2025
Wicaksono, Hendro; Mengistu, Abel; Bashyal, Atit; Fekete, Tamas
Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review Journal Article
In: Procedia Computer Science, vol. 253, pp. 2980-2989, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, digital product passport, technology adoption
@article{nokey,
title = {Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review},
author = {Hendro Wicaksono and Abel Mengistu and Atit Bashyal and Tamas Fekete},
url = {https://www.sciencedirect.com/science/article/pii/S1877050925003655},
doi = {https://doi.org/10.1016/j.procs.2025.02.022},
year = {2025},
date = {2025-02-25},
journal = {Procedia Computer Science},
volume = {253},
pages = {2980-2989},
abstract = {This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.
},
keywords = {artificial intelligence, digital product passport, technology adoption},
pubstate = {published},
tppubtype = {article}
}
This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.
2024
Ompusunggu, Agusmian P; Tjahjowidodo, Tegoeh; Wicaksono, Hendro
Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem Proceedings Article
In: Cranfield University, 2024.
Abstract | Links | BibTeX | Tags: causal AI, causal inference, circular economy, digital product passport
@inproceedings{ompusunggua2024causal,
title = {Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem},
author = {Agusmian P Ompusunggu and Tegoeh Tjahjowidodo and Hendro Wicaksono},
doi = {10.57996/cran.ceres-2579},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Cranfield University},
abstract = {Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.},
keywords = {causal AI, causal inference, circular economy, digital product passport},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.