How do you extend a PIM data model for the Digital Product Passport?

To prepare a PIM for the Digital Product Passport (DPP), brands should add structured fields for identifiers, material composition, origin per production stage, economic operators, care and repair, and end of life, and record for each field where the value came from and who owns it. The textile requirements are not final yet: the European Commission plans to adopt the delegated act for textiles in Q4 2027, so the aim now is a flexible model rather than a fixed checklist.

What is the Digital Product Passport and when does it apply to textiles?

The DPP is established under the Ecodesign for Sustainable Products Regulation (ESPR), Regulation (EU) 2024/1781. Each product group gets its own requirements through a delegated act. For textiles and apparel, the Commission's DPP page lists the planned adoption of the ESPR delegated act in Q4 2027, followed by guidance and technical specifications, and stresses that timelines may evolve.

The infrastructure is already being built. The Commission announced that the DPP Registry went live on 20 July 2026. The registry holds unique product identifiers and associated metadata, while the product data itself remains decentralised: economic operators keep it in their own systems and register each passport. The Commission's overview also explains that physical products link to their passport through a data carrier such as a QR code, and that access rights differ for consumers, repairers and recyclers, authorities and economic operators.

Which data will the textile DPP likely require?

The Commission lists the information categories the textile DPP is expected to make accessible, while noting that the exact requirements will be defined by the delegated act and technical specifications. Mapping those categories against a typical fashion PIM shows where the gaps are.

Read also

Textile Digital Product Passport timeline: what is decided and what is open in 2026

How should the PIM data model be extended?

A practical extension follows six steps, designed so that the model can absorb the final delegated act without a rebuild.

1. Fix the identifier logic. Decide how the DPP identifier relates to existing keys (style, colour, size GTIN) and keep the option of a lower granularity open, since the level required for textiles is not yet confirmed.
2. Structure composition. Replace free text such as "95% cotton, 5% elastane" with repeatable fields: component (shell, lining, trim), fibre, percentage, recycled or certified share. Validate that each component adds up to 100 percent.
3. Model the supply chain as entities. Store production stages (spinning, weaving or knitting, dyeing and finishing, making up) with country and supplier references, rather than a single country of origin field.
4. Separate audiences. Tag each attribute with its intended audience (consumer, repairer or recycler, authority), reflecting the access levels the Commission describes.
5. Add evidence and ownership. For each DPP relevant field store source document, date, responsible team and verification status. This is what makes a value defensible.
6. Plan the link to the carrier. Define how a QR code or other carrier will resolve to the passport, and which system serves the data.

Where does the data come from?

Most DPP data does not originate in the PIM. Composition and bill of materials sit in PLM, supplier and facility data in sourcing tools or spreadsheets, certificates in supplier portals and care information in the labelling process. The PIM extension therefore depends on integrations and on supplier cooperation.

Industry bodies are working on shared definitions. GS1 Netherlands publishes a fashion data model for exchanging product information, built on the Global Product Classification, and reports that it is being adapted for international DPP use through a GS1 working group for textiles and apparel launched on 27 May 2026. GS1 UK frames the main challenge as agreement on what information to share and how to structure it, rather than technology, and points to GTINs and GS1 powered QR codes as building blocks. Aligning internal attribute names with such shared models reduces mapping effort later.

How can AI help, and where are the limits?

The DPP creates a large volume of document heavy data work, which is where AI tools are genuinely useful:

• Document extraction: reading supplier declarations, test reports and certificates and proposing values for composition, facility names or certificate numbers.
• Consistency checks: flagging composition that differs between label text, PLM bill of materials and webshop copy.
• Gap analysis: listing styles with missing origin stages or unverified fields ahead of a deadline.
• Drafting guidance texts: producing care and repair instructions from structured attributes for human review.

The limits are as important. Passport information may be used by authorities for compliance checks, so a value extracted by a model is a proposal until someone verifies it against the source. Extraction quality depends on document quality, and suppliers' documents vary widely. Brands should log which values were machine proposed, who approved them and when.

Read also

Sharing sustainability data with retail partners: a practical guide

What should brands do in the next twelve months?

• Run a gap analysis of current PIM and PLM fields against the Commission's expected categories.
• Pilot structured composition and multi stage origin on one product category.
• Agree data ownership between product, sourcing, sustainability and e-commerce teams.
• Follow the delegated act process and GS1 working group outputs, and avoid hard coding assumptions about granularity or carriers.

None of this requires waiting for the final text. A clean, evidenced data model also improves label accuracy, marketplace listings and customer service today, which makes the investment easier to justify before the obligation applies.