M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda
Ano de defesa: | 2018 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Pontif?cia Universidade Cat?lica do Rio Grande do Sul
|
Programa de Pós-Graduação: |
Programa de P?s-Gradua??o em Ci?ncia da Computa??o
|
Departamento: |
Escola Polit?cnica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://tede2.pucrs.br/tede2/handle/tede/8171 |
Resumo: | Demand forecasting is one of the most essential components of supply chain management. Forecasts are used both for long-term and for short-term. Long-term forecasts are important because it is difficult in terms of production to face the demand deviation in a short time, so the anticipation of prediction helps to increase the responsiveness of the supply chain. Short term forecasts are important for the demand monitoring aiming to keep healthy inventory levels. In the fashion industry, the high change of products, the short life cycle and the lack of historical data makes difficult accurate predictions. To deal with this problem, the literature presents three approaches: statistical, artificial intelligence and hybrid that combines statistical and artificial intelligence. This research presents a two-phased method: (1) long-term prediction, identifies the different life cycles in the products, allowing the identification of sales prototypes for each cluster and (2) short-term prediction, classifies new products in the clusters labeled in the long-term phase and adjusts the sales curve considering optimistic and pessimist factors. As a differential, the method is based in dynamic time warping, distance measure for time series. The method is tested in a real dataset with real data from fashion retailers that demonstrates the quality of the contribution. |
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Ruiz, Duncan Dubugras Alcobahttp://lattes.cnpq.br/8250832800932125http://lattes.cnpq.br/0511455313062046Santos, Graziele Marques Mazuco dos2018-06-27T13:21:15Z2018-04-26http://tede2.pucrs.br/tede2/handle/tede/8171Demand forecasting is one of the most essential components of supply chain management. Forecasts are used both for long-term and for short-term. Long-term forecasts are important because it is difficult in terms of production to face the demand deviation in a short time, so the anticipation of prediction helps to increase the responsiveness of the supply chain. Short term forecasts are important for the demand monitoring aiming to keep healthy inventory levels. In the fashion industry, the high change of products, the short life cycle and the lack of historical data makes difficult accurate predictions. To deal with this problem, the literature presents three approaches: statistical, artificial intelligence and hybrid that combines statistical and artificial intelligence. This research presents a two-phased method: (1) long-term prediction, identifies the different life cycles in the products, allowing the identification of sales prototypes for each cluster and (2) short-term prediction, classifies new products in the clusters labeled in the long-term phase and adjusts the sales curve considering optimistic and pessimist factors. As a differential, the method is based in dynamic time warping, distance measure for time series. The method is tested in a real dataset with real data from fashion retailers that demonstrates the quality of the contribution.A previs?o de vendas no varejo da moda ? um problema complexo e um dos componentes essenciais da cadeia de suprimento, sendo utilizada tanto para previs?o de longo prazo quanto para a previs?o de curto prazo. A previs?o de longo prazo ? importante pois ? dif?cil, em termos de produ??o, enfrentar o desvio da demanda em um curto espa?o de tempo, ent?o a previs?o antecipada permite aumentar a capacidade de resposta da cadeia de suprimento. A previs?o de curto prazo ? importante para o acompanhamento da demanda, visando a adequa??o do n?vel de estoque. No varejo da moda a alta rotatividade, o curto ciclo de vida dos produtos e a consequente aus?ncia de dados hist?ricos dificulta a gera??o de previs?es precisas. Para lidar com esse problema, h? na literatura tr?s principais abordagens: estat?stica, baseada em intelig?ncia artificial e h?brida, que combina estat?stica e intelig?ncia artificial. Esta pesquisa prop?e um m?todo de previs?o de vendas em duas etapas: (1) previs?o de longo prazo, que pretende detectar diferentes grupos de produtos com ciclos de vida semelhantes, permitindo assim a identifica??o do comportamento m?dio de cada um dos grupos e (2) previs?o de curto prazo que busca associar os produtos novos nos grupos identificados na etapa de longo prazo e ajustar a curva de vendas levando em considera??o fatores conservadores, otimistas ou pessimistas. Al?m disso, nesta etapa ? poss?vel realizar a previs?o de reposi??o de itens. Como diferencial, o m?todo proposto utiliza a medida de dist?ncia Dynamic Time Warping, identificada na literatura como adequada para lidar com s?ries temporais. O m?todo ? testado utilizando dois conjuntos de dados reais de varejistas da moda, foram realizados dois experimentos, que demonstram a qualidade da contribui??o.Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-06-19T12:25:43Z No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5)Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-27T13:05:50Z (GMT) No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5)Made available in DSpace on 2018-06-27T13:21:15Z (GMT). No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5) Previous issue date: 2018-04-26application/pdfhttp://tede2.pucrs.br:80/tede2/retrieve/172646/GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.jpgporPontif?cia Universidade Cat?lica do Rio Grande do SulPrograma de P?s-Gradua??o em Ci?ncia da Computa??oPUCRSBrasilEscola Polit?cnicaMinera??o de DadosS?ries TemporaisPrevis?o de VendasInd?stria da ModaDynamic Time WarpingData MiningTime SeriesSales ForecastFashion IndustryCIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAOM?