Sales forecasting using machine learning algorithms
Retail companies, as production systems, must use their resources efficiently and make strategic decisions to obtain growing and stable revenues, especially when market conditions are becoming more competitive and profit margins are increasingly pressured. Thus, sales forecasting is crucial to maint...
| Autores: | , |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2023 |
| País: | Brasil |
| Recursos: | Sindicato das Secretárias do Estado de São Paulo (SINSESP) |
| Repositório: | GeSec |
| Idioma: | inglês |
| OAI Identifier: | oai:ojs2.revistagesec.org.br:article/1670 |
| Acesso em linha: | https://ojs.revistagesec.org.br/secretariado/article/view/1670 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Sales Forecast Retail Machine Learning Time Series Productive Systems Previsão de Vendas Varejo Aprendizado de Máquina Séries Temporais |
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Sales forecasting using machine learning algorithms Sales forecasting using machine learning algorithms |
| title |
Sales forecasting using machine learning algorithms |
| spellingShingle |
Sales forecasting using machine learning algorithms Martins, Emerson Sales Forecast Retail Machine Learning Time Series Productive Systems Previsão de Vendas Varejo Aprendizado de Máquina Séries Temporais |
| title_short |
Sales forecasting using machine learning algorithms |
| title_full |
Sales forecasting using machine learning algorithms |
| title_fullStr |
Sales forecasting using machine learning algorithms |
| title_full_unstemmed |
Sales forecasting using machine learning algorithms |
| title_sort |
Sales forecasting using machine learning algorithms |
| dc.creator.none.fl_str_mv |
Martins, Emerson Galegale, Napoleão Verardi |
| author |
Martins, Emerson |
| author_facet |
Martins, Emerson Galegale, Napoleão Verardi |
| author_role |
author |
| author2 |
Galegale, Napoleão Verardi |
| author2_role |
author |
| dc.subject.por.fl_str_mv |
Sales Forecast Retail Machine Learning Time Series Productive Systems Previsão de Vendas Varejo Aprendizado de Máquina Séries Temporais |
| topic |
Sales Forecast Retail Machine Learning Time Series Productive Systems Previsão de Vendas Varejo Aprendizado de Máquina Séries Temporais |
| description |
Retail companies, as production systems, must use their resources efficiently and make strategic decisions to obtain growing and stable revenues, especially when market conditions are becoming more competitive and profit margins are increasingly pressured. Thus, sales forecasting is crucial to maintain competitiveness in the retail segment, but obtaining inaccurate forecasts can lead to stock shortages, causing delays in deliveries and generating customer dissatisfaction, as well as increasing inventory, increasing the cost of warehousing, forcing the “burn” of stock through promotional campaigns, directly affecting profitability. Forecasting the demand for products and services and adapting the supply chain by finding a balance has always been and will continue to be a challenge in the retail segment. This research aims to evaluate the main methods and identify the one with the greatest accuracy in sales prediction. Based on an integrative literature review (ILR), three main methods were evaluated: time series, artificial neural networks and machine learning algorithms. The results show that machine learning is more suitable in terms of accuracy, particularly when models contain exogenous and endogenous variables, in addition to allowing the identification of hidden patterns in demand that can be used to identify market trends. However, in markets with constant demands and few external interferences, its use is not justified because, for these cases, the use of time series is simpler and less costly. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-07-19 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://ojs.revistagesec.org.br/secretariado/article/view/1670 10.7769/gesec.v14i7.1670 |
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https://ojs.revistagesec.org.br/secretariado/article/view/1670 |
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10.7769/gesec.v14i7.1670 |
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eng |
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eng |
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https://ojs.revistagesec.org.br/secretariado/article/view/1670/1341 |
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Copyright (c) 2023 Emerson Martins, Napoleão Verardi Galegale info:eu-repo/semantics/openAccess |
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Copyright (c) 2023 Emerson Martins, Napoleão Verardi Galegale |
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openAccess |
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application/pdf |
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Revista de Gestão e Secretariado |
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Revista de Gestão e Secretariado |
