Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Augusto
Data de Publicação: 2016
Outros Autores: Durão, Natércia, Seruca, Isabel
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/11328/1749
Resumo: For pharmaceutical distribution companies it is essential to obtain good estimates of medicine needs, due to the short shelf life of many medicines and the need to control stock levels, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In this paper we explore the use of the time series data mining technique for the sales prediction of individual products of a pharmaceutical distribution company in Portugal. Through data mining techniques, the historical data of product sales are analyzed to detect patterns and make predictions based on the experience contained in the data. The results obtained with the technique as well as with the proposed method suggest that the performed modelling may be considered appropriate for the short term product sales prediction.
id RCAP_34e4f25a68b66edefe0c3c1090bdb08b
oai_identifier_str oai:repositorio.uportu.pt:11328/1749
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str
spelling Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em PortugalMedicinesStock unavailabilityData miningTime seriesSales predictionFor pharmaceutical distribution companies it is essential to obtain good estimates of medicine needs, due to the short shelf life of many medicines and the need to control stock levels, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In this paper we explore the use of the time series data mining technique for the sales prediction of individual products of a pharmaceutical distribution company in Portugal. Through data mining techniques, the historical data of product sales are analyzed to detect patterns and make predictions based on the experience contained in the data. The results obtained with the technique as well as with the proposed method suggest that the performed modelling may be considered appropriate for the short term product sales prediction.2017-02-10T18:06:01Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11328/1749por10.1109/CISTI.2016.7521396Ribeiro, AugustoDurão, NatérciaSeruca, Isabelinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-06-15T02:09:52ZPortal AgregadorONG
dc.title.none.fl_str_mv Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
title Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
spellingShingle Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
Ribeiro, Augusto
Medicines
Stock unavailability
Data mining
Time series
Sales prediction
title_short Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
title_full Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
title_fullStr Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
title_full_unstemmed Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
title_sort Detection of outliers for a pharmaceutical distribution company in Portugal | Deteção de outliers numa empresa de distribuição farmacêutica em Portugal
author Ribeiro, Augusto
author_facet Ribeiro, Augusto
Durão, Natércia
Seruca, Isabel
author_role author
author2 Durão, Natércia
Seruca, Isabel
author2_role author
author
dc.contributor.author.fl_str_mv Ribeiro, Augusto
Durão, Natércia
Seruca, Isabel
dc.subject.por.fl_str_mv Medicines
Stock unavailability
Data mining
Time series
Sales prediction
topic Medicines
Stock unavailability
Data mining
Time series
Sales prediction
description For pharmaceutical distribution companies it is essential to obtain good estimates of medicine needs, due to the short shelf life of many medicines and the need to control stock levels, so as to avoid excessive inventory costs while guaranteeing customer demand satisfaction, and thus decreasing the possibility of loss of customers due to stock outages. In this paper we explore the use of the time series data mining technique for the sales prediction of individual products of a pharmaceutical distribution company in Portugal. Through data mining techniques, the historical data of product sales are analyzed to detect patterns and make predictions based on the experience contained in the data. The results obtained with the technique as well as with the proposed method suggest that the performed modelling may be considered appropriate for the short term product sales prediction.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2017-02-10T18:06:01Z
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://hdl.handle.net/11328/1749
url http://hdl.handle.net/11328/1749
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 10.1109/CISTI.2016.7521396
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1777302550119186432