legal Entity Identifier (LEI), few of Matrix’s suppliers had LEIs. Most were based in China, so the team pivoted to using the Unified Social Credit Code (USCC)—China’s national identifier system for registered companies. To locate USCCs, the team requested supplier names again, but in Chinese.

Simultaneously, the team uploaded the location data to Open Supply Hub to obtain OSIDs—unique universal facility identifiers. With a location and organizational identifier established for each supplier, each USCC could then be linked through the JSON schema to an OSID, providing a bridge between geospatial and organizational datasets.

This cross-referencing of identifiers demonstrates one of SC-DEX’s most powerful features: the ability to map identifiers across systems, creating a foundation for integration, auditing, and analysis.

Interoperability by Design

Another major takeaway: the SC-DEX format is not only lightweight—it’s interoperable.

Now that Matrix has clean, structured supply chain data in SC-DEX format, they can:

  • Export it into other formats for audits or platform integrations
  • Easily share it with partners or researchers
  • Extend the data with additional fields for sustainability, wages, or certifications

This flexibility ensures that the investment in mapping and cleaning data continues to provide value—regardless of future reporting requirements.

Reflections and Key Takeaways

We were so excited when Matrix Footwear approached us with this pilot and it validated what we hoped for SC-DEX during its design phase. Here's a quick recap of what we learned:

  • Repeatability: With the contact information of Tier 2 suppliers collected, Matrix can now trace additional tiers using the same simple process.
  • Scalability: The jump from 4 to 34 suppliers highlights the exponential nature of supply chains—and the need for scalable solutions.
  • Accessibility: A supplier’s choice not to share data didn’t break the system; SC-DEX accommodates partial, closed, or delayed data.
  • Identifier Mapping: The process of linking USCCs to OSIDs shows how SC-DEX supports robust, multi-layered data integration.
  • Tooling: Tools like the CSV-to-JSON converter made adoption fast and smooth.
  • Futureproofing: The standardized format means Matrix’s data is now easier to update, audit, and share as regulatory demands evolve.

Interested? Try Multi-Tier Mapping with SC-DEX

Matrix Footwear’s pilot shows that multi-tier supplier mapping doesn’t have to be difficult. With the right structure and tools, even small teams can achieve deep visibility—and build a foundation for future compliance and impact.

If you're interested in piloting SC-DEX to map your supply chain, check out the SC-DEX Implementation Guide or get in touch with the team.

Case Study: Cross-Organizational Civil Society Supply Chain Data Exchange

How three civil society organizations tested SC-DEX to align thousands of data points across location, organization, and affiliation data in Bangladesh

In early 2025, Open Supply Hub, Mapped in Bangladesh (MiB), and Wikirate teamed up to test the SC-DEX data exchange standard by running a cross-organizational experiment. The goal? To see if a shared open data schema could align thousands of data points across their separate systems and enable easy collaboration without requiring complex technical integrations.

This was a powerful early test of SC-DEX's core promise: to make it easier for organizations to exchange, link, and make use of supply chain data—whether they held location data, labor indicators, or legal information.

The Experiment

Three organizations, three datasets:

  • Open Supply Hub (OSH) provided a snapshot of 11,785 production locations in Bangladesh that had been registered in their platform.
  • Mapped in Bangladesh (MiB) contributed data from 3,322 verified factory profiles, including additional fields on certifications, labor standards, and working conditions.
  • Wikirate cross-referenced each of these data points to organizational identifiers (mostly LEIs and OpenCorporates URLs) and relationship affiliations across brands and suppliers from their database.

After data cleaning and matching, 2,194 facilities were successfully mapped using the SC-DEX core schema. Each facility had:

  • a location identifier (OSID),
  • an organization identifier (LEI or OpenCorporates),
  • an affiliation linking it to a brand or other organization.

Step-by-Step: From Data Collection to Alignment

This experiment followed a basic but effective structure:

1. Data collection
Each organization exported its relevant data and shared it into a secure shared folder. Each dataset used a separate schema unique to the organization it came from.

2. Schema alignment
The teams reviewed which fields matched SC-DEX fields. For instance:

  • With its unique OSID, OSH data was already oriented around the location-id entity.
  • Wikirate’s brand-supplier links informed affiliations and organization-id.
  • MiB’s additional factory attributes were proposed as extensions.

3. Data cleaning
Inconsistencies across datasets were flagged. For example, one location might have different spellings of a facility name, or address fields might be structured differently.

4. Identifier mapping
OSIDs were used for all location IDs. Wikirate’s database included LEIs or OpenCorporates to serve as organization identifiers.

5. Formatting into SC-DEX
A shared set of JSON files was created, following the SC-DEX structure of locations, organizations, and affiliations. These JSON files were validated and used as the reference outputs.

Key Learnings

We wanted to test out whether or not the SC-DEX schema worked for small-scale data exchange and realized that the simplicity of location—organization—affiliation as the core data shared allowed the flexibility for more complexity. We also learned that:

  • Cross-referencing across systems works
    By aligning around a common schema, the teams were able to spot inconsistencies—then resolve them once, for all three systems.

    “I’d give an arm and a leg to do this lookup and mapping across companies. The formats are always inconsistent.” —Civil Society Partner

  • Data sharing across organizations was easier
    Once each dataset was converted into SC-DEX format, the extended fields (like MiB’s certifications or Wikirate’s relationships) became attachable. For example:

    • MiB’s certification data could be linked to OSH’s OSIDs.
    • These OSIDs were also mapped to org identifiers like LEIs or OpenCorporates from Wikirate’s dataset.

We're excited about what this points towards. The exchange experiment highlighted the importance of shared formats. Without a shared schema and shared identifiers, reconciling and comparing datasets was a time-consuming manual task. While migrating from one schema to another is not easy, migrating all to the same schema first is the pre-work that makes matching across datasets significantly easier. With SC-DEX in place, a single update could potentially flow back into each database without requiring a different format for each organization.

It also signals to us that as the technical standard is in place, the next phase for SC-DEX is growing into a shared network. If this experiment validates that the SC-DEX format works for data sharing across organizations—the real opportunity ahead is to build an ecosystem around it, where a network of supply chain organizations can find each other and share or request information.

Looking Ahead: Supporting Temporality in Supply Chain Data

One key insight that emerged from this exchange was the need for temporal data handling in supply chain systems. The current SC-DEX schema is intentionally lightweight and simple, but it does not yet include native support for time-related data fields.

As organizations like MiB, OSH, and Wikirate continue to collect and publish time-sensitive information—such as certifications that expire, changes in supplier relationships, or audit timelines—there is increasing interest in features like:

  • Timestamps: When a facility was added, edited, or verified.
  • Historical records: Retaining versions of a facility's profile over time.
  • Time-bounded affiliations: Capturing when a supplier relationship began or ended.
  • Temporal queries: Asking what a supply chain looked like at a specific date.

While these needs may be addressed through optional extensions to the core schema, their recurrence across multiple organizations suggests that temporality may deserve deeper consideration in future versions of SC-DEX. As the standard matures, integrating temporal concepts could greatly improve traceability, auditing, and historical accountability across the ecosystem.

Want to Try It?

If your organization has supply chain data and is interested in mapping it across others—without spending weeks cleaning spreadsheets—get in touch.

You can:

SC-DEX isn’t just a format. It’s an invitation to build a better way to share supply chain data—together.