Financial impact of errors in business forecasting: a comparative study of linear models and neural networks
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista Produção Online |
Texto Completo: | https://www.producaoonline.org.br/rpo/article/view/959 |
Resumo: | The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used), with mean absolute percent error (MAPE) around 10%. The total financial impact for the company was 6,05% on annual sales. |
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Financial impact of errors in business forecasting: a comparative study of linear models and neural networksImpacto financeiro dos erros na previsão empresarial: um estudo comparativo entre modelos lineares e redes neuraisDemand forecasting. Linear models. Neural networks. Accuracy. financial performance.Previsão de demanda. Métodos lineares. Redes neurais. Acuracidade. Desempenho financeiro.The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used), with mean absolute percent error (MAPE) around 10%. The total financial impact for the company was 6,05% on annual sales.A importância da previsão de demanda como uma ferramenta gerencial é um assunto bem documentado. Entretanto, é difícil mensurar os custos gerados por erros de previsão, assim como encontrar no mercado um modelo que assimile adequadamente as particularidades do funcionamento de cada empresa. No geral, quando os modelos lineares falham no processo de previsão, modelos não lineares mais complexos são considerados. Embora alguns estudos comparativos entre os modelos tradicionais e redes neurais tenham sido conduzidos na literatura, as conclusões são geralmente contraditórias. Neste sentido, o objetivo deste trabalho foi comparar a acurácia de métodos de previsão de demanda lineares e redes neurais em relação ao atual método utilizado pela empresa em estudo. Os resultados desta análise também serviram como subsídio para avaliar a influência dos erros de previsão de demanda no desempenho financeiro da empresa. O estudo foi conduzido em cinco grupos de produtos, com dados históricos de demanda abrangendo o período de 2004 a 2008. De um modo geral, pode-se afirmar que todos os modelos estudados apresentaram uma previsão boa (melhor que o atual método usado pela empresa), com erro absoluto médio percentual (MAPE) em torno de 10%. O impacto financeiro total referente aos erros de previsão para a empresa foi de aproximadamente 6,05% sobre o faturamento anual.Associação Brasileira de Engenharia de Produção2012-08-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfaudio/mpeghttps://www.producaoonline.org.br/rpo/article/view/95910.14488/1676-1901.v12i3.959Revista Produção Online; Vol. 12 No. 3 (2012); 629-656Revista Produção Online; v. 12 n. 3 (2012); 629-6561676-1901reponame:Revista Produção Onlineinstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROporhttps://www.producaoonline.org.br/rpo/article/view/959/926https://www.producaoonline.org.br/rpo/article/view/959/927Copyright (c) 2014 Revista Produção Onlineinfo:eu-repo/semantics/openAccessda Veiga, Claudimar Pereirada Veiga, Cássia Rita PereiraVieira, Guilherme ErnaniTortato, Ubiratã2015-11-11T17:25:48Zoai:ojs.emnuvens.com.br:article/959Revistahttp://producaoonline.org.br/rpoPUBhttps://www.producaoonline.org.br/rpo/oai||producaoonline@gmail.com1676-19011676-1901opendoar:2015-11-11T17:25:48Revista Produção Online - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks Impacto financeiro dos erros na previsão empresarial: um estudo comparativo entre modelos lineares e redes neurais |
title |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
spellingShingle |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks da Veiga, Claudimar Pereira Demand forecasting. Linear models. Neural networks. Accuracy. financial performance. Previsão de demanda. Métodos lineares. Redes neurais. Acuracidade. Desempenho financeiro. |
title_short |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
title_full |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
title_fullStr |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
title_full_unstemmed |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
title_sort |
Financial impact of errors in business forecasting: a comparative study of linear models and neural networks |
author |
da Veiga, Claudimar Pereira |
author_facet |
da Veiga, Claudimar Pereira da Veiga, Cássia Rita Pereira Vieira, Guilherme Ernani Tortato, Ubiratã |
author_role |
author |
author2 |
da Veiga, Cássia Rita Pereira Vieira, Guilherme Ernani Tortato, Ubiratã |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
da Veiga, Claudimar Pereira da Veiga, Cássia Rita Pereira Vieira, Guilherme Ernani Tortato, Ubiratã |
dc.subject.por.fl_str_mv |
Demand forecasting. Linear models. Neural networks. Accuracy. financial performance. Previsão de demanda. Métodos lineares. Redes neurais. Acuracidade. Desempenho financeiro. |
topic |
Demand forecasting. Linear models. Neural networks. Accuracy. financial performance. Previsão de demanda. Métodos lineares. Redes neurais. Acuracidade. Desempenho financeiro. |
description |
The importance of demand forecasting as a management tool is a well documented issue. However, it is difficult to measure costs generated by forecasting errors and to find a model that assimilate the detailed operation of each company adequately. In general, when linear models fail in the forecasting process, more complex nonlinear models are considered. Although some studies comparing traditional models and neural networks have been conducted in the literature, the conclusions are usually contradictory. In this sense, the objective was to compare the accuracy of linear methods and neural networks with the current method used by the company. The results of this analysis also served as input to evaluate influence of errors in demand forecasting on the financial performance of the company. The study was based on historical data from five groups of food products, from 2004 to 2008. In general, one can affirm that all models tested presented good results (much better than the current forecasting method used), with mean absolute percent error (MAPE) around 10%. The total financial impact for the company was 6,05% on annual sales. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-08-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.producaoonline.org.br/rpo/article/view/959 10.14488/1676-1901.v12i3.959 |
url |
https://www.producaoonline.org.br/rpo/article/view/959 |
identifier_str_mv |
10.14488/1676-1901.v12i3.959 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.producaoonline.org.br/rpo/article/view/959/926 https://www.producaoonline.org.br/rpo/article/view/959/927 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2014 Revista Produção Online info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2014 Revista Produção Online |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf audio/mpeg |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
dc.source.none.fl_str_mv |
Revista Produção Online; Vol. 12 No. 3 (2012); 629-656 Revista Produção Online; v. 12 n. 3 (2012); 629-656 1676-1901 reponame:Revista Produção Online instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
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Associação Brasileira de Engenharia de Produção (ABEPRO) |
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ABEPRO |
institution |
ABEPRO |
reponame_str |
Revista Produção Online |
collection |
Revista Produção Online |
repository.name.fl_str_mv |
Revista Produção Online - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
||producaoonline@gmail.com |
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1761536948737933312 |