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Revista de Gestão e Secretariado (Management and Administrative Professional Review); Vol. 14 No. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-11308 Revista de Gestão e Secretariado; Vol. 14 Núm. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-11308 Revista de Gestão e Secretariado; v. 14 n. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-11308 2178-9010 reponame:GeSec instname:Sindicato das Secretárias do Estado de São Paulo (SINSESP) instacron:SINSESP |
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GeSec |
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GeSec - Sindicato das Secretárias do Estado de São Paulo (SINSESP) |
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editor@revistagesec.org.br | gestoreditorial@revistagesec.org.br | rf.sabino@gmail.com |
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1853663830241968128 |
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Sales forecasting using machine learning algorithmsSales forecasting using machine learning algorithmsSales ForecastRetailMachine LearningTime SeriesProductive SystemsPrevisão de VendasVarejoAprendizado de MáquinaSéries TemporaisRetail companies, as production systems, must use their resources efficiently and make strategic decisions to obtain growing and stable revenues, especially when market conditions are becoming more competitive and profit margins are increasingly pressured. Thus, sales forecasting is crucial to maintain competitiveness in the retail segment, but obtaining inaccurate forecasts can lead to stock shortages, causing delays in deliveries and generating customer dissatisfaction, as well as increasing inventory, increasing the cost of warehousing, forcing the “burn” of stock through promotional campaigns, directly affecting profitability. Forecasting the demand for products and services and adapting the supply chain by finding a balance has always been and will continue to be a challenge in the retail segment. This research aims to evaluate the main methods and identify the one with the greatest accuracy in sales prediction. Based on an integrative literature review (ILR), three main methods were evaluated: time series, artificial neural networks and machine learning algorithms. The results show that machine learning is more suitable in terms of accuracy, particularly when models contain exogenous and endogenous variables, in addition to allowing the identification of hidden patterns in demand that can be used to identify market trends. However, in markets with constant demands and few external interferences, its use is not justified because, for these cases, the use of time series is simpler and less costly.Retail companies, as production systems, must use their resources efficiently and make strategic decisions to obtain growing and stable revenues, especially when market conditions are becoming more competitive and profit margins are increasingly pressured. Thus, sales forecasting is crucial to maintain competitiveness in the retail segment, but obtaining inaccurate forecasts can lead to stock shortages, causing delays in deliveries and generating customer dissatisfaction, as well as increasing inventory, increasing the cost of warehousing, forcing the “burn” of stock through promotional campaigns, directly affecting profitability. Forecasting the demand for products and services and adapting the supply chain by finding a balance has always been and will continue to be a challenge in the retail segment. This research aims to evaluate the main methods and identify the one with the greatest accuracy in sales prediction. Based on an integrative literature review (ILR), three main methods were evaluated: time series, artificial neural networks and machine learning algorithms. The results show that machine learning is more suitable in terms of accuracy, particularly when models contain exogenous and endogenous variables, in addition to allowing the identification of hidden patterns in demand that can be used to identify market trends. However, in markets with constant demands and few external interferences, its use is not justified because, for these cases, the use of time series is simpler and less costly.Revista de Gestão e Secretariado2023-07-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.revistagesec.org.br/secretariado/article/view/167010.7769/gesec.v14i7.1670Revista de Gestão e Secretariado (Management and Administrative Professional Review); Vol. 14 No. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-11308Revista de Gestão e Secretariado; Vol. 14 Núm. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-11308Revista de Gestão e Secretariado; v. 14 n. 7 (2023): Revista de Gestão e Secretariado v.14, n.7, 2023; 11294-113082178-9010reponame:GeSecinstname:Sindicato das Secretárias do Estado de São Paulo (SINSESP)instacron:SINSESPenghttps://ojs.revistagesec.org.br/secretariado/article/view/1670/1341Copyright (c) 2023 Emerson Martins, Napoleão Verardi Galegaleinfo:eu-repo/semantics/openAccessMartins, EmersonGalegale, Napoleão Verardi2023-07-20T11:03:31Zoai:ojs2.revistagesec.org.br:article/1670Revistahttps://www.revistagesec.org.br/ONGhttps://ojs.revistagesec.org.br/secretariado/oaieditor@revistagesec.org.br | gestoreditorial@revistagesec.org.br | rf.sabino@gmail.com2178-90102178-9010opendoar:2023-07-20T11:03:31GeSec - Sindicato das Secretárias do Estado de São Paulo (SINSESP)false |
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15,301629 |