Assessing and forecasting method of financial efficiency in a free industrial economic zone
: Industrial free zones are key to the economic progress of developing countries, making the evaluation and forecast of efficiency in these organisations relevant. This research proposes a three-phase method to evaluate and forecast the financial efficiency of the business profiles of companies belo...
- Autores:
-
Fontalvo Herrera, Tomás José
De la Hoz Domínguez, Enrique José
Fontalvo-Echavez, Orianna
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10424
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10424
https://dx.doi.org/10.1504/IJPQM.2021.115694
- Palabra clave:
- Data envelope analysis
DEA
Clustering
Machine learning
Random forest
Efficiency
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| title |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
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Assessing and forecasting method of financial efficiency in a free industrial economic zone Data envelope analysis DEA Clustering Machine learning Random forest Efficiency LEMB |
| title_short |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| title_full |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| title_fullStr |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| title_full_unstemmed |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| title_sort |
Assessing and forecasting method of financial efficiency in a free industrial economic zone |
| dc.creator.fl_str_mv |
Fontalvo Herrera, Tomás José De la Hoz Domínguez, Enrique José Fontalvo-Echavez, Orianna |
| dc.contributor.author.none.fl_str_mv |
Fontalvo Herrera, Tomás José De la Hoz Domínguez, Enrique José Fontalvo-Echavez, Orianna |
| dc.subject.keywords.spa.fl_str_mv |
Data envelope analysis DEA Clustering Machine learning Random forest Efficiency |
| topic |
Data envelope analysis DEA Clustering Machine learning Random forest Efficiency LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
: Industrial free zones are key to the economic progress of developing countries, making the evaluation and forecast of efficiency in these organisations relevant. This research proposes a three-phase method to evaluate and forecast the financial efficiency of the business profiles of companies belonging to the free economic zone of Cartagena – Colombia. The first phase consisted of a cluster analysis to determine representative groups among the companies analysed. In the second phase, financial efficiency is measured for each of the clusters found in phase 1. Finally, in phase 3 a machine learning model is trained and validated to predict the belonging of a company to a category of financial efficiency – cluster. The results show the creation of two business clusters, with an average efficiency of 49.8% and 14.6% respectively. The random forest model has an accuracy of 95% in the validation phase. |
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2021 |
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2021-06-11 |
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2022-01-28T20:07:07Z |
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2022-01-28T20:07:07Z |
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2022-01-28 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Fontalvo-Herrera, T.J., Delahoz-Dominguez, E. and Fontalvo-Echavez, O. (2021) ‘Assessing and forecasting method of financial efficiency in a free industrial economic zone’, Int. J. Productivity and Quality Management, Vol. 33, No. 2, pp.253–270. |
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https://hdl.handle.net/20.500.12585/10424 |
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https://dx.doi.org/10.1504/IJPQM.2021.115694 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Fontalvo-Herrera, T.J., Delahoz-Dominguez, E. and Fontalvo-Echavez, O. (2021) ‘Assessing and forecasting method of financial efficiency in a free industrial economic zone’, Int. J. Productivity and Quality Management, Vol. 33, No. 2, pp.253–270. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/10424 https://dx.doi.org/10.1504/IJPQM.2021.115694 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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18 Páginas |
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application/pdf |
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Cartagena de Indias |
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Int. J. Productivity and Quality Management, Vol. 33, No. 2, 2021 |
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Fontalvo Herrera, Tomás Joséf3552c06-0ce3-4ffa-80a5-20b1dea28b42De la Hoz Domínguez, Enrique José1845b064-8f16-48b4-8210-4a40985833e2Fontalvo-Echavez, Orianna7241c12a-e61c-4ef0-bb31-9c499371e0012022-01-28T20:07:07Z2022-01-28T20:07:07Z2021-06-112022-01-28Fontalvo-Herrera, T.J., Delahoz-Dominguez, E. and Fontalvo-Echavez, O. (2021) ‘Assessing and forecasting method of financial efficiency in a free industrial economic zone’, Int. J. Productivity and Quality Management, Vol. 33, No. 2, pp.253–270.https://hdl.handle.net/20.500.12585/10424https://dx.doi.org/10.1504/IJPQM.2021.115694Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de Bolívar: Industrial free zones are key to the economic progress of developing countries, making the evaluation and forecast of efficiency in these organisations relevant. This research proposes a three-phase method to evaluate and forecast the financial efficiency of the business profiles of companies belonging to the free economic zone of Cartagena – Colombia. The first phase consisted of a cluster analysis to determine representative groups among the companies analysed. In the second phase, financial efficiency is measured for each of the clusters found in phase 1. Finally, in phase 3 a machine learning model is trained and validated to predict the belonging of a company to a category of financial efficiency – cluster. The results show the creation of two business clusters, with an average efficiency of 49.8% and 14.6% respectively. The random forest model has an accuracy of 95% in the validation phase.18 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Int. J. Productivity and Quality Management, Vol. 33, No. 2, 2021Assessing and forecasting method of financial efficiency in a free industrial economic zoneinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Data envelope analysisDEAClusteringMachine learningRandom forestEfficiencyLEMBCartagena de IndiasBailke, P.A. and Patil, S.T. (2019) ‘Distributed algorithms for improved associative multilabel document classification considering reoccurrence of features and handling minority classes’, International Journal of Business Intelligence and Data Mining, Vol. 14, No. 3, pp.299–321, DOI: 10.1504/IJBIDM.2019.098843 (accessed 10 September 2019).Benicio, J. and de Mello, J.C.S. 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(2018) ‘Rationality, accounting and benchmarking water businesses’, International Journal of Public Sector Management, DOI: 10.1108/IJPSM-04- 2017-0124 (accessed 29 July 2019).Sinuany-Stern, Z., Mehrez, A. and Hadad, Y. (2000) ‘An AHP/DEA methodology for ranking decision making units’, International Transactions in Operational Research, Vol. 7, No. 2, pp.109–124, DOI: 10.1016/S0969- 6016(00)00013-7 (accessed 29 July 2019).Smitha, J.a. and Rajkumar, N. (2019) ‘Efficient moving vehicle detection for intelligent traffic surveillance system using optimal probabilistic neural network’, International Journal of Business Intelligence and Data Mining, Vol. 15, No. 1, pp.22–48, DOI: 10.1504/IJBIDM. 2019.100466 (accessed 10 September 2019).Sreenivasan, S. and Sundaram, M. (2018) ‘A probabilistic model for predicting service level adherence of application support projects’, International Journal of Productivity and Quality Management, Vol. 25, No. 3, pp.305–330, DOI: 10.1504/IJPQM.2018.095648 (accessed 10 September 2019).Varma, G.N. and Padma, K. 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