Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data

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The objective of the work was to enhance the competitiveness of e-commerce and to mitigate the disadvantages associated with it, such as the untimely delivery of purchased products. This was to be achieved through the implementation of a proposed methodology in the processes related to the ‘Last Mil...

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Detalles Bibliográficos
Autores: Rojas García, José Antonio, Elias Giordano, Cynthia, Nallusamy; S., Quiroz Flores, Juan Carlos
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/23195
Enlace del recurso:https://hdl.handle.net/20.500.12724/23195
https://doi.org/10.1016/j.ssaho.2024.100945
Nivel de acceso:acceso abierto
Materia:Pendiente
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spelling Rojas García, José AntonioElias Giordano, CynthiaNallusamy; S.Quiroz Flores, Juan CarlosQuiroz Flores, Juan Carlos2025-09-09T21:26:35Z2025-09-09T21:26:35Z20242590-2911https://hdl.handle.net/20.500.12724/23195Social Sciences and Humanities Open121541816https://doi.org/10.1016/j.ssaho.2024.1009452-s2.0-85192889688The objective of the work was to enhance the competitiveness of e-commerce and to mitigate the disadvantages associated with it, such as the untimely delivery of purchased products. This was to be achieved through the implementation of a proposed methodology in the processes related to the ‘Last Mile’, leveraging Big Data and Lean Logistics to boost the productivity of light logistics SMEs (small and medium-sized enterprises). To identify the conditions impacting the distribution processes, a study was conducted on a population of 750 S MEs, utilizing a sample of 255 companies through stratified probabilistic sampling. The research spanned the years 2022 and 2023. The methodology advocated in this study combines Lean Logistics and Big Data to enhance the supply chain's efficiency and profitability for SMEs engaged in light logistics, amidst the post-pandemic landscape and the conflict between Ukraine and Russia. This methodology was structured into three stages: firstly, the organization of the customer shipment database; secondly, the cleaning of this database to pinpoint records scheduled for distribution; and thirdly, the assignment of delivery rates and probabilities using historical delivery outcomes. The findings suggested that the integration of Lean Logistics and Big Data offers a viable solution for improving the supply chain efficiency and profitability for light logistics SMEs during the post-pandemic period and amidst the Ukraine-Russia conflict. This research's originality is underscored by its novel approach of merging Lean Logistics and Big Data to fortify the supply chain efficiency and profitability for SMEs in light logistics, a synergy not previously identified in existing literature.application/htmlengElsevierGBurn:issn: 2590-2911info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/PendientePendienteEnhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big datainfo:eu-repo/semantics/articleArtículo (Scopus)reponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMA20.500.12724/23195oai:repositorio.ulima.edu.pe:20.500.12724/231952025-11-08 09:06:38.97Repositorio Universidad de Limarepositorio@ulima.edu.pe
dc.title.en_EN.fl_str_mv Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
title Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
spellingShingle Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
Rojas García, José Antonio
Pendiente
Pendiente
title_short Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
title_full Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
title_fullStr Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
title_full_unstemmed Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
title_sort Enhancement of the distribution process on light logistics SMEs in times post-pandemic Covid-19 with Ukraine-Russia conflict by lean logistics and big data
author Rojas García, José Antonio
author_facet Rojas García, José Antonio
Elias Giordano, Cynthia
Nallusamy; S.
Quiroz Flores, Juan Carlos
author_role author
author2 Elias Giordano, Cynthia
Nallusamy; S.
Quiroz Flores, Juan Carlos
author2_role author
author
author
dc.contributor.other.none.fl_str_mv Quiroz Flores, Juan Carlos
dc.contributor.author.fl_str_mv Rojas García, José Antonio
Elias Giordano, Cynthia
Nallusamy; S.
Quiroz Flores, Juan Carlos
dc.subject.none.fl_str_mv Pendiente
topic Pendiente
Pendiente
dc.subject.ocde.none.fl_str_mv Pendiente
description The objective of the work was to enhance the competitiveness of e-commerce and to mitigate the disadvantages associated with it, such as the untimely delivery of purchased products. This was to be achieved through the implementation of a proposed methodology in the processes related to the ‘Last Mile’, leveraging Big Data and Lean Logistics to boost the productivity of light logistics SMEs (small and medium-sized enterprises). To identify the conditions impacting the distribution processes, a study was conducted on a population of 750 S MEs, utilizing a sample of 255 companies through stratified probabilistic sampling. The research spanned the years 2022 and 2023. The methodology advocated in this study combines Lean Logistics and Big Data to enhance the supply chain's efficiency and profitability for SMEs engaged in light logistics, amidst the post-pandemic landscape and the conflict between Ukraine and Russia. This methodology was structured into three stages: firstly, the organization of the customer shipment database; secondly, the cleaning of this database to pinpoint records scheduled for distribution; and thirdly, the assignment of delivery rates and probabilities using historical delivery outcomes. The findings suggested that the integration of Lean Logistics and Big Data offers a viable solution for improving the supply chain efficiency and profitability for light logistics SMEs during the post-pandemic period and amidst the Ukraine-Russia conflict. This research's originality is underscored by its novel approach of merging Lean Logistics and Big Data to fortify the supply chain efficiency and profitability for SMEs in light logistics, a synergy not previously identified in existing literature.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2025-09-09T21:26:35Z
dc.date.available.none.fl_str_mv 2025-09-09T21:26:35Z
dc.date.issued.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.other.none.fl_str_mv Artículo (Scopus)
format article
dc.identifier.issn.none.fl_str_mv 2590-2911
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/23195
dc.identifier.journal.en_EN.fl_str_mv Social Sciences and Humanities Open
dc.identifier.isni.none.fl_str_mv 121541816
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.ssaho.2024.100945
dc.identifier.scopusid.none.fl_str_mv 2-s2.0-85192889688
identifier_str_mv 2590-2911
Social Sciences and Humanities Open
121541816
2-s2.0-85192889688
url https://hdl.handle.net/20.500.12724/23195
https://doi.org/10.1016/j.ssaho.2024.100945
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn: 2590-2911
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/html
dc.publisher.none.fl_str_mv Elsevier
dc.publisher.country.none.fl_str_mv GB
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:ULIMA-Institucional
instname:Universidad de Lima
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instname_str Universidad de Lima
instacron_str ULIMA
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repository.mail.fl_str_mv repositorio@ulima.edu.pe
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