Supply Chain Data Products: Lessons from Global Industry Leaders

In the modern global economy, the old "just-in-time" supply chain model has been replaced by a much more complex reality. Today’s supply chain leaders are navigating a landscape defined by extreme volatility, from geopolitical shifts and climate events to rapid fluctuations in consumer demand. To survive, organizations are realizing that data is no longer just a byproduct of their operations—it is the very engine of their resilience.

However, most companies still struggle to harness this power. Data often remains trapped in siloed ERP systems, warehouse management platforms, and third-party logistics feeds. When data is fragmented, it leads to the "bullwhip effect," where small changes in demand cause massive, costly ripples upstream.

The solution lies in a fundamental shift in strategy: moving from managing data as a project to managing data as a product. As McKinsey notes, "Scale and value come from treating a data product like an engine that can support a large number of high-value use cases (or cars)." By treating data as a product—curated, governed, and ready for consumption—supply chain leaders can transform their operations from reactive to predictive.

Why supply chain leaders are shifting to a data product mindset

For years, organizations have invested heavily in data lakes and warehouses, only to find that the data remains difficult to find, trust, or use. At Alation, we believe that effective data products combine value, trust, and governance to drive measurable impact.

Think of a data product like a ready-to-eat meal. Raw data is like the individual ingredients—flour, eggs, and water. On their own, they aren't very useful to a hungry consumer. A data product is the finished meal, complete with a "nutrition label" (metadata and governance) and a "recipe" (a reusable framework). It is designed for a range of consumers, who can analyze (or “chat”) with this data product in order to answer their unique questions and support a range of use cases.

McKinsey highlights that this approach is essential for scaling value, stating, "A data product delivers a high-quality, ready-to-use set of data that people across an organization can easily access and reuse for a variety of business opportunities." In the context of the supply chain, this means moving away from one-off reports and toward durable data assets like a "Supplier Risk Scorecard" or an "Inventory Optimization Dataset."

The 10 attributes of a high-impact supply chain data product

To ensure these "meals" are actually consumed and deliver value, Alation has identified 10 key attributes for faster business impact. In the supply chain, these attributes act as the bridge between raw signals and strategic decisions.

1. Valuable: Start with the business outcome. Whether it’s reducing stockouts or lowering carrying costs, a data product must have a clear ROI.

2. Discoverable: If a planner can’t find the data, it doesn’t exist. A searchable marketplace is essential for reuse.

3. Linked to a data contract: This defines the "SLA" of the data, ensuring producers and consumers are aligned on quality and frequency.

4. Understandable and addressable: Clear documentation and global identifiers allow a SKU to be tracked consistently across different systems.

5. Trustworthy: Users must know where the data came from (lineage) and who is accountable for it (stewardship).

6. Accessible: Value is realized when users can easily pull data into their specific tools, whether it’s an ERP or an AI model.

7. Reusable: A data product should be built once and used many times—for example, using the same "Shipment Feed" for both customer service updates and carrier performance reviews.

8. Composable and interoperable: In a complex network, data products must "speak the same language" to work together across domains.

9. Secure: Built-in security ensures that sensitive supplier or pricing data is only seen by those with the right permissions.

10. Globally unique: Every data product should have a stable, unique identity to prevent duplication and confusion.

As McKinsey points out, "The goal of developing data products isn't to generate better data; it's to generate value. No data product program should begin until leadership has a firm grasp of the value that each use case can generate and prioritized the biggest opportunities."

Five essential supply chain data products for 2026

To help you move from theory to practice, here are five high-impact data product use cases that are transforming supply chain operations today.

1. The inventory optimization dataset

This product provides dynamic visibility into inventory levels, demand forecasts, and replenishment schedules across the entire network.

• Business value: By dynamically calculating safety stock and reorder points, a multinational distributor could potentially reduce excess inventory by 20% or more, freeing up millions in working capital while maintaining high fulfillment rates.

• Key inputs: ERP transactions, demand forecasts, and POS data.

2. The supplier performance and risk scorecard

This data product continuously assesses supplier delivery, quality, and geopolitical exposure.

• Business value: A global manufacturer might use these scorecards to identify that 10-15% of their suppliers are exposed to emerging risks, allowing them to pre-qualify alternative vendors and potentially prevent tens of millions in lost quarterly revenue.

