AICA Data Addresses Data Gaps for EU Digital Product Passports

Atlas helps manufacturers, distributors and passport platforms classify and enrich product data ahead of EU Digital Product Passport requirements.

LONDON, NORTH YORKSHIRE, UNITED KINGDOM, September 16, 2026 /EINPresswire.com/ -- AICA Data has outlined how its Atlas classification engine helps organisations prepare product data for European Union Digital Product Passports (DPPs). The company addresses inconsistent classifications and incomplete attributes in product catalogues, helping manufacturers, distributors and passport platforms organise the information needed before a passport can be populated. Its approach focuses on data held across business systems and supplier feeds.

The European Commission launched the EU Digital Product Passport Registry on 20 July 2026. From 18 February 2027, Regulation (EU) 2023/1542 requires passports for electric vehicle batteries, light means of transport batteries and industrial batteries with a capacity greater than 2 kilowatt-hours when placed on the EU market or put into service.

Further product groups, including iron and steel and textiles, are expected to follow under the Ecodesign for Sustainable Products Regulation (EU) 2024/1781. Requirements and application dates will be set through product-specific rules. The information required in a passport will therefore depend on the product category and the legislation that applies to it.

AICA offers a free assessment of an organisation's current product classification status. It produces a sample-based report comparing data before and after classification, identifying coverage gaps, gaps against relevant standards and the estimated work required. Details are available at aicadata.com.

A DPP makes product information accessible through a digital record. Depending on the applicable requirements, that information may cover materials, environmental performance, repairability, reuse and recycling. The EU registry records product identifiers and associated metadata, while detailed product information is maintained separately. Preparing the underlying records remains an essential part of the process.

"A Digital Product Passport is only as good as the data behind it," said Isak Marais, Chief Executive Officer of AICA Data. "Inconsistent classifications and missing attributes need to be addressed before a catalogue is ready for passport use. We help organisations prepare that data, whichever platform ultimately issues the passport."

Product information can be spread across enterprise resource planning (ERP) systems, product information management (PIM) systems and supplier catalogues. Records may use different descriptions or classification structures, making them difficult to compare. Missing attributes create further gaps. Establishing a consistent structure helps organisations understand what information they hold and what still needs attention.

For example, a product description may identify an item sufficiently for an internal purchasing team while omitting attributes needed by another system. Classification helps establish what the item is and how it should be grouped. Enrichment addresses the supporting details. Reviewing both together gives organisations a clearer picture of the work required before existing catalogue records can support the information needs of a Digital Product Passport.

Atlas classifies and enriches catalogues using the United Nations Standard Products and Services Code (UNSPSC), eCl@ss and ETIM technical classifications, GS1 Global Product Classification (GPC), and Harmonized System (HS) customs codes. These mappings support the organisation of product information across industrial supply chains and provide a basis for preparing data for passport systems.

The relevant classification depends on the product and its intended use. A common structure can make records easier to organise, compare and exchange between systems. However, mapping a product to a classification standard does not by itself establish compliance with DPP requirements. Organisations must also address the specific information and obligations applicable to their products.

Atlas assigns a reliability score and an audit trail to each output. Records with low confidence scores are routed to human review, allowing specialists to examine uncertain results. This process helps teams focus their attention on records requiring judgement and maintains visibility over the classification work undertaken across a catalogue.

AICA reports that Atlas achieved 95.2% accuracy for its first-choice UNSPSC version 26 classifications on a test set of maintenance, repair and operations items excluded from training. The measure reflects how often the highest-ranked classification matched the expected code. The company also reports an increase of at least 40% in attribute completeness.

In a recent project, AICA classified more than 325,000 stock keeping units for a global information technology solutions provider. Records with low confidence scores were isolated for expert review. AICA applies the same review process, reliability scoring and audit trail to catalogues being prepared for DPP use, retaining human oversight where automated results require further examination.

Atlas integrates with SAP, Oracle, Maximo, Coupa, PIM and master data management systems. These connections support organisations working with product information across existing business applications. The classification and enrichment work takes place before the prepared data is used by a passport platform, allowing organisations to address catalogue quality within their broader preparation process.

The engine is available through an application programming interface (API), self-service access and white-label deployment, as well as through Amazon Web Services (AWS) Marketplace. These options provide different ways to access AICA's classification and enrichment services, including integration into software workflows and delivery through partner-branded services.

For organisations preparing for DPP requirements, the initial task is to establish the condition of their product records. A sample assessment can reveal inconsistent classifications and incomplete attributes before work extends across a larger catalogue. The findings can then inform decisions about data preparation, specialist review and the information that must be obtained from suppliers.

About AICA Data

AICA Data uses artificial intelligence to classify and enrich product data for industrial organisations across more than 55 countries. Its Atlas engine maps catalogues to global classification standards, with reliability scoring, human review and auditable outputs. The platform is available through an API, self-service access, white-label deployment and AWS Marketplace.

Isak Marais
AICA Data International
+447741366037 ext.
info@aicadata.com
Visit us on social media:
LinkedIn
Facebook
YouTube
X