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A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation
Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction impl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001944/ https://www.ncbi.nlm.nih.gov/pubmed/32023306 http://dx.doi.org/10.1371/journal.pone.0228746 |
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author | Dehdasht, Gholamreza Ferwati, M. Salim Zin, Rosli Mohamad Abidin, Nazirah Zainul |
author_facet | Dehdasht, Gholamreza Ferwati, M. Salim Zin, Rosli Mohamad Abidin, Nazirah Zainul |
author_sort | Dehdasht, Gholamreza |
collection | PubMed |
description | Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions. |
format | Online Article Text |
id | pubmed-7001944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70019442020-02-18 A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation Dehdasht, Gholamreza Ferwati, M. Salim Zin, Rosli Mohamad Abidin, Nazirah Zainul PLoS One Research Article Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions. Public Library of Science 2020-02-05 /pmc/articles/PMC7001944/ /pubmed/32023306 http://dx.doi.org/10.1371/journal.pone.0228746 Text en © 2020 Dehdasht et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dehdasht, Gholamreza Ferwati, M. Salim Zin, Rosli Mohamad Abidin, Nazirah Zainul A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title | A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title_full | A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title_fullStr | A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title_full_unstemmed | A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title_short | A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation |
title_sort | hybrid approach using entropy and topsis to select key drivers for a successful and sustainable lean construction implementation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001944/ https://www.ncbi.nlm.nih.gov/pubmed/32023306 http://dx.doi.org/10.1371/journal.pone.0228746 |
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