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A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

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A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

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dc.contributor.author Herrera-Granda, Israel D. es_ES
dc.contributor.author Lorente-Leyva, Leandro L. es_ES
dc.contributor.author Peluffo-Ordóñez, Diego H. es_ES
dc.contributor.author Alemany Díaz, María Del Mar es_ES
dc.date.accessioned 2021-09-14T03:33:27Z
dc.date.available 2021-09-14T03:33:27Z
dc.date.issued 2021-01-07 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/172310
dc.description.abstract [EN] Ecuador is worldwide considered as one of the main natural flower producers and exporters ¿being roses the most salient ones. Such a fact has naturally led the emergence of small and medium sized companies devoted to the production of quality roses in the Ecuadorian highlands, which intrinsically entails resource usage optimization. One of the first steps towards optimizing the use of resources is to forecast demand, since it enables a fair perspective of the future, in such a manner that the in-advance raw materials supply can be previewed against eventualities, resources usage can be properly planned, as well as the misuse can be avoided. Within this approach, the problem of forecasting the supply of roses was solved into two phases: the first phase consists of the macro-forecast of the total amount to be exported by the Ecuadorian flower sector by the year 2020, using multi-layer neural networks. In the second phase, the monthly demand for the main rose varieties offered by the study company was micro-forecasted by testing seven models. In addition, a Bayesian network model is designed, which takes into consideration macroeconomic aspects, the level of employability in Ecuador and weather-related aspects. This Bayesian network provided satisfactory results without the need for a large amount of historical data and at a low-computational cost. es_ES
dc.description.sponsorship Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS ¿Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems¿ (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015. In addition, the authors are greatly grateful by the support given by the SDAS Research Group (www.sdas-group.com) es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.relation.ispartof LOD 2020: Machine Learning, Optimization, and Data Science es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bayesian-Networks-based forecasting es_ES
dc.subject Demand forecast es_ES
dc.subject Floriculture sector es_ES
dc.subject Neural-Networks-based forecasting es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-64580-9_21 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/691249/EU/Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Herrera-Granda, ID.; Lorente-Leyva, LL.; Peluffo-Ordóñez, DH.; Alemany Díaz, MDM. (2021). A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios. Lecture Notes in Computer Science. 12566:245-258. https://doi.org/10.1007/978-3-030-64580-9_21 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 6th International Conference on Machine Learning, Optimization, and Data Science (LOD 2020) es_ES
dc.relation.conferencedate Julio 19-23,2020 es_ES
dc.relation.conferenceplace Siena, Italy es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-64580-9_21 es_ES
dc.description.upvformatpinicio 245 es_ES
dc.description.upvformatpfin 258 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12566 es_ES
dc.relation.pasarela S\430740 es_ES
dc.contributor.funder COMISION DE LAS COMUNIDADES EUROPEA es_ES
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