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Multi-dimensional layers framework for classifying waste in industrial plants

2024Conference paper Restricted access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorRossi, Arthur
dc.contributor.authorLeitão, Paulo
dc.contributor.authorPontes, Joseane
dc.date.accessioned2025-07-09T15:02:59Z
dc.date.available2025-07-09T15:02:59Z
dc.date.issued2024
dc.description.abstractThe transition from Industry 4.0 to Industry 5.0 introduces new challenges in waste management, particularly digital, energy and human waste. However its not clear how to classify and categorize without creating new dilemmas in the 8 traditional established wastes from Lean theory. To solve this problem, this paper aims to develop a multi-dimensional layers framework to classify waste by making a interpretation of the traditional lean principles into a digital and sustainable layer perspective. The methodology involves projecting the 8 lean wastes onto physical, digital, and sustainable layers, allowing for a comprehensive waste characterization. With this, its presented a framework that identifies various types of digital waste, such as defective data and excessive non-value-adding data, and high-lights the energy inefficiencies and human factors associated with digital transformation. The results shows the framework’s ability to provide a n organized approach to attempt to classification new wastes typologies without creating new wastes and keeping the Lean theory at its original state. As such, this study contributes to the literature by expanding the concept of digital waste and integrating sustainability into waste typology and providing the implications of the layer expansion for future research.eng
dc.description.sponsorshipThis research has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020. The realization of this work was possible due to the scientific initiation grant provided by UTFPR, to which we are immensely grateful.
dc.identifier.citationRossi, Arthur; Leitão, Paulo; Pontes, Joseane. (2024). Multi-Dimensional Layers Framework for Classifying Waste in Industrial Plants. In 3rd Industrial Electronics Society Annual On-Line Conference, ONCON 2024. p. 1-7
dc.identifier.doi10.1109/oncon62778.2024.10931478
dc.identifier.isbn979-833154031-9
dc.identifier.urihttp://hdl.handle.net/10198/34655
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relation.ispartof2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDigital Transformation
dc.subjectSustainable Waste
dc.subjectDigital Waste
dc.titleMulti-dimensional layers framework for classifying waste in industrial plantseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.conferenceDate2024
oaire.citation.endPage7
oaire.citation.startPage1
oaire.citation.title2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference, ONCON 2024
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLeitão
person.givenNamePaulo
person.identifierA-8390-2011
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isAuthorOfPublication.latestForDiscovery68d9eb25-ad4f-439b-aeb2-35e8708644cc
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

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