Implementing Digital Product Passports: DPP Software and Beyond

When the European Commission first introduced the Digital Product Passport proposal, most discussions focused on the concept itself: upcoming legislation, affected industries, and what the adoption timeline across the European Union would look like.

Now the discourse is becoming more operational. The Internet is full of explainers of what DPP is – but what starts to matter is how exactly to implement it in real operations. This is what this article is about: whether or not it’s about “setting up another software tool”, and at what points in this journey operational value for the company can be achieved.

What companies in the EU expect from a DPP solution

As the DPP adoption across the European Union is gradually moving along the timeline, companies are transitioning from questions like “what is DPP?” towards “okay, what do concrete solutions look like?”

The typical perspective is usually a pragmatic and focused one: compliance readiness, product traceability, alignment with EU legislation under the ESPR framework.

The fact that DPP is adopted in phases also allows both businesses and the software market to experiment with solutions. The sectors highlighted in the ESPR Working Plan (textiles, batteries, electronics) are expected to see some of the earliest large-scale implementation activity around 2026 or 2027. Pilot programs already exist – and tech startups are designing software tool MVPs to fill the niche.

The general assumption about a good DPP solution is that it will act as a system for extending traditional product labels with structured, dynamically updated digital information. It will allow:

• generating and managing digital product passports for individual products / product batches
• storing structured product data (materials, components, provenance information)
• tracking key sustainability and lifecycle attributes (like recyclability, repairability, or environmental impact metrics)
• enabling standardized access to product data for consumers, regulators, and supply chain stakeholders from physical products: QR codes, NFC tags, and other data carriers
• supporting compliance reporting aligned with EU regulatory frameworks as they mature

From this perspective, a “DPP solution” is often seen as a dedicated software layer that can be just added on top of the existing enterprise systems. The whole implementing process, then, is assumed to follow the familiar software rollout pattern: configuration, mapping data into the chosen structured format, etc. – until the actual launch with selected product lines or business units.

And, of course, there is an expectation that much of the complexity that comes with regulatory alignment and data structuring will be handled by the solution itself. This puts the nascent “DPP platforms” in the same category as PIM, LPM, or ESG reporting platforms.

Many of these expectations are justified, and that’s indeed what the technology industry is working on: abstracting away a large portion of regulations-related complexity and standardizing it until you can automate it.

However, as the first tools of the kind are implemented in practice (and we’ve been part of such projects), what becomes visible is how these expectations interact with the existing enterprise architecture, data availability, and, importantly, workflows within the organization. And that’s where things get really interesting.

Where implementation becomes more complex

The real central challenge is not deciding what the best DPP software is – when the market is mature enough, most platforms of this kind will provide functionality for generating and managing the passports, tying each to a unique product identifier and presenting product data.

The main body of work that can really “make it or break it” lies in connecting the solution to the reality of the actual product data landscape. Here are the main aspects that are tricky to solve within the tool itself:

1. Distributed product data across enterprise systems

In most organizations, the data required for a Digital Product Passport won’t be stored in a single system. It will be distributed across multiple environments, all of them originally built for their own operational purpose.

The typical setup can look something like this:

Which, in turn, means that no single system contains a complete “DPP-ready” dataset. For example, a textiles company might manage fabric composition in procurement spreadsheets, but store product descriptions in a PIM system, while supplier certifications are documents of their own.

As a result, the challenge is rarely about creating a digital product passport itself, but about assembling a consistent and reliable data foundation that can feed it.

2. Integration between the DPP software and existing systems

This is where things become more technically involved: the systems that the DPP platform is supposed to interact with were not designed for this kind of standardized transparency requirements.

This, in turn, means there’s some work to do:

• mapping inconsistent data structures across digital records and systems
• aligning product attributes and definitions
• building APIs or middleware connections
• synchronizing updates between source systems and the DPP layer

And once you want changes made in one system to be automatically reflected in the digital product passport, this requires custom integration logic – not a standalone app configuration.

3. Supplier data collection and external dependencies

Another key factor is that DPPs often rely on data that originates outside the organization. Which introduces additional complexity: collecting structured data from external suppliers, handling incomplete submissions, validating certifications, and importantly, maintaining the updates (because one-time imports become exhausting very fast).

In many cases, this results in having to introduce or update supplier portals – or standardize submission workflows so that the data is consistent.

4. Data ownership and governance across departments

The more tech revolves around data, the clearer it becomes just how much data issues are organizational in nature. The questions about data governance that need answering include:

– Who is responsible for maintaining product composition data?

– Who validates sustainability or lifecycle attributes?

– How are updates approved and propagated across systems?

