Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1108/JEIM-09-2019-0289 http://hdl.handle.net/11449/195511 |
Resumo: | Purpose The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources. Design/methodology/approach Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros. Findings A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time. Originality/value The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques. |
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Repositório Institucional da UNESP |
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Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniquesFuzzy setsSustainabilitySupply chainFuzzy time seriesPurpose The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources. Design/methodology/approach Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros. Findings A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time. Originality/value The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.Sao Paulo State Univ, Sorocaba, BrazilSao Paulo State Univ, Sao Jose Dos Campos, BrazilIslamic Azad Univ, Dept Math, Isfahan Khorasgan Branch, Esfahan, IranUniv Fed Rio de Janeiro, Rio De Janeiro, BrazilSao Paulo State Univ, Sorocaba, BrazilSao Paulo State Univ, Sao Jose Dos Campos, BrazilEmerald Group Publishing LtdUniversidade Estadual Paulista (Unesp)Islamic Azad UnivUniversidade Federal do Rio de Janeiro (UFRJ)Ewbank, Henrique [UNESP]Frutuoso Roveda, Jose Arnaldo [UNESP]Monteiro Masalskiene Roveda, Sandra Regina [UNESP]Ribeiro, Admilson Irio [UNESP]Bressane, Adriano [UNESP]Hadi-Vencheh, AbdollahWanke, Peter2020-12-10T17:37:07Z2020-12-10T17:37:07Z2020-07-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18http://dx.doi.org/10.1108/JEIM-09-2019-0289Journal Of Enterprise Information Management. Bingley: Emerald Group Publishing Ltd, 18 p., 2020.1741-0398http://hdl.handle.net/11449/19551110.1108/JEIM-09-2019-0289WOS:000547981600001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Enterprise Information Managementinfo:eu-repo/semantics/openAccess2021-10-23T09:13:43Zoai:repositorio.unesp.br:11449/195511Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T11:02:39.329525Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
title |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
spellingShingle |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques Ewbank, Henrique [UNESP] Fuzzy sets Sustainability Supply chain Fuzzy time series |
title_short |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
title_full |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
title_fullStr |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
title_full_unstemmed |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
title_sort |
Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques |
author |
Ewbank, Henrique [UNESP] |
author_facet |
Ewbank, Henrique [UNESP] Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Ribeiro, Admilson Irio [UNESP] Bressane, Adriano [UNESP] Hadi-Vencheh, Abdollah Wanke, Peter |
author_role |
author |
author2 |
Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Ribeiro, Admilson Irio [UNESP] Bressane, Adriano [UNESP] Hadi-Vencheh, Abdollah Wanke, Peter |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Islamic Azad Univ Universidade Federal do Rio de Janeiro (UFRJ) |
dc.contributor.author.fl_str_mv |
Ewbank, Henrique [UNESP] Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Ribeiro, Admilson Irio [UNESP] Bressane, Adriano [UNESP] Hadi-Vencheh, Abdollah Wanke, Peter |
dc.subject.por.fl_str_mv |
Fuzzy sets Sustainability Supply chain Fuzzy time series |
topic |
Fuzzy sets Sustainability Supply chain Fuzzy time series |
description |
Purpose The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources. Design/methodology/approach Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros. Findings A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time. Originality/value The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:37:07Z 2020-12-10T17:37:07Z 2020-07-14 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1108/JEIM-09-2019-0289 Journal Of Enterprise Information Management. Bingley: Emerald Group Publishing Ltd, 18 p., 2020. 1741-0398 http://hdl.handle.net/11449/195511 10.1108/JEIM-09-2019-0289 WOS:000547981600001 |
url |
http://dx.doi.org/10.1108/JEIM-09-2019-0289 http://hdl.handle.net/11449/195511 |
identifier_str_mv |
Journal Of Enterprise Information Management. Bingley: Emerald Group Publishing Ltd, 18 p., 2020. 1741-0398 10.1108/JEIM-09-2019-0289 WOS:000547981600001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Enterprise Information Management |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
18 |
dc.publisher.none.fl_str_mv |
Emerald Group Publishing Ltd |
publisher.none.fl_str_mv |
Emerald Group Publishing Ltd |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803045760084738048 |