• Key inputs: Purchase orders, credit reports, and ESG audits.

3. The transportation and logistics visibility feed

This feed provides real-time tracking and predictive ETAs for shipments, helping optimize carrier selection and route planning.

• Business value: As an example, a distributor might use this feed to proactively re-route shipments around weather delays, reducing late deliveries and saving millions annually in avoided air freight expediting costs.

• Key inputs: IoT sensors, carrier feeds, and weather data.

4. The demand forecasting model output

This product predicts future product demand based on historical trends, promotions, and real-time market signals.

• Business value: It minimizes the "bullwhip effect" and improves on-shelf availability. A CPG manufacturer could theoretically reduce forecast error by over 20% by integrating POS data into this product, significantly cutting finished goods inventory while maintaining peak-season availability.

• Key inputs: Sales history, market trends, and economic indicators.

5. The supply chain disruption monitoring and alerting product

This "control tower" product monitors global events—like port strikes or floods—and maps them against the company's supplier locations.

• Business value:  Imagine an electronics manufacturer detecting a flood near a key factory. With this product, they could activate alternative sourcing plans within hours, potentially avoiding millions in lost sales due to production shutdowns.

• Key inputs: News feeds, port congestion reports, and supplier site mapping.

Proof in practice: How global leaders use data products for smarter supply chains

Building these products requires a foundation of trusted metadata and clear governance. Several global leaders are already using Alation to turn their supply chain data into a strategic asset.

Lipton: Creating a smarter supply chain

For Robin Rietveldt, Global Director Data & AI, LIPTON Teas and Infusions, the goal is moving from searching for data to solving problems with agentic AI. Managing a $2 billion business requires a lean supply chain, which Rietveldt supports through a solid data foundation. “Once you’ve chosen use cases, you need to build them on top of the data foundation, and it needs to be very solid,” he explains.

At Lipton, the supply chain is the proving ground for AI. By treating data as a governed product, they have moved beyond dashboards toward "agentic" systems. For example, frontline factory workers can now use digital assistants to troubleshoot machinery instantly, and digital twins simulate production to "repair machines before they break."

Rietveldt’s strategy involves "meeting users where they are" by integrating Alation’s metadata directly into Power BI. This ensures transparency regarding data sources and refresh schedules. He likens this constant governance to "making your bed"—an essential daily discipline for success. This foundation allows Lipton to scale self-service analytics and prepare for a future driven by AI agents. 

Discover how Lipton builds a smarter supply chain with Alation.

Daimler Trucks North America: AI agents and the future of manufacturing

In the intricate world of heavy-duty manufacturing, Édgar Gallo, Head of Chief Data Office at Daimler Trucks North America (DTNA), views data as more than just information—it is the fuel for a global economy where nearly every physical good is moved by a truck. Since partnering with Alation in 2019, DTNA has transitioned from merely cataloging data for compliance to activating it as a strategic engine for innovation.

The turning point for DTNA arrived when they realized that documented data alone wasn't enough to support a 900-person data team. They needed to move beyond "data unknown" to a state of readiness where information could actively shape decisions on the factory floor. 

Combating the bullwhip effect with AI agents

One of the most unforgiving challenges in manufacturing is the "bullwhip effect," where minor shifts in consumer demand create massive, destabilizing swings in the supply chain. To build resilience, DTNA is developing sophisticated AI agents that act as early-warning systems. These include "vertical" agents designed to think like supply chain planners and "horizontal" agents capable of executing specific tasks.

These agents allow the workforce to shift their focus. Instead of spending hours manually scouring siloed systems to find data, employees act as partners to their vendors, focusing on resolution plans and strategic oversight. The goal is to move the human element of the supply chain away from data digging and toward high-level problem-solving.

The core philosophy driving DTNA’s success is a simple but powerful realization: “No metadata, no AI.” While large language models often capture the headlines, Gallo emphasizes that without high-quality, well-documented metadata, AI is little more than noise. Metadata provides the essential context that makes AI agents trustworthy and accurate enough to guide industrial operations.

Discover more about Daimler Truck’s use of AI agents and metadata.

Brambles: Governing CDEs

Brambles, a global leader in the circular economy, manages the complex flow of millions of pallets and containers across the world. To maintain operational excellence, they have turned to Alation to govern their Critical Data Elements (CDEs). These are the essential data points—such as customer IDs, asset locations, and transactional records—that allow the business to function, comply with regulations, and make high-stakes decisions.

It is important to note the distinction between CDEs and data products. While a data product is a complete, "ready-to-use" asset designed to solve a specific business problem (like the Inventory Optimization Dataset), CDEs are the high-priority ingredients that power those products. Think of a CDE as a "VIP" data point; if a CDE like a "SKU Identifier" is inaccurate, every data product that uses it becomes untrustworthy. By identifying and prioritizing these specific elements, Brambles ensures that governance efforts are focused on the data that has the most significant impact on their supply chain.

By using Alation to catalog and define these CDEs, Brambles has moved away from the challenge of fragmented data silos. This approach allows them to establish clear ownership and implement strict data quality controls where they matter most. The result is a foundation of trusted, high-quality data that fuels their circular economy model and ensures their assets are always where they need to be.

Read more about how Brambles uses CDEs to govern supply chain data.

The blueprint: How to build your first supply chain data product

Scaling a data product program isn't just about the technology; it's about the economics and the people. As McKinsey argues, "The value of a data product comes from the steady reduction in incremental costs achieved from reusing it and the acceleration in capturing the value of each additional use case."

Here is a four-step blueprint to get started:

Step 1: Identify the business outcome

Don't build a product because you can; build it because it solves a problem. Focus on a high-value use case, like reducing stockouts. Work with dedicated data product managers who liaise with both data and business teams, aligning needs. Per McKinsey, "Put in place empowered data product owners (DPOs) and senior data leaders who understand what matters to the business."

Step 2: Map the “ingredients”

Identify the data sources needed—ERP, WMS, or external risk feeds. Use a data catalog to understand where this data lives and who owns it.

Step 3: Define the "nutrition label"

Document the metadata, lineage, and data contracts. This ensures that when a planner uses the data, they know exactly what it represents and how often it’s updated. McKinsey reminds us that "Data without data products is like oil without refineries: There is little value in the raw form."

Step 4: Make it discoverable

Place the finished product in a data products marketplace. This allows other teams (like Finance or Sales) to find and reuse the product for their own needs, creating the "flywheel effect" of increasing value and decreasing costs.

From data products to agentic AI: Preventing supply shortages

The true power of a data product is realized when it is consumed by an AI agent. An AI agent is more than a chatbot; it combines AI with automation to make decisions and complete tasks with minimal human intervention.

In the healthcare sector, running out of critical medical supplies—whether PPE or surgical equipment—can disrupt care and compromise patient safety. Traditional, manual inventory management is labor-intensive and prone to error.

Demo: The medical supply chain agent in Agent Studio

Here is a look at how agentic AI, powered by Agent Studio, offers a smarter alternative for medical suppliers.

In this demo, our team models a typical medical-supplier scenario: ensuring hospitals and clinics don’t run out of critical items. We built a custom “Low Supply” agent that brings together three components:

• A Large Language Model (LLM): Provides reasoning and interprets complex instructions.

• A system prompt: Defines the business logic (e.g., "detect items below reorder point, prioritize critical items").

• A set of tools: Allows the agent to query real inventory data and fetch schema details.

The agent runs autonomously to query data, apply logic, and generate a summary of at-risk supplies. By hooking this agent into an automated workflow, it sends proactive alerts to stakeholders with vendor contact information and a clear call to action. For healthcare organizations, this provides consistency, speed, and trust—ensuring that agents operate on governed data and well-defined business logic.

Conclusion: Data products as the foundation for the AI era

The transition to data products is no longer optional. As organizations move toward AI-driven supply chains and digital twins, the need for trusted, interoperable data products becomes even more critical. AI models are only as good as the data they consume; without the structure and governance of a data product mindset, even the most advanced AI will fail to deliver results.

By adopting Alation’s 10 attributes and focusing on high-value use cases like inventory optimization and supplier risk, you can transform your supply chain from a cost center into a resilient, value-driving engine.

As you look toward 2026, ask yourself: Are you managing data as a series of disconnected projects, or are you building the products that will power your future?

Ready to start your data product journey? Download the Alation Data Product Blueprint or request a demo today to see how we help the world’s leading supply chains turn data into a competitive advantage.