Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Independent Journal of Management & Production |
Texto Completo: | http://www.ijmp.jor.br/index.php/ijmp/article/view/998 |
Resumo: | Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy. |
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Independent Journal of Management & Production |
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Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysissales forecastMonte Carlo simulationmechanical componentsOrganizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy.Independent2019-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://www.ijmp.jor.br/index.php/ijmp/article/view/99810.14807/ijmp.v10i4.998Independent Journal of Management & Production; Vol. 10 No. 4 (2019): Independent Journal of Management & Production (Special Edition IFLOG); 1324-13402236-269X2236-269Xreponame:Independent Journal of Management & Productioninstname:Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)instacron:IJM&Penghttp://www.ijmp.jor.br/index.php/ijmp/article/view/998/1116http://www.ijmp.jor.br/index.php/ijmp/article/view/998/1126Copyright (c) 2019 Kevin William Matos Paixão, Adriano Maniçoba da Silvainfo:eu-repo/semantics/openAccessPaixão, Kevin William MatosSilva, Adriano Maniçoba da2019-11-01T03:21:40Zoai:www.ijmp.jor.br:article/998Revistahttp://www.ijmp.jor.br/PUBhttp://www.ijmp.jor.br/index.php/ijmp/oaiijmp@ijmp.jor.br||paulo@paulorodrigues.pro.br||2236-269X2236-269Xopendoar:2019-11-01T03:21:40Independent Journal of Management & Production - Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)false |
dc.title.none.fl_str_mv |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
title |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
spellingShingle |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis Paixão, Kevin William Matos sales forecast Monte Carlo simulation mechanical components |
title_short |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
title_full |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
title_fullStr |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
title_full_unstemmed |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
title_sort |
Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis |
author |
Paixão, Kevin William Matos |
author_facet |
Paixão, Kevin William Matos Silva, Adriano Maniçoba da |
author_role |
author |
author2 |
Silva, Adriano Maniçoba da |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Paixão, Kevin William Matos Silva, Adriano Maniçoba da |
dc.subject.por.fl_str_mv |
sales forecast Monte Carlo simulation mechanical components |
topic |
sales forecast Monte Carlo simulation mechanical components |
description |
Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-08-01 |
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 |
http://www.ijmp.jor.br/index.php/ijmp/article/view/998 10.14807/ijmp.v10i4.998 |
url |
http://www.ijmp.jor.br/index.php/ijmp/article/view/998 |
identifier_str_mv |
10.14807/ijmp.v10i4.998 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.ijmp.jor.br/index.php/ijmp/article/view/998/1116 http://www.ijmp.jor.br/index.php/ijmp/article/view/998/1126 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Kevin William Matos Paixão, Adriano Maniçoba da Silva info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Kevin William Matos Paixão, Adriano Maniçoba da Silva |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Independent |
publisher.none.fl_str_mv |
Independent |
dc.source.none.fl_str_mv |
Independent Journal of Management & Production; Vol. 10 No. 4 (2019): Independent Journal of Management & Production (Special Edition IFLOG); 1324-1340 2236-269X 2236-269X reponame:Independent Journal of Management & Production instname:Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP) instacron:IJM&P |
instname_str |
Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP) |
instacron_str |
IJM&P |
institution |
IJM&P |
reponame_str |
Independent Journal of Management & Production |
collection |
Independent Journal of Management & Production |
repository.name.fl_str_mv |
Independent Journal of Management & Production - Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP) |
repository.mail.fl_str_mv |
ijmp@ijmp.jor.br||paulo@paulorodrigues.pro.br|| |
_version_ |
1797220492290031616 |