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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Moura, P. E. de., Amaral, V. & Brito e Abreu, F. (2023). Assessing the impact of process awareness in industry 4.0. In Anwar, S., Ullah, A., Rocha, Á., and Sousa, M. J. (Ed.), Proceedings of International Conference on Information Technology and Applications ICITA 2022. Lecture Notes in Networks and Systems . (pp. 311-321). Lisboa: Springer.
Exportar Referência (IEEE)
P. E. Moura et al.,  "Assessing the impact of process awareness in industry 4.0", in Proc. of Int. Conf. on Information Technology and Applications ICITA 2022. Lecture Notes in Networks and Systems , Anwar, S., Ullah, A., Rocha, Á., and Sousa, M. J., Ed., Lisboa, Springer, 2023, vol. 614, pp. 311-321
Exportar BibTeX
@inproceedings{moura2023_1732211477332,
	author = "Moura, P. E. de. and Amaral, V. and Brito e Abreu, F.",
	title = "Assessing the impact of process awareness in industry 4.0",
	booktitle = "Proceedings of International Conference on Information Technology and Applications ICITA 2022. Lecture Notes in Networks and Systems ",
	year = "2023",
	editor = "Anwar, S., Ullah, A., Rocha, Á., and Sousa, M. J.",
	volume = "614",
	number = "",
	series = "",
	doi = "10.1007/978-981-19-9331-2_26",
	pages = "311-321",
	publisher = "Springer",
	address = "Lisboa",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-981-19-9331-2"
}
Exportar RIS
TY  - CPAPER
TI  - Assessing the impact of process awareness in industry 4.0
T2  - Proceedings of International Conference on Information Technology and Applications ICITA 2022. Lecture Notes in Networks and Systems 
VL  - 614
AU  - Moura, P. E. de.
AU  - Amaral, V.
AU  - Brito e Abreu, F.
PY  - 2023
SP  - 311-321
SN  - 2367-3370
DO  - 10.1007/978-981-19-9331-2_26
CY  - Lisboa
UR  - https://link.springer.com/book/10.1007/978-981-19-9331-2
AB  - The historical (and market) value of classic cars’ depends on their authenticity, which can be ruined by careless restoration processes. This paper reports on our ongoing research on monitoring the progress of such processes. We developed a process monitoring platform that combines data gathered from IoT sensors with input provided by a plant shop manager, using a process-aware GUI. The underlying process complies with the best practices expressed in FIVA’s Charter of Turin.
Evidence (e.g., photos, documents, and short movies) can be attached to each task during process instantiation. Furthermore, car owners can remotely control cameras and car rotisserie to monitor critical steps of the restoration process. The benefits are manifold for all involved stakeholders. Restoration workshops increase their transparency and credibility while getting a better grasp on work assignments. Car owners can better assure the authenticity of their cars to third parties (potential buyers and certification bodies) while reducing their financial and scheduling overhead and carbon footprint.
ER  -