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https://hdl.handle.net/1822/90007
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Campo DC | Valor | Idioma |
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dc.contributor.author | Reis, Angelica Muffato | por |
dc.contributor.author | Sousa, Sérgio | por |
dc.contributor.author | Costa, Lino | por |
dc.date.accessioned | 2024-03-25T19:42:25Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Reis, A.M., de Sousa, S.D.T., Costa, L. (2023). Wastes Identification Through Kaizen Events: A Case Study in the Automotive Sector. In: Kim, KY., Monplaisir, L., Rickli, J. (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus. FAIM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17629-6_24 | por |
dc.identifier.isbn | 978-3-031-17628-9 | por |
dc.identifier.issn | 2195-4356 | por |
dc.identifier.uri | https://hdl.handle.net/1822/90007 | - |
dc.description.abstract | The efficient use of lean tools and techniques leads to the reduction of non-value-added activities in production systems. Continuous Improvement (CI) efforts in a workshop format, a.k.a. Kaizen Event (KE), is one of these lean tools. Measuring the gain from KEs has always been a challenge and as a result, firms spend much effort fixing issues that are non-critical or have low or no effect on factory performance, therefore, it is necessary more research on the metrics and the outcomes KE, including waste metrics. This paper presents a case study within a company in the automotive electronics sector to characterize and present outcomes of eight KEs, within which a total of 136 wastes were identified. Categorizing these wastes by groups, results reveal that the “operator motion” is the waste category most frequently noticed by the teams, while “automatic assembly” is the most impactful one in terms of cycle time reduction. While this case study makes a significant contribution in providing empirical evidence of waste in an organization, more research is needed to develop context-specific tools to narrow down the wastes once they have been identified. | por |
dc.description.sponsorship | This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. | por |
dc.language.iso | eng | por |
dc.publisher | Springer, Cham | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | Kaizen Event | por |
dc.subject | Lean manufacturing | por |
dc.subject | Wastes | por |
dc.title | Wastes identification through Kaizen events: a case study in the automotive sector | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-031-17629-6_24 | por |
sdum.event.type | conference | por |
oaire.citationStartPage | 228 | por |
oaire.citationEndPage | 235 | por |
dc.identifier.doi | 10.1007/978-3-031-17629-6_24 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-3-031-17629-6 | por |
dc.subject.fos | Engenharia e Tecnologia::Outras Engenharias e Tecnologias | por |
sdum.journal | Lecture Notes in Mechanical Engineering | - |
oaire.version | VoR | por |
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978-3-031-17629-6_24.pdf Acesso restrito! | paper | 260,03 kB | Adobe PDF | Ver/Abrir |