Implementing DPP Technically—Data, Standards, and Systems

With the Digital Product Passport, the QR code is just the tip of the iceberg.

The real challenge lies beneath the surface: product information must be uniquely identified, consolidated from different systems, and made available in an interoperable manner across organizational boundaries.

The key question, therefore, is not, “What DPP software do we need?” but rather:

What technical architecture will remain scalable for additional product groups and requirements?

What the Architecture Must Achieve

The ESPR sets forth essential technical principles. DPP data must be accurate, complete, and up-to-date. Digital Product Passports must be technically, semantically, and organizationally interoperable. Access rights may vary depending on the user group.

The specific data required for a product group is determined by the relevant legal act. The architecture should therefore separate stable core functions from variable domain-specific data fields.

From the Source System to the Product Passport

A scalable architecture can be conceptualized in four layers.

Data Sources:

ERP, PIM, MDM, PLM, MES, and other systems provide product, material, production, or supplier data

Data and identity layer:

Products, models, batches, or individual items require unique identities. At the same time, it must be clear which system is the primary source for each data object.

Integration layer:

APIs and standardized exchange mechanisms connect internal sources, suppliers, and external DPP services. Here, the DPP logic should not be replaced by manual data copies.

Provisioning:

Data carriers link the physical product to its digital passport. The EU DPP registry does not simply store the complete product passport centrally but serves, in particular, as an index for identifiers, registration information, and metadata.

DPP Platform or Existing Systems?

A DPP does not automatically mean that all product data must be migrated to a new platform.
A federated architecture can make strategic sense: Key information remains in appropriate source systems, while a DPP layer orchestrates data, controls access, and handles standards-compliant provisioning.

This leads to three architectural decisions:

Data sovereignty: What information must be controlled within the company?
System responsibility: Which system is the authoritative source for which data object?
Decoupling: Can the DPP service or provider be changed without having to rebuild the entire product data landscape?
This last point is particularly important because the ESPR is geared toward open standards, interoperability, and data exchange without vendor lock-in.

First Data Architecture, Then the DPP Front End

A well-designed pilot project therefore does not begin with the design of a product passport. Companies should first review product identities, data sources, data quality, interfaces, and responsibilities.
Only then should they consider which service will be used to provide the data. In this way, a single DPP project can evolve into a reusable infrastructure for additional product groups.

architectural and data management task. European standards now provide concrete technical guidelines for identification, data carriers, interoperability, APIs, exchange, and storage.
Companies should leverage these foundations without tying their architecture to specific, as-yet-unresolved product requirements or to a single vendor. A robust product data foundation—on which various DPP use cases can be built—is crucial.

FAQ

What DPP standards are already in place?
Six European standards cover, among other things, identifiers, data carriers, interoperability, data exchange, storage, and APIs. Two additional DPP standards are expected to follow.

Does a company need its own DPP platform?
Not necessarily. Existing systems can remain data sources. The key is that data can be integrated and made available in a manner that complies with the standards

Is all DPP data stored in the EU registry?
No. The registry stores, in particular, identifiers, registration data, and high-level metadata. The DPP itself generally follows a decentralized approach.

What should a DPP pilot test first?
Product identification, data provenance, interfaces, data quality, and updates should be tested in practice before a broad technical rollout.

How much of your existing IT infrastructure is already DPP-ready? A technical DPP readiness check with asioso can evaluate data sources, interfaces, and the target architecture and identify specific integration steps.