In our previous post, we explored why AI-ready data is the indispensable fuel for Artificial Intelligence, ensuring accurate insights and unlocking true business value. We emphasized that AI models are only as good as the data they consume, requiring data that is accurate, clean, contextual, up-to-date, and accessible. But how do organizations, especially those navigating complex global supply chains, actually achieve this gold standard of AI-ready data?
The answer often lies in the adoption of global standards – and for supply chain traceability, none is more pivotal than GS1’s EPCIS (Electronic Product Code Information Services). As we have discussed in our EPCIS solution brief “The power of GS1’s EPCIS 2.0 standard”, a properly designed events-based traceability solution based on EPCIS is what companies need to gain the necessary visibility into their value chains to reduce friction, improve operational efficiency, and reduce waste. As we pointed out, event data differs from master data and transactional data since they can be captured through all key points in the supply chain and beyond. This not only requires a scalable events repository, but it also requires a lot of resources to make value out of the data. And this is, of course, where AI comes in. Ensuring that event-based traceability data is AI-ready through GS1’s EPCIS 2.0 standard is crucial for tomorrow’s resilient and intelligent supply chains.
Understanding EPCIS: The language of supply chain events
Before diving into its AI implications, let’s quickly recap what EPCIS is. EPCIS is a GS1 standard designed to enable trading partners to share detailed information about the physical movement and status of products as they travel through the supply chain. It captures “event-level data,” answering the four crucial Ws:
- What: What item (or group of items) is being described, e.g., a specific serialized product, a batch, a case, a pallet?
- When: When did the event happen (Timestamp)?
- Where: Where did the event happen (Location, e.g., facility, warehouse, truck)?
- Why: Why did the event happen (Business step, e.g., receiving, shipping, commissioning, packing)?
With EPCIS 2.0, a how was added to the equation, including capturing of sensor data like temperature and humidity using the EPCIS standard. This standardized, event-based approach transforms disparate operational data into a coherent, shareable narrative.
How EPCIS ensures AI-ready data in the supply chain
EPCIS inherently produces data that possesses the key characteristics of AI-readiness:
1. Accuracy and completeness by design:
- Granular capture: EPCIS mandates the capture of data at the event level, often tied to unique item identifiers (like serial numbers). This granularity reduces aggregation errors and data loss.
- Standardized and harmonized: By defining specific Key Data Elements (KDEs) and leveraging the Core Business Vocabulary (CBV), the EPCIS standard creates a universal language for all supply chain partners. This eliminates ambiguity and ensures that data is not only captured consistently but is also instantly understandable by any system—from a trading partner’s ERP to an AI model.
- Validation: Implementation often involves validation rules at the point of data capture, meaning data is “born” more accurate and complete, reducing the need for post-collection cleansing. With the right chain of critical tracking events defined and objects that can be uniquely identified, the EPCIS solution can serve as a secure gate to reliable, AI-ready data.
2. Inherently clean and structured data:
- XML/JSON format: EPCIS data is typically exchanged in standardized XML or JSON formats, providing a predefined, hierarchical structure. This inherent structure means AI models can “digest” the data much more easily, reducing the significant time typically spent on data structuring and transformation.
- Reduced ambiguity: The clear definitions of event types and KDEs reduce ambiguity, ensuring that “what happened” is consistently understood, making the data highly reliable for AI pattern recognition.
Imagine trying to build a house using a random pile of lumber, nails, and tools scattered everywhere. Now imagine building that same house with all the materials perfectly organized, sorted, and labelled. That’s the difference between raw data and EPCIS data.
3. Richly contextual and relevant:
- The 4 Ws: The What, When, Where, and Why form the core of an EPCIS event, providing the essential context for AI to understand the circumstances surrounding an event. This inherent structure builds a comprehensive, verifiable narrative of a product’s journey, allowing AI to analyze full lifecycle data for deeper insights into efficiency, risk, and sustainability.