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da modainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisTrabalho n?o apresenta restri??o para publica??o1974996533081274470500500-862078257083325301info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da PUC_RSinstname:Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)instacron:PUC_RSTHUMBNAILGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.jpgGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.jpgimage/jpeg4990http://tede2.pucrs.br/tede2/bitstream/tede/8171/4/GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.jpge7dd3c1a7bf792ea729040d735b65bdaMD54TEXTGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.txtGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.txttext/plain303743http://tede2.pucrs.br/tede2/bitstream/tede/8171/3/GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf.txtb1dd5b05cc2c4d0fefc4584d27137de3MD53ORIGINALGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdfGRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdfapplication/pdf3857481http://tede2.pucrs.br/tede2/bitstream/tede/8171/2/GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf9c3c88f01e8e5d920ba3bc8989d2cfbfMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8610http://tede2.pucrs.br/tede2/bitstream/tede/8171/1/license.txt5a9d6006225b368ef605ba16b4f6d1beMD51tede/81712018-06-27 12:01:06.613oai:tede2.pucrs.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede2.pucrs.br/tede2/PRIhttps://tede2.pucrs.br/oai/requestbiblioteca.central@pucrs.br||opendoar:2018-06-27T15:01:06Biblioteca Digital de Teses e Dissertações da PUC_RS - Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)false |
dc.title.por.fl_str_mv |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
title |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
spellingShingle |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda Santos, Graziele Marques Mazuco dos Minera??o de Dados S?ries Temporais Previs?o de Vendas Ind?stria da Moda Dynamic Time Warping Data Mining Time Series Sales Forecast Fashion Industry CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
title_short |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
title_full |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
title_fullStr |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
title_full_unstemmed |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
title_sort |
M?todo de previs?o de vendas e estimativa de reposi??o de itens no varejo da moda |
author |
Santos, Graziele Marques Mazuco dos |
author_facet |
Santos, Graziele Marques Mazuco dos |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ruiz, Duncan Dubugras Alcoba |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8250832800932125 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0511455313062046 |
dc.contributor.author.fl_str_mv |
Santos, Graziele Marques Mazuco dos |
contributor_str_mv |
Ruiz, Duncan Dubugras Alcoba |
dc.subject.por.fl_str_mv |
Minera??o de Dados S?ries Temporais Previs?o de Vendas Ind?stria da Moda |
topic |
Minera??o de Dados S?ries Temporais Previs?o de Vendas Ind?stria da Moda Dynamic Time Warping Data Mining Time Series Sales Forecast Fashion Industry CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Dynamic Time Warping Data Mining Time Series Sales Forecast Fashion Industry |
dc.subject.cnpq.fl_str_mv |
CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
description |
Demand forecasting is one of the most essential components of supply chain management. Forecasts are used both for long-term and for short-term. Long-term forecasts are important because it is difficult in terms of production to face the demand deviation in a short time, so the anticipation of prediction helps to increase the responsiveness of the supply chain. Short term forecasts are important for the demand monitoring aiming to keep healthy inventory levels. In the fashion industry, the high change of products, the short life cycle and the lack of historical data makes difficult accurate predictions. To deal with this problem, the literature presents three approaches: statistical, artificial intelligence and hybrid that combines statistical and artificial intelligence. This research presents a two-phased method: (1) long-term prediction, identifies the different life cycles in the products, allowing the identification of sales prototypes for each cluster and (2) short-term prediction, classifies new products in the clusters labeled in the long-term phase and adjusts the sales curve considering optimistic and pessimist factors. As a differential, the method is based in dynamic time warping, distance measure for time series. The method is tested in a real dataset with real data from fashion retailers that demonstrates the quality of the contribution. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-06-27T13:21:15Z |
dc.date.issued.fl_str_mv |
2018-04-26 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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masterThesis |
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publishedVersion |
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http://tede2.pucrs.br/tede2/handle/tede/8171 |
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http://tede2.pucrs.br/tede2/handle/tede/8171 |
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Pontif?cia Universidade Cat?lica do Rio Grande do Sul |
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Pontif?cia Universidade Cat?lica do Rio Grande do Sul |
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