– How do you resolve inconsistencies between departments?

This is important because since the data in DPPs comes from different sources, ownership boundaries and coordination mechanisms need to be compatible with the software logic (which makes assumptions about how things likely get done).

5. From software deployment to operational integration

This is why DPP implementation in real-world environments is not likely to just be about “buying licenses for a software tool”.

For example, a company may deploy a platform relatively quickly, but spend significantly more time aligning data across ERP, PLM, and supply chain partners. But this is where the actual value lies.

Why DPP integration matters as much as the DPP software itself

In practice, two organizations can deploy the same platform and end up with completely different outcomes depending on how the data moves through systems. In one scenario, this DPP layer becomes a full-fledged operational component, continuously updated and connected to ERP, PLM, procurement, supplier workflows and such. In another, it’s a static data layer.

This difference is determined not by the platform itself, but by the integration approach behind it.

No one wants just another platform to enter data in – if the EU is imposing Digital Product Passports, companies may as well benefit from this operationally and improve their efficiency. And this is where the core principle comes to surface:

A software solution is only as operationally useful as the system of processes and integrations that keeps it alive.

This is important because product data is not static. Specifications evolve, suppliers change materials, certifications expire, sustainability metrics get recalculated, regulatory expectations continue to mature. The DPP layer must continuously absorb changes.

For example, a textile manufacturer might have some stable format for material declarations that they use when onboarding suppliers. At a certain point, they discover that different regions or product categories now require additional sustainability attributes or traceability info. Who’s taking care of this: the integrations between systems, or a paid human employee?

This is why, in DPP initiatives, interactions are more than simply connecting systems. They determine:

• whether updates propagate automatically or require manual intervention;
• whether or not supplier data can be validated and normalized consistently;
• whether product changes remain traceable over time;
• whether the DPP layer reflects operational reality or only periodic snapshots;
• whether DPP data can later support broader ESG, repairability, or sustainability and circularity initiatives.

This is also the stage where organizations often rethink the scope. At this point, we can not just talk of “deploying another tool” but also of building future-proof operational capability around product transparency data.

The role of custom software and DPP implementation services

Like any specialized technology, DPP platforms will vary in scope and industry focus – and, of course, in flexibility. Some companies will be able to adopt them with relatively standard configurations, while others will require deeper adaptation to their data structures, supplier workflows and other criteria.

This is where the role of custom software partners is reassessed – it is not just about building and offering a software tool, but also connecting it or extending an existing one:

• building integration layers between DPP platforms and ERP, PLM, or PIM systems
• developing supplier-facing portals or structured data collection workflows
• creating middleware to normalize and synchronize product data across systems
• implementing validation logic for product, material, or sustainability attributes
• supporting data transformation where legacy structures do not align with DPP schemas

In some cases, this work is highly technical; in others, it is more about aligning business processes with how data needs to flow between systems.

In all cases, the most benefit comes from having partners who understand both sides of the challenge: software, on the one hand, and operational reality, on the other.

Digital Product Passport as catalyst for operational optimization

In general, the whole DPPs “epic” is a perfect example of how initiatives are introduced in response to regulations pressure, like the Ecodesign for Sustainable Products Regulation, but implementing them brings operational advantages in and of itself.

When done properly, of course.

We are seeing companies who work through these practical challenges of integrating, aligning, and coordinating the different pieces of the puzzle, and discover that the underlying issue is not so much DPP compliance readiness – but the overall fragmentation of product information. And they finally get to fix it.

This brings improvements in product data quality, better traceability and authenticity verification of materials and components, better alignment between departments (eventually saving money), higher readiness for ESG reporting – and importantly, the data infrastructure becomes tidier, meaning the next digital initiatives will take less effort in turn.

Not to mention that stronger transparency can also contribute to consumer trust.

These outcomes are not the result of a DPP tool being in place; they represent the upsides of integration and data alignment that DPP happens to nudge businesses towards.

What begins as a compliance-driven requirement can evolve into a more systematic approach to managing product data across its full lifecycle, from design and sourcing to use, repair, and eventual reuse or recycling.

Conclusions The companies that benefit most from DPP initiatives will probably not be the ones that simply deploy a compliance tool, but those that use the process to improve how product data flows through their systems, suppliers, manufacturing processes, and workflows as a whole.

Because ultimately, Digital Product Passports are not just about making product information accessible. They are about operationalizing it.

If your organization is currently exploring DPP implementation scenarios – whether from the software, integration, or workflow perspective – now is a good time to start mapping the existing product data landscape and identifying where the real operational bottlenecks are likely to emerge.