What it does

Food-quality systems generate many kinds of data: product master data, batch identifiers, transport events, temperature records, scanner measurements, inspection results, model outputs, shelf-life estimates, certificates and reports.

The difficult part is not only producing this data. The difficult part is making it usable across company boundaries.

A retailer, logistics provider, quality manager, supplier, auditor and consumer do not need the same information. They also do not use the same systems. A useful architecture therefore needs a common product identity, a shared event language and controlled access to different information layers.

GS1 EPCIS and GS1 Digital Link provide that foundation. EPCIS structures supply-chain events: what happened, to which object, where, when, why and under which business context. Digital Link connects product and batch identifiers to web-based resources that can resolve to different views: product information, event data, certificates, quality records, digital product-pass data or consumer-facing content.

Why it is hard

Supply-chain data is fragmented by design. Each actor has its own systems, responsibilities, commercial interests and data-sharing boundaries. Even when standards exist, real implementations still require detailed decisions: which identifiers are used, which events are recorded, which data is shared, which access rights apply, and how incomplete records are handled.

Food adds another layer of difficulty. Quality is dynamic. A product’s condition changes over time, and quality-relevant information may come from different sources: logistics events, temperature measurements, scanner readings, laboratory results, inspections or digital-twin calculations.

If these signals remain separate, they are hard to use operationally. A shelf-life model needs the product history. A quality manager needs the evidence behind a recommendation. A consumer-facing product pass needs a simple answer without exposing internal operational data. A professional user may need the full traceable record.

The expertise is in connecting these layers without losing meaning, trust or control.

What made the work substantial

In BioFreshCloud, MiGROS logistics events and temperature measurement events were transmitted via an OpenEPCIS repository into the BioFreshCloud solution. Inside the platform, incoming data was ingested, curated and transformed into product histories: the most plausible, gapless trace of a product from harvest or commissioning to the point of sale, including available quality-related data such as temperature, warehouse humidity and scanner events.

Those product histories then became inputs for shelf-life models. The model outputs were appended to create augmented product histories — records that combined traceability data with quality intelligence.

This is the central point: EPCIS-style traceability was not treated as an isolated compliance layer. It became part of the data foundation for quality modelling and operational decision support.

In FriDa, the same logic appeared at platform level. The project developed an open, decentralised data and service framework based on standardised interfaces. Sensors, scanners, event data and modelling services could be connected across different company IT structures. The work also showed a practical lesson: even standardised interfaces such as JSON-LD and GS1 EPCIS can require substantial coordination because standards often allow multiple implementation variants.

That experience matters. Interoperability is not achieved by naming a standard. It is achieved by making many small design decisions consistent enough that different systems can actually work together.

Digital Link as the access layer

GS1 Digital Link is important because it turns product identity into a web-accessible entry point.

A product or batch can carry a 2D code. When scanned, the same identity can resolve to different information depending on the user and context. A consumer may receive simple freshness, storage or product-pass information. A professional user may receive deeper batch, quality, event or certificate information. A system may request machine-readable EPCIS events or JSON-LD data.

This makes Digital Link a practical bridge between product identity, master data, event histories, certificates, ESG information, digital product-pass content and quality-model outputs.

The value is not the barcode itself. The value is the controlled routing of product-specific information to the right user, in the right format, at the right level of detail.

Why this matters

Food-quality automation becomes much more valuable when the results can travel.

A scanner measurement at goods receipt should not remain an isolated reading. A temperature event should not be lost in a logistics system. A shelf-life estimate should not live only inside a dashboard. A digital product pass should not be disconnected from the product’s real history.

By linking product identity, event data, quality measurements and model outputs, teams can create a more complete operating record:

• what product or batch was involved
• what happened to it in the supply chain
• what was measured
• which model or assessment was applied
• what result was produced
• which user received which information

For quality managers, this supports traceability and audit readiness. For supply-chain teams, it improves the usability of event and temperature data. For digital product-pass scenarios, it provides a route from internal evidence to external communication without exposing everything to everyone.

Where it is used

• Applications: FreshPass — digital product information and consumer-facing access · FreshIndex — freshness and shelf-life information linked to product identity · FreshForecast — model outputs connected to product history.
• Case studies: Dynamic Shelf-Life Dating / METRO Pilot — QR-to-cloud workflow for batch-specific freshness information · Smart Cold Chain / BioFreshCloud — OpenEPCIS data flows, product histories and shelf-life modelling · Digital Product Pass demo — differentiated access for consumers and professional users.
• Research projects: BioFreshCloud — OpenEPCIS-based event integration and augmented product histories · FriDa — open, decentralised data and service framework · FRED — edge-oriented food-supply-chain data infrastructure.
• Related expertise: Physics-Based Modelling & Digital Twins — model outputs that need structured product histories · Sensor Systems & IoT Deployment — field data that feeds the traceability layer.

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