It is tempting to think of a Digital Product Passport as a QR code stitched into a care label. It is not. The QR code is only the door. Behind it sits a structured, machine-readable record of product data: fibre composition, durability, recyclability, recycled content, substances of concern, producer information and more. (source: Circularise)

The hard part is getting that data and proving it is correct. The “Study on DPP content for textile apparel products under ESPR”, dated 13 May 2026, gives apparel brands, EU importers and textile exporters an early operational roadmap for DPP readiness. The study should not be treated only as a regulatory document; it should be used to prepare supplier data, product identifiers, access rights, verification workflows and tiered supply-chain visibility before the textile delegated act becomes legally binding.

Easier said than done of course. The study explains that brands usually know their Tier 1 factory, which is the final assembly site. Far fewer can trace the full story behind the fabric (Tier 2), the yarn (Tier 3) and the raw fibre or chemical inputs (Tier 4). Upstream suppliers often keep this data in unstructured files and treat it as confidential.

Solvira, a German company that helps textile exporters, EU importers and apparel brands translate the emerging DPP framework into supplier data maps, readiness audits and phased implementation plans, wrote an analysis of this JRC study at "The May 2026 Textile DPP Study: A Phased DPP Readiness Roadmap for Apparel Brands, Importers and Tiered Supply Chains". Here is their view on the different supplier tiers, and the 6 practical steps to get the information:

It also separates two ideas that are easy to mix up. Supplier tiers tell you where the data comes from. Granularity tells you at what level the data is stored, at model level, batch level or item level. For most apparel firms, model level is the place to start, with batch level added where testing or sourcing changes from one production run to the next.

To deal with this, the study sets out 6 practical phases: monitor the rules, map which products are in scope, take stock of the data you already hold, trace the missing supplier data across tiers, design who can see which data, and run a pilot on one product family before scaling up. To deal with this, the study sets out 6 practical phases: monitor the rules, map which products are in scope, take stock of the data you already hold, trace the missing supplier data across tiers, design who can see which data, and run a pilot on one product family before scaling up.

Further analysis of the impact on e.g. supply-chain responsibility and granularity (the model vs batch vs item dilemma) can be found in the Solvira article.