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A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring dec...

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Autores principales: Sequeira, Movin, Adlemo, Anders, Hilletofth, Per
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245875/
http://dx.doi.org/10.1007/s12063-022-00284-6
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author Sequeira, Movin
Adlemo, Anders
Hilletofth, Per
author_facet Sequeira, Movin
Adlemo, Anders
Hilletofth, Per
author_sort Sequeira, Movin
collection PubMed
description Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.
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spelling pubmed-92458752022-07-01 A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions Sequeira, Movin Adlemo, Anders Hilletofth, Per Oper Manag Res Article Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives. Springer US 2022-06-30 2023 /pmc/articles/PMC9245875/ http://dx.doi.org/10.1007/s12063-022-00284-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sequeira, Movin
Adlemo, Anders
Hilletofth, Per
A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title_full A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title_fullStr A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title_full_unstemmed A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title_short A hybrid fuzzy-AHP-TOPSIS model for evaluation of manufacturing relocation decisions
title_sort hybrid fuzzy-ahp-topsis model for evaluation of manufacturing relocation decisions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245875/
http://dx.doi.org/10.1007/s12063-022-00284-6
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