Operating cost budgeting methods: quantitative methods to improve the process
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Production |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000400675 |
Resumo: | Abstract Operating cost forecasts are used in economic feasibility studies of projects and in budgeting process. Studies have pointed out that some companies are not satisfied with the budgeting process and chief executive officers want updates more frequently. In these cases, the main problem lies in the costs versus benefits. Companies seek simple and cheap forecasting methods without, at the same time, conceding in terms of quality of the resulting information. This study aims to compare operating cost forecasting models to identify the ones that are relatively easy to implement and turn out less deviation. For this purpose, we applied ARIMA (autoregressive integrated moving average) and distributed dynamic lag models to data from a Brazilian petroleum company. The results suggest that the models have potential application, and that multivariate models fitted better and showed itself a better way to forecast costs than univariate models. |
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Operating cost budgeting methods: quantitative methods to improve the processBudgeting ProcessForecasting ModelsDynamic Lag ModelsARIMAAbstract Operating cost forecasts are used in economic feasibility studies of projects and in budgeting process. Studies have pointed out that some companies are not satisfied with the budgeting process and chief executive officers want updates more frequently. In these cases, the main problem lies in the costs versus benefits. Companies seek simple and cheap forecasting methods without, at the same time, conceding in terms of quality of the resulting information. This study aims to compare operating cost forecasting models to identify the ones that are relatively easy to implement and turn out less deviation. For this purpose, we applied ARIMA (autoregressive integrated moving average) and distributed dynamic lag models to data from a Brazilian petroleum company. The results suggest that the models have potential application, and that multivariate models fitted better and showed itself a better way to forecast costs than univariate models.Associação Brasileira de Engenharia de Produção2016-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000400675Production v.26 n.4 2016reponame:Productioninstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPRO10.1590/0103-6513.201415info:eu-repo/semantics/openAccessSilva,José Olegário Rodrigues daFortunato,GrazielaBastos,Sérgio Augusto Pereiraeng2016-12-08T00:00:00Zoai:scielo:S0103-65132016000400675Revistahttps://www.scielo.br/j/prod/https://old.scielo.br/oai/scielo-oai.php||production@editoracubo.com.br1980-54110103-6513opendoar:2016-12-08T00:00Production - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Operating cost budgeting methods: quantitative methods to improve the process |
title |
Operating cost budgeting methods: quantitative methods to improve the process |
spellingShingle |
Operating cost budgeting methods: quantitative methods to improve the process Silva,José Olegário Rodrigues da Budgeting Process Forecasting Models Dynamic Lag Models ARIMA |
title_short |
Operating cost budgeting methods: quantitative methods to improve the process |
title_full |
Operating cost budgeting methods: quantitative methods to improve the process |
title_fullStr |
Operating cost budgeting methods: quantitative methods to improve the process |
title_full_unstemmed |
Operating cost budgeting methods: quantitative methods to improve the process |
title_sort |
Operating cost budgeting methods: quantitative methods to improve the process |
author |
Silva,José Olegário Rodrigues da |
author_facet |
Silva,José Olegário Rodrigues da Fortunato,Graziela Bastos,Sérgio Augusto Pereira |
author_role |
author |
author2 |
Fortunato,Graziela Bastos,Sérgio Augusto Pereira |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Silva,José Olegário Rodrigues da Fortunato,Graziela Bastos,Sérgio Augusto Pereira |
dc.subject.por.fl_str_mv |
Budgeting Process Forecasting Models Dynamic Lag Models ARIMA |
topic |
Budgeting Process Forecasting Models Dynamic Lag Models ARIMA |
description |
Abstract Operating cost forecasts are used in economic feasibility studies of projects and in budgeting process. Studies have pointed out that some companies are not satisfied with the budgeting process and chief executive officers want updates more frequently. In these cases, the main problem lies in the costs versus benefits. Companies seek simple and cheap forecasting methods without, at the same time, conceding in terms of quality of the resulting information. This study aims to compare operating cost forecasting models to identify the ones that are relatively easy to implement and turn out less deviation. For this purpose, we applied ARIMA (autoregressive integrated moving average) and distributed dynamic lag models to data from a Brazilian petroleum company. The results suggest that the models have potential application, and that multivariate models fitted better and showed itself a better way to forecast costs than univariate models. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000400675 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000400675 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-6513.201415 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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 |
Production v.26 n.4 2016 reponame:Production instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Production |
collection |
Production |
repository.name.fl_str_mv |
Production - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
||production@editoracubo.com.br |
_version_ |
1754213154083045376 |