A Collection-based Digital Product Passport Suitable for Tracking Parts With No ID

Digital Product Passports (DPPs) are great for collecting, tracking, and using product-related information across different stakeholders along the entire product lifecycle. Usually, a product is linked to its virtual counterpart (the DPP) by a machine-readable identifier (ID) such as a QR-code. An open question is how to seamlessly track products or parts of products that carry no ID on them. In this paper, we introduce a DPP concept based on the well-understood concept of a Collection. This approach assumes that each part belongs to a Collection at all times, which makes it trackable without an ID. An object can be removed from a Collection or added to another Collection. We explain our concept via a use case from the System 180 Green-AI Hub pilot project. We validate the approach with an edge demonstrator and a web application, where computer vision detects components and condition and results are stored in a Neo4j graph. Collections ensure traceability without physical IDs. The experiments demonstrate feasibility, and we discuss limitations and industrial relevance.

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Publication Details

Journal
Procedia Computer Science
Published
2026-01-01
DOI
https://doi.org/10.1016/j.procs.2026.02.223
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
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article

A Collection-based Digital Product Passport Suitable for Tracking Parts With No ID

Peter J. Stein, Martin Ruskowski, Christiane Plociennik, Abdullah Farrukh et al.
Procedia Computer Science
Food Supply Chain Traceability
article

A Collection-based Digital Product Passport Suitable for Tracking Parts With No ID

Peter J. Stein, Martin Ruskowski, Christiane Plociennik, Abdullah Farrukh, Jonas Brozeit
article en

Abstract

Digital Product Passports (DPPs) are great for collecting, tracking, and using product-related information across different stakeholders along the entire product lifecycle. Usually, a product is linked to its virtual counterpart (the DPP) by a machine-readable identifier (ID) such as a QR-code. An open question is how to seamlessly track products or parts of products that carry no ID on them. In this paper, we introduce a DPP concept based on the well-understood concept of a Collection. This approach assumes that each part belongs to a Collection at all times, which makes it trackable without an ID. An object can be removed from a Collection or added to another Collection. We explain our concept via a use case from the System 180 Green-AI Hub pilot project. We validate the approach with an edge demonstrator and a web application, where computer vision detects components and condition and results are stored in a Neo4j graph. Collections ensure traceability without physical IDs. The experiments demonstrate feasibility, and we discuss limitations and industrial relevance.

Procedia Computer ScienceVol. 277
German Research Centre for Artificial Intelligence (DE)
Openalex Percentile: Top 11%
Food Supply Chain Traceability
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