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.
Brozeit et al. (Thu,) studied this question.
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