- Complete the picture: For an even richer narrative, the EPCIS standard allows for the inclusion of supplementary data (the “how”). This can include sensor readings such as temperature and humidity, or other relevant information that provides a complete picture of a product’s condition and journey. AI models can leverage this information for tasks like predictive maintenance, quality control, and fraud detection.
4. Accessible, interoperable, and governed:
- Seamless data exchange: EPCIS was designed for interoperability. By using a globally recognized standard, companies can exchange traceability data with any trading partner that also implements EPCIS, regardless of their internal IT systems. This breaks down data silos and makes data from the entire supply chain accessible to centralized AI platforms.
- Trusted data flow: The standardized nature and often secure exchange mechanisms (e.g., using secure messaging layers) of EPCIS foster trust in the data’s integrity, crucial for AI models that rely on reliable inputs.
- Facilitates data lakes: EPCIS data feeds naturally into modern data lakes, providing a highly organized and readily available repository for AI training and real-time analysis.
5. Timeliness for dynamic AI:
- From Reactive to Proactive: Since EPCIS captures events as they happen, it enables a fundamental shift from a reactive to a proactive supply chain. Instead of learning about a problem after it has already occurred (e.g., a shipment is late), AI can analyze the real-time stream of data to anticipate disruptions. It can then automatically alert stakeholders or propose alternative actions before an issue escalates.
- Real-Time Decision-Making: The timeliness of EPCIS data is critical for AI to make dynamic, automated decisions. It empowers a system to do more than just make predictions. An AI can automatically reroute a truck based on a traffic event, trigger a quality check in response to a sudden temperature change, or even adjust inventory allocation across a network of stores based on real-time sales data.
- Enhanced Consumer Experience: Near real-time visibility also directly benefits the end consumer. Brands can provide accurate, up-to-the-minute updates on an order’s status, delivery time, and location, building trust and improving the overall customer experience.
The Future: A Self-Optimizing Supply Chain
The true power of this technology lies in moving beyond simple operational efficiency to achieving a self-optimizing supply chain. While dynamic AI can react to real-time events, the real strategic value comes from analyzing the complete, trustworthy data stream to identify systemic issues and weak spots. This enables AI to serve as a strategic consultant for your business, spotting patterns that would be invisible to the human eye. It can reveal, for example:
- Supplier Weaknesses: Which suppliers or carriers consistently have a higher rate of delays or damaged goods
- Logistics Bottlenecks: Which specific routes or warehouses are consistently less efficient
- Systemic Failures: Which product lines or manufacturing batches have a higher rate of quality control issues
And the intelligence doesn’t stop at the point of sale. Extending traceability to capture events like returns, repairs, and recycling provides brands with invaluable insights into how products are used and fail, directly informing future product design and durability. By capturing EPCIS events beyond the point of sale, brands can gain unprecedented insights that drive both customer loyalty and future product and process innovation.
By providing a single source of truth for the entire product lifecycle—from raw material to final recycling—EPCIS data gives AI the intelligence to not only react to problems but to uncover and help fix the root causes. This is the ultimate competitive advantage in a complex global market, transforming your products into a unified, intelligent, and continuously improving strategic asset.
Kezzler and the power of EPCIS for AI-ready data
At Kezzler, we specialize in product digitization and value chain traceability, and our solutions are built to capture, manage, and share event-based data at scale. We understand that effective traceability isn’t just about meeting regulatory mandates; it’s about transforming raw supply chain events into a strategic asset. By implementing our solutions, companies are not only gaining granular visibility but are also inherently building the robust, AI-ready data foundation required to power their next generation of intelligent operations and competitive advantage.
Ensuring your supply chain data is “born AI-ready” with standards like EPCIS isn’t just a technical task; it’s a strategic imperative for any organization looking to thrive in an AI-powered future.
You might also find this interesting
- Our blog on “The foundation of future innovation: Understanding AI-ready data”
- Our blog on “Why transformation events are the future of traceability”
- Our blog on “GS1 2D barcodes: the future of data sharing”
- A deeper dive into EPCIS with our solution brief “The power of GS1’s EPCIS 2.0